Nonlinear control systems play a crucial role in managing exoskeleton robots, especially given the complex, unpredictable nature of human movement. Unlike linear systems, which assume proportional responses, nonlinear control systems can handle the dynamic and variable forces involved in human–robot interaction, allowing the exoskeleton to respond more naturally and effectively to the user’s actions. This capability allows exoskeletons to adapt in real time to changes in movement and force. By accounting for these complexities, nonlinear control systems enable a smoother, more intuitive interaction between the exoskeleton and the user.
Nonlinear control techniques, such as sliding mode control and adaptive control, allow the exoskeleton to adjust its behavior based on real-time feedback and external disturbances, such as a sudden change in the user’s movement or strength. Sliding mode control, for example, is effective for maintaining stability despite external disruptions, helping the exoskeleton maintain precise support. Adaptive nonlinear control systems are also valuable, as they adjust to each user’s unique biomechanics and gradually refine assistance based on ongoing interactions. This adaptability enhances comfort, safety, and efficiency in rehabilitation exercises. By using nonlinear control, exoskeleton robots can provide a more intuitive and supportive experience, shaping assistance to each user’s needs and fostering better recovery outcomes. The following section will discuss the various types of nonlinear control schemes commonly used in exoskeleton robot control.
5.1.1. Computed Torque Control
Computed torque control (CTC) is a widely used model-based control method in exoskeleton robots. It is designed to achieve precise trajectory tracking and smooth, responsive movement. CTC uses the robot’s dynamic model to compute the exact torques required at each joint, enabling the robot to reach the desired positions and orientations. By effectively linearizing the nonlinear dynamics of the robot, CTC simplifies complex control challenges, transforming them into manageable linear problems. This approach is especially important in rehabilitation applications, where accurate and stable movements are essential to support users safely during exercises or mobility tasks.
The architecture of a CTC system is shown in
Figure 3. CTC consists of two loops. The linearization loop removes nonlinear effects caused by gravity, Coriolis, and centrifugal forces. The control loop provides the input needed to achieve the desired performance while maintaining system stability.
CTC heavily depends on the accuracy of the dynamic model. It calculates torques based on factors such as inertia, friction, and external forces acting on the exoskeleton. When the model closely matches real-world dynamics, CTC ensures smooth movement and consistent support. However, discrepancies between the model and actual conditions can degrade control performance. To mitigate this, CTC is often combined with adaptive or robust control techniques. These enhancements improve resilience to modeling inaccuracies, ensuring reliable operation in diverse rehabilitation scenarios [
13].
The following referenced studies explore advancements in computed torque control (CTC) systems for rehabilitation exoskeletons. Each paper highlights unique control strategies tailored to specific needs, such as handling uncertainties, improving trajectory tracking, and enhancing user comfort. This section compares these studies based on control strategy, dynamic modeling, handling of uncertainties, experimental validation, and focus on user-specific requirements.
Control Strategies
The papers utilize various control approaches to enhance performance and robustness in rehabilitation robotics. The paper “
Time-delay Estimation Based Computed Torque Control with Robust Adaptive RBF Neural Network Compensator” [
13] integrates time-delay estimation (TDE) with CTC and radial basis function neural networks (RBFNN). This combination addresses time delays and compensates for unknown dynamics, resulting in improved trajectory tracking and stability. Similarly, the paper “
Adaptive RBF Neural Network-Computed Torque Control for a Pediatric Gait Exoskeleton System” [
14] employs an adaptive RBFNN-based CTC, which adjusts to dynamic uncertainties and enhances tracking accuracy for pediatric gait rehabilitation. Both approaches highlight the benefits of neural networks in approximating unknown dynamics and improving performance under varying conditions.
In contrast, the study “
Modified Computed Torque Control of a Robotic Orthosis for Gait Rehabilitation” [
15] incorporates fractional-order derivatives into the CTC framework to enhance performance during transient and steady-state conditions. This modification addresses the limitations of traditional proportional-derivative (PD) controllers, achieving faster stabilization and reduced tracking errors. The paper “
Feedback Control Design for Robust Comfortable Sit-to-Stand Motions of 3D Lower-Limb Exoskeletons” [
16] uses a quadratic programming (QP)-based CTC approach, emphasizing constrained optimization to ensure user comfort during sit-to-stand transitions. Meanwhile, “
A Realistic Model Reference Computed Torque Control Strategy for Human Lower Limb Exoskeletons” [
17] introduces a dual-loop control system to improve computational efficiency and precision, demonstrating an alternative approach to traditional CTC frameworks.
Dynamic Modeling
Dynamic modeling plays a central role in most studies. The paper “
Time-delay Estimation Based Computed Torque Control with Robust Adaptive RBF Neural Network Compensator” [
13] focuses on estimating time delays and unmodeled dynamics, allowing for compensation through TDE and neural networks. Similarly, “
Adaptive Computed Torque Control Based on RBF Network for a Lower Limb Exoskeleton” [
18] employs RBF networks to estimate and compensate for unmodeled dynamics, ensuring precise motion control.
The study “
Computed Torque Control of the Stewart Platform with Uncertainty for Lower Extremity Robotic Rehabilitation” [
19] incorporates Polynomial Chaos Expansion (PCE) into the CTC framework to handle parameter uncertainties. This method allows systematic evaluation of dynamic responses under stochastic conditions. In comparison, the paper “
Modified Computed Torque Control of a Robotic Orthosis for Gait Rehabilitation” [
15] develops a mathematical model that incorporates pneumatic artificial muscles (PAMs) to achieve smooth and responsive assistance. The paper “
A Realistic Model Reference Computed Torque Control Strategy for Human Lower Limb Exoskeletons” [
17] uses a 7-degree-of-freedom (DOF) model incorporating human biomechanics and friction effects, providing a realistic representation for more accurate simulations.
Handling of Uncertainties
Uncertainty management is a critical aspect of these studies. Papers such as [
13,
14] address uncertainties using RBF neural networks, which estimate and compensate for unknown dynamics in real time. This approach ensures robust performance even when the system encounters unmodeled disturbances or user-specific variations. Similarly, the study “
Adaptive Computed Torque Control Based on RBF Network for a Lower Limb Exoskeleton” [
18] extends this concept by integrating adaptive features into the neural network, further enhancing the system’s ability to handle dynamic uncertainties.
The paper “
Computed Torque Control of the Stewart Platform with Uncertainty for Lower Extremity Robotic Rehabilitation” [
19] uses PCE to model uncertainties, providing an efficient alternative to Monte Carlo simulations. This method improves computational efficiency while accurately accounting for stochastic variations. On the other hand, “
A Realistic Model Reference Computed Torque Control Strategy for Human Lower Limb Exoskeletons” [
17] excludes Coriolis and centrifugal forces from the model, treating them as disturbances. This simplification reduces computational load while maintaining control robustness.
Experimental Validation
Experimental validation varies across the studies. Papers like “
Time-delay Estimation Based Computed Torque Control with Robust Adaptive RBF Neural Network Compensator” [
13] and “
Adaptive RBF Neural Network-Computed Torque Control for a Pediatric Gait Exoskeleton System” [
14] validate their approaches through co-simulations and experiments, demonstrating improved tracking accuracy and robustness compared to traditional CTC methods. For example, ref. [
14] reports a 37.5% to 40.98% improvement in tracking accuracy across pediatric exoskeleton joints.
The study “
Modified Computed Torque Control of a Robotic Orthosis for Gait Rehabilitation” [
15] uses experimental tests on multiple subjects to validate its fractional-order derivative approach, achieving reduced tracking errors and faster stabilization. “
Feedback Control Design for Robust Comfortable Sit-to-Stand Motions of 3D Lower-Limb Exoskeletons” [
16] focuses on simulations to test robustness against perturbations, including user-specific variations like spasticity. Similarly, “
A Realistic Model Reference Computed Torque Control Strategy for Human Lower Limb Exoskeletons” [
17] relies on simulations to demonstrate the effectiveness of its dual-loop design in achieving accurate trajectory tracking and robust performance.
User-Specific Requirements
User-specific needs are central to many studies. The paper “
Adaptive RBF Neural Network-Computed Torque Control for a Pediatric Gait Exoskeleton System” [
14] targets pediatric rehabilitation, focusing on personalized gait training for children aged 8–12. This study emphasizes the importance of safety and adaptability in exoskeleton design for young users. Similarly, “
Feedback Control Design for Robust Comfortable Sit-to-Stand Motions of 3D Lower-Limb Exoskeletons” [
16] addresses sit-to-stand transitions, optimizing control for user comfort through constraints on joint angles, motor torques, and contact forces.
The paper “
A Realistic Model Reference Computed Torque Control Strategy for Human Lower Limb Exoskeletons” [
17] incorporates user-specific parameters, such as weight and height, to ensure consistent performance across diverse rehabilitation scenarios. In contrast, the studies [
13,
18] focus more on robust control under general dynamic uncertainties, without explicitly tailoring systems to specific user populations.
Key Similarities and Differences
The studies share a common goal of improving the robustness, accuracy, and adaptability of CTC systems for rehabilitation robotics. Neural networks and adaptive strategies are recurring themes, particularly in [
13,
14,
18], which leverage RBF networks for real-time compensation of unknown dynamics. Similarly, all studies emphasize stability, often validated through Lyapunov theory or experimental testing.
However, differences emerge in focus areas. Papers like [
14,
16] prioritize user-specific needs, targeting pediatric users and sit-to-stand transitions, respectively. In contrast, the authors of [
13,
18,
19] emphasize robust handling of uncertainties and unmodeled dynamics, often through advanced estimation and compensation methods. Computational efficiency is another area of divergence, with [
17,
19] optimizing performance through PCE and simplified dynamic models.
The referenced studies present diverse approaches to computed torque control, addressing challenges such as nonlinear dynamics, uncertainties, and user-specific requirements. Neural network integration, adaptive features, and robust modeling techniques enhance system performance across different rehabilitation scenarios. While all studies contribute to advancing exoskeleton control, future research should aim to combine these approaches, integrating user-specific customization with robust uncertainty management to create versatile and efficient systems.
Table 1 summarizes the articles discussed in this section:
The next section will discuss the recent advancements in adaptive control systems for rehabilitation exoskeleton applications.
5.1.2. Adaptive Control
Adaptive control in exoskeleton robots is essential for providing personalized and responsive assistance to users. The adaptive control scheme adjusts its parameters in real time, accommodating changes in the user’s movements, strength, and interaction dynamics. This capability is critical in exoskeletons used for rehabilitation, where patients may exhibit varying levels of mobility, muscle tone, or fatigue during each session. Adaptive control enables the exoskeleton to customize its support, offering a seamless experience that aligns with the user’s current capabilities and needs.
The adaptive control system constantly monitors the user’s actions through sensors and modifies its response to match these inputs accurately. For instance, if a user unexpectedly changes speed or force, the adaptive control system can adjust the exoskeleton’s output to maintain stability and comfort. Additionally, adaptive control enhances safety by quickly responding to irregularities, reducing the risk of injury. Often, adaptive control is paired with machine learning techniques, allowing the exoskeleton to refine its responses based on accumulated data over multiple sessions. This adaptability not only improves the effectiveness of rehabilitation but also supports a more natural, intuitive interaction between the user and the exoskeleton, ultimately fostering more consistent and meaningful recovery progress.
The following referenced studies highlight diverse advancements in control strategies for rehabilitation robots, emphasizing solutions tailored to various challenges in human–robot interaction, dynamic uncertainties, and personalized therapy. This comparative analysis explores the contributions, methodologies, and limitations of these studies based on key criteria.
Control Approaches and Adaptability
The control strategies differ significantly in their methodologies for managing dynamic uncertainties and human interaction. The paper “
Impedance Learning-Based Hybrid Adaptive Control of Upper Limb Rehabilitation Robots” [
20] introduces a hybrid adaptive control (HAC) system that integrates impedance learning with adaptive control. This approach estimates parametric uncertainties and time-varying human impedance using differential and periodic adaptation mechanisms, achieving asymptotic stability and precise tracking. The HAC system stands out for not requiring force measurements and its ability to adapt to subtle changes in human impedance.
Similarly, Cai et al. [
21] propose a compensation-corrective adaptive control (CCAC) system for upper-limb rehabilitation. Unlike HAC, which focuses on impedance learning, CCAC dynamically adjusts robotic assistance to minimize trunk compensation during rehabilitation tasks. By integrating an admittance model, human-intention estimators, and dynamic assistance adjustments, CCAC personalizes support to enhance motor performance. This system reduces trunk compensation by over 60% in various tasks, highlighting its adaptability to user-specific conditions.
For lower-limb rehabilitation, Han et al. [
22] employ an adaptive control system based on interaction torque, inertia compensation, and an Adaptive Frequency Oscillator (AFO). This system synchronizes robotic assistance with the user’s natural gait, enabling dynamic frequency adjustments for tailored support. The lightweight design of the exoskeleton further enhances its usability, demonstrating adaptability in gait training.
Human–Robot Interaction (HRI)
Enhancing HRI is central to the effectiveness of rehabilitation robots. The study “
A Muscle Synergy-Inspired Control Design to Coordinate Functional Electrical Stimulation and a Powered Exoskeleton” [
23] addresses HRI challenges by combining functional electrical stimulation (FES) with powered exoskeletons. Its adaptive synergy-based controller simplifies complex movements by leveraging muscle synergies. This approach coordinates joint trajectories and muscle activation, optimizing locomotion for individuals with spinal cord injuries (SCIs). The integration of subject-specific gait trajectories further personalizes therapy, making it highly relevant for SCI rehabilitation.
Similarly, the study by Wang et al. [
24] introduces the adaptive interaction torque-based assist-as-needed (AITAAN) control strategy. This system uses a Nonlinear Disturbance Observer (NDO) to estimate muscle torque, allowing the exoskeleton to provide assistance dynamically tailored to the user’s strength. By prioritizing precise trajectory tracking and interaction torque adjustments, AITAAN enhances HRI, improving both rehabilitation outcomes and user comfort.
In contrast, Pan et al. [
25] emphasize multi-axis self-tuning control to improve gait patterns. The system dynamically adjusts controller gains based on motor current signals, optimizing performance during various walking conditions. While less focused on direct HRI, this approach improves gait stability and reduces errors, indirectly enhancing user experience.
Handling Dynamic Uncertainties
Dynamic uncertainties, including variations in human limb properties and external disturbances, are common challenges addressed in several studies. “
An Adaptive Controller for Human Lower Extremity Exoskeleton Robot” [
26] presents a direct adaptive controller for a 7-DOF exoskeleton, accounting for nonlinear dynamics and human variability. By leveraging a regressor matrix and real-time feedback, the controller adapts to changing parameters, ensuring precise trajectory tracking and stability even under disturbances.
The study “
Switched Concurrent Learning Adaptive Control for Treadmill Walking Using a Lower Limb Hybrid Exoskeleton” [
27] introduces a switched adaptive controller designed to manage nonlinear dynamics during treadmill walking. This controller combines cable-driven motors with FES to activate muscles, facilitating smooth phase transitions in gait. Its concurrent learning algorithm accelerates parameter convergence and reduces kinematic tracking errors by 22.6%, demonstrating robust performance in handling uncertainties.
The “
Realistic Model Reference Computed Torque Control Strategy for Human Lower Limb Exoskeletons” [
17] takes a computational approach to address uncertainties. By excluding Coriolis and centrifugal forces from the dynamic model, the controller reduces computational demands while maintaining trajectory accuracy. This trade-off between model complexity and efficiency ensures robust control despite parameter variations.
Control System Performance and Stability
The stability and performance of control systems are critical in rehabilitation scenarios. The HAC system in [
20] ensures stability through a non-negative function analysis, keeping estimation and tracking errors bounded over time. Similarly, CCAC [
21] uses statistical analyses to validate its effectiveness, achieving reduced trunk compensation and improved movement smoothness.
The direct adaptive controller in [
26] employs Lyapunov stability theory, guaranteeing stable trajectory tracking under high-friction conditions. Its adaptive gains dynamically converge, maintaining performance across diverse scenarios. Likewise, the switched adaptive controller in [
27] ensures stability during gait transitions using multiple Lyapunov functions, highlighting its reliability during complex movements.
Papers like [
17,
25] also emphasize performance optimization. The realistic model reference controller in [
17] achieves excellent trajectory tracking by balancing model accuracy and computational efficiency. The self-tuning control system in [
25] reduces hip and knee tracking errors through dynamic gain adjustments, ensuring consistent performance under varying conditions.
Personalization and User-Specific Design
Personalization is a recurring theme across studies. The CCAC system in [
21] tailors assistance by interpreting user intentions and adjusting robotic support to reduce compensatory movements. Similarly, the synergy-inspired controller in [
23] uses subject-specific gait trajectories, offering lightweight and personalized rehabilitation solutions for SCI patients.
The AITAAN strategy in [
24] dynamically adapts assistance based on muscle torque estimations, ensuring user-specific support during rehabilitation. The multi-axis self-tuning controller in [
25] accommodates users of different heights through stepless adjustments, enhancing comfort and usability.
Pan et al. [
25] and Han et al. [
22] also prioritize user-specific design. Han’s exoskeleton uses a cable-driven mechanism and lightweight materials to minimize physical strain, while Pan’s system optimizes motor operations for individual walking gaits.
Comparison of Experimental Validation
Experimental validation varies across studies, with some focusing on simulations and others incorporating human trials. The HAC system in [
20] is validated through simulations on a five-bar planar mechanism, demonstrating improved tracking accuracy compared to traditional adaptive controls. Similarly, the direct adaptive controller in [
26] relies on simulation results to validate trajectory tracking and stability.
In contrast, studies like CCAC [
21] and AITAAN [
24] include experiments with human participants. CCAC uses healthy subjects performing reaching tasks to measure improvements in trunk compensation, while AITAAN validates its control strategy through co-simulation experiments focused on interaction torque.
The synergy-inspired controller in [
23] and the switched adaptive controller in [
27] also incorporate human trials. These studies test their systems on SCI patients and treadmill walking scenarios, respectively, providing valuable insights into real-world applicability.
Despite their contributions, the studies have limitations. The HAC system in [
20] does not involve human trials, limiting its validation to simulations. Similarly, Pan’s self-tuning controller [
25] focuses primarily on mechanical aspects, with limited emphasis on HRI.
CCAC [
21] and AITAAN [
24], while effective in reducing compensation and improving torque assistance, have yet to be tested on clinical populations such as stroke or SCI patients. The synergy-inspired controller in [
23] and the switched adaptive controller in [
27] focus heavily on gait rehabilitation, leaving other rehabilitation tasks unexplored.
Future research across these studies emphasizes expanding experimental validation and refining user-centric designs. For example, ref. [
21] calls for applications in stroke patients, while ref. [
27] highlights the need to address muscle fatigue and external disturbances.
The referenced studies collectively advance the state of rehabilitation robotics by introducing diverse control strategies tailored to dynamic uncertainties, HRI, and user-specific needs. Methods like HAC [
20], CCAC [
21], and AITAAN [
24] prioritize adaptability and personalized support, while synergy-inspired [
23] and switched adaptive controllers [
27] focus on efficient gait rehabilitation. However, many systems still require broader experimental validation, particularly on clinical populations, to enhance their real-world applicability. By addressing these gaps, future research can further refine control strategies, ensuring precise, adaptable, and user-centered rehabilitation technologies.
Table 2 summarizes the articles discussed in this section:
The next section will discuss the application of robust control in rehabilitation exoskeleton robots.
5.1.3. Robust Control
Robust control in exoskeleton robots is a powerful technique designed to maintain stability and performance despite uncertainties, disturbances, or changes in the interaction dynamics. This approach is crucial in rehabilitation, where the robot must adapt to diverse users and unpredictable forces, such as sudden shifts in the user’s movement or unexpected external loads. Unlike adaptive control, which adjusts parameters in real time, robust control is configured to handle a predefined range of uncertainties, ensuring that the exoskeleton can perform reliably even when faced with modeling inaccuracies or external disturbances.
The primary goal of robust control is to make the exoskeleton resilient to variations that could disrupt smooth operation or lead to safety concerns. For example, if a user with limited muscle strength suddenly leans into the exoskeleton, robust control can manage this extra force, maintaining balance and providing appropriate support without sudden jerks or movements. Common robust control methods, such as H-infinity control and sliding mode control, are particularly effective for ensuring stable performance across a range of challenging scenarios. By prioritizing stability and consistency, robust control enhances user safety and provides a reliable experience, making it an ideal choice for rehabilitation tasks where user movements are often variable and unpredictable.
The following referenced studies present a range of innovative control strategies for rehabilitation robotics, each addressing unique challenges such as nonlinear dynamics, uncertainties, human–robot interaction, and user-specific requirements. Below is a detailed comparison and contrast of these approaches based on various criteria, including control methodologies, system adaptability, experimental validation, and application scope.
Model-Based Control and Differential Flatness
Brahmi et al. [
33] proposed a flatness-based control strategy for an upper-limb rehabilitation robot, leveraging the differential flatness property to simplify nonlinear system control. The transformation into a triangular flat canonical form allowed for efficient trajectory tracking with reduced control inputs compared to traditional computed torque control (CTC). Lyapunov stability ensured asymptotic stability, making it a reliable approach for precise rehabilitation tasks.
Similarly, Jiang et al. [
34] employed an adaptive robust control strategy for a 3DOF lower-limb rehabilitation robot. By integrating kinematic, friction, and motor models, the system managed uncertainties and disturbances effectively. Unlike Brahmi et al. [
33], this study included a proportional-integral (PI) sub-controller to enhance trajectory tracking accuracy, particularly for repetitive rehabilitation tasks.
Cable-Driven Systems
The cable-driven knee exoskeleton proposed by the authors in [
35] introduced a switched systems approach for controlling cable tension and knee tracking. This two-layer control system included a high-level controller for periodic trajectory tracking and a low-level controller for maintaining cable tension. The incorporation of real-time tension feedback ensured precise control while minimizing slackness, setting it apart from rigid-link control methods like those in [
33,
34].
Nonlinear and Robust Control
Several papers emphasized nonlinear and robust control strategies. The work in [
36] used adaptive central pattern generators (ACPGs) and nonlinear disturbance observers to adjust gait frequency and amplitude dynamically. This real-time adaptability addressed human–robot interaction challenges, ensuring smooth locomotion transitions. Similarly, ref. [
37] applied Generalized proportional integral (GPI) controllers for hip-joint rehabilitation, focusing on robust trajectory tracking under parametric uncertainties. Both studies validated stability using Lyapunov theory, though [
36] concentrated on gait adaptability while [
37] prioritized trajectory precision.
Fuzzy Logic and Fractional Control
In [
38], a deterministic adaptive robust control strategy for a 2DOF lower-limb exoskeleton employed fuzzy set theory to address system uncertainties. Cooperative game theory optimized control gains, improving tracking and reducing input fluctuations. This approach contrasts with the fractional multi-loop active disturbance rejection control (FADRC) used in [
39], which integrated fractional calculus into feedback loops for smoother control and better disturbance rejection. Both methods achieved robust performance but differed in their mathematical frameworks.
User-Specific Adaptations
Several studies prioritized adaptability for diverse users. The control framework in [
35] dynamically adjusted cable tension, improving comfort and safety during knee rehabilitation. Similarly, the SEAC system in [
40] used a clutch mechanism to manage torque delivery precisely, ensuring mechanical safety during walking phases. Adaptive control methods in [
34,
41] tailored rehabilitation exercises to user-specific parameters such as weight, height, and muscle strength, promoting personalized therapy.
Handling Uncertainties
The studies in [
18,
19,
42] focused on managing uncertainties in robotic systems. Polynomial Chaos Expansion (PCE) in [
19] modeled stochastic responses to handle patient-specific parameter variations, while [
18] used RBF neural networks to compensate for unmodeled dynamics in a 12DOF exoskeleton. The leakage-type adaptive mechanism in [
42] estimated uncertainties without requiring prior bounds, ensuring robust control even under varying conditions. These methods enhanced system resilience but differed in their computational complexity and application focus.
Human Subject Testing
Many studies validated their approaches using human subjects. Brahmi et al. [
33] demonstrated improved trajectory tracking in flatness-based control compared to CTC, while [
35] tested the cable-driven exoskeleton with six able-bodied participants, showing significant reductions in slackness and control variability. In contrast, refs. [
34,
40] included preliminary trials with healthy subjects to validate torque tracking and gait training effectiveness, emphasizing system safety and user comfort.
Simulation-Based Validation
Some studies relied primarily on simulations for validation. For example, the robust control strategy in [
37] was tested using numerical simulations and Bezier polynomial trajectories. Similarly, refs. [
39,
42] evaluated fractional and leakage-type adaptive controls through MATLAB /Simulink, showcasing their effectiveness in rejecting disturbances and maintaining stability. While simulations provided valuable insights, real-world testing would strengthen the reliability of these findings.
Upper-Limb vs. Lower-Limb Rehabilitation
Upper-limb rehabilitation was the focus of Brahmi et al. [
33], which targeted stroke recovery with a smart robotic exoskeleton. This contrasts with the lower-limb systems in [
34,
35,
36], which emphasized gait training and mobility improvements. Each approach tailored control strategies to specific joint dynamics, whether for the knee, hip, or ankle.
Rehabilitation Context
The SEAC system in [
40] and the multi-level control strategy in [
41] targeted walking assistance, emphasizing locomotion phase transitions. In contrast, refs. [
37,
39] focused on repetitive task-specific exercises for stroke recovery. These differences highlight the diverse rehabilitation goals addressed by robotic systems, from mobility enhancement to passive movement training.
Safety and Comfort
Safety and comfort were recurring themes across studies. For example, refs. [
35,
40] incorporated real-time feedback mechanisms to minimize tension and torque fluctuations, reducing strain on users. The leakage-type adaptive mechanism in [
42] ensured bounded gait trajectories, enhancing physiological safety during rehabilitation. These approaches underscore the importance of user-centric design in robotic rehabilitation.
Tracking Accuracy
All studies demonstrated significant improvements in trajectory tracking, with specific methods excelling under certain conditions. For example, refs. [
33,
42] achieved precise control through differential flatness and fuzzy logic, respectively. Meanwhile, refs. [
42,
43] showcased the advantages of fractional and leakage-type adaptive control in reducing tracking errors.
Robustness
Robustness against disturbances was a critical feature in [
36,
37,
42]. These studies used adaptive components and advanced estimation techniques to maintain stability and performance under varying conditions. The robustness of these methods makes them particularly suitable for unpredictable human–robot interactions.
Computational Efficiency
Efficiency varied across approaches. PCE in [
19] and dual-loop control in [
17] reduced computational demands, enabling real-time performance. In contrast, adaptive robust controls in [
34,
42] required more computational resources due to their complexity but delivered enhanced precision.
The referenced studies collectively advance control strategies for rehabilitation robotics, addressing key challenges like nonlinear dynamics, system uncertainties, and user-specific adaptability. While flatness-based control in [
33] simplifies upper-limb rehabilitation, cable-driven and multi-level strategies in [
35,
41] enhance lower-limb mobility. Robust methods like those in [
36,
37,
42] ensure stability under diverse conditions, while safety-focused designs in [
34,
40] prioritize user comfort. Although all methods contribute significantly, further experimental validation and comparative analysis in real-world settings remain essential to optimize rehabilitation technologies.
Table 3 summarizes the articles discussed in this section:
The next section will discuss the recent advancements of sliding mode control systems for rehabilitation exoskeleton applications.
5.1.4. Sliding Mode Control
Sliding mode control (SMC) is a robust and nonlinear control strategy widely used in engineering and scientific applications due to its effectiveness in handling system uncertainties and external disturbances. This method is based on driving the system state trajectories onto a predefined sliding surface and maintaining them there for the remainder of the control process. The sliding surface is designed to achieve the desired system dynamics, ensuring stability and performance. SMC is characterized by its ability to switch control actions at high frequency, creating a discontinuous control signal that enforces the sliding motion. This switching mechanism makes SMC inherently robust to modeling inaccuracies and external perturbations, as it forces the system to “slide” along the surface where these effects are minimized. One of the key challenges associated with SMC is the chattering phenomenon, a high-frequency oscillation caused by the finite bandwidth of actuators and measurement noise. To mitigate chattering, various techniques have been developed, including boundary layer approaches, adaptive sliding surfaces, and higher-order sliding mode controllers. These advancements extend the applicability of SMC to systems with practical constraints. Due to its simplicity in design and strong robustness properties, SMC continues to be a focal area in control research, particularly in robotics, automotive systems, and power electronics.
Figure 4 shows the architecture of the sliding mode controller for robotics applications. The major limitation of the sliding mode control is the issue of chattering. An additional chattering suppressor is required to remove chattering effects.
Figure 5 shows the sliding mode controller with a chattering suppressor. The following section will cover the recent advancement of the sliding mode controller for robotics applications.
The referenced studies below present diverse advancements in sliding mode control strategies for rehabilitation exoskeletons, focusing on aspects such as trajectory tracking, robustness to uncertainties, user-specific adaptability, and the reduction in chattering. Each paper offers a unique approach to addressing the challenges of human–robot interaction in rehabilitation scenarios, with distinct strengths and limitations.
Handling Model Uncertainties and External Disturbances
The paper “
Adaptive Neural Network-Based Practical Predefined-Time Nonsingular Terminal Sliding Mode Control for Upper Limb Rehabilitation Robots” [
44] proposes a robust method to address uncertainties and disturbances in upper-limb rehabilitation robots. By integrating radial basis function neural networks (RBFNNs), the strategy compensates for unknown dynamics and minimizes chattering, enhancing trajectory tracking and responsiveness. Similarly, “
Extended State Observer-Based Nonlinear Terminal Sliding Mode Control with Feedforward Compensation for Lower Extremity Exoskeleton” [
45] introduces ESO-F-NTSMC, which leverages disturbance estimation capabilities of Extended State Observers (ESOs) and robust sliding mode control. Both strategies emphasize adaptability to dynamic uncertainties, with [
45] validated through experimental tests showing significant improvements in root mean square error (RMSE).
In contrast,
“Design and Implementation of a Robotic Hip Exoskeleton for Gait Rehabilitation” [
46] employs a Linear Extended State Observer (LESO) combined with sliding mode controllers. This method effectively manages dynamic uncertainties without additional torque sensors, demonstrating strong tracking performance during slow and moderate walking speeds. Unlike [
44,
45], which focus on algorithmic precision, this study prioritizes hardware simplicity by excluding torque sensors while maintaining robustness through advanced control.
Sliding Mode Control Enhancements
Several studies highlight the utility of sliding mode control (SMC) for its robustness to disturbances. The paper
“Study on the Control Algorithm for Lower Limb Exoskeleton Based on ADAMS/Simulink Co-Simulation” [
47] uses SMC for lower-limb gait rehabilitation. The approach improves trajectory tracking and system stability over traditional PID control, showing resilience to system nonlinearities. However, chattering remains a challenge, as it can cause mechanical wear and user discomfort. To address this,
“Biomechanical Design and Control of an Eight DOF Human Lower Extremity Rehabilitation Exoskeleton Robot” [
48] incorporates a super-twisting algorithm to suppress chattering. This enhancement ensures smooth trajectory tracking while maintaining SMC’s robustness. The integration of a detailed friction model in [
48] further improves control accuracy, highlighting its practical application in rehabilitation therapy.
Similarly,
“Development of a Sliding Mode Controller with Chattering Suppressor for Human Lower Extremity Exoskeleton Robot” [
49] focuses on minimizing chattering effects in SMC by introducing a continuous mode chattering suppressor. This addition enhances user comfort and reduces actuator wear, addressing the limitations of traditional SMC. Comparatively,
“Model-Free Finite-Time Robust Control Using Fractional-Order Ultra-Local Model and Prescribed Performance Sliding Surface for Upper-Limb Rehabilitation Exoskeleton” [
50] adopts a model-free approach to SMC. By using fractional-order dynamics and a prescribed performance sliding surface, this method ensures finite-time convergence and robustness without requiring an accurate dynamic model, a key advantage over model-dependent strategies in [
47,
49,
50].
Trajectory Tracking and User-Specific Adaptability
The referenced studies emphasize precise trajectory tracking as a critical aspect of rehabilitation robotics. For example,
“Human Gait Tracking for Rehabilitation Exoskeleton: Adaptive Fractional Order Sliding Mode Control Approach” [
51] introduces AFOFTSMC, which offers high precision and robustness in tracking hemiplegic patients’ gaits. This controller adjusts parameters adaptively to handle uncertainties, outperforming conventional sliding mode controllers in simulations.
“Design, Development and Control of a 2PRP-2PPR Planar Parallel Manipulator for Lower Limb Rehabilitation Therapies” [
52] focuses on improving user-specific adaptability through the non-singular fast terminal sliding mode control (NSTSMC). This strategy ensures finite-time convergence with reduced chattering, validated by successful tracking of clinical gait trajectories within ±1° error. By combining safety protocols and adjustable components, this system ensures user-specific adaptability while maintaining control precision.
Control Strategies for Upper and Lower-Limb Rehabilitation
The studies also differ in their focus on upper or lower-limb rehabilitation. For upper-limb applications, “
Adaptive Neural Network-Based Practical Predefined-Time Nonsingular Terminal Sliding Mode Control for Upper Limb Rehabilitation Robots” [
44] and “
Model-Free Finite-Time Robust Control Using Fractional-Order Ultra-Local Model and Prescribed Performance Sliding Surface for Upper-Limb Rehabilitation Exoskeleton” [
50] prioritize robust control under dynamic uncertainties. Both integrate advanced features like RBFNNs and fractional-order dynamics to achieve accurate and responsive upper-limb movements. However, ref. [
50] adopts a model-free approach, offering greater flexibility compared to the model-dependent strategy in ref. [
44].
Lower-limb rehabilitation is extensively explored in studies such as “
Extended State Observer-Based Nonlinear Terminal Sliding Mode Control with Feedforward Compensation for Lower Extremity Exoskeleton” [
45], “
Biomechanical Design and Control of an Eight DOF Human Lower Extremity Rehabilitation Exoskeleton Robot” [
48,
51], and “
Human Gait Tracking for Rehabilitation Exoskeleton: Adaptive Fractional Order Sliding Mode Control Approach” [
51]. These papers emphasize robustness and precision in gait tracking, with [
48] focusing on ergonomics and user comfort through a detailed friction model. The studies also highlight varying degrees of adaptability to user-specific conditions, such as hemiplegic gait compensation in ref. [
51].
Experimental Validation and Simulation Approaches
The methods for validating control strategies differ across studies. Papers like “
Study on the Control Algorithm for Lower Limb Exoskeleton Based on ADAMS/Simulink Co-Simulation” [
47] and “
Design, Development and Control of a 2PRP-2PPR Planar Parallel Manipulator for Lower Limb Rehabilitation Therapies” [
52] emphasize co-simulation environments. By integrating ADAMS and Simulink, these studies optimize the design and testing process. In contrast, “
Biomechanical Design and Control of an Eight DOF Human Lower Extremity Rehabilitation Exoskeleton Robot” [
48] and “
Development of a Sliding Mode Controller with Chattering Suppressor for Human Lower Extremity Exoskeleton Robot” [
49] rely on MATLAB simulations to evaluate performance under dynamic conditions.
Some studies combine simulation with experimental validation. For example, “
Extended State Observer-Based Nonlinear Terminal Sliding Mode Control with Feedforward Compensation for Lower Extremity Exoskeleton” [
45] validates its approach with human volunteers, demonstrating improvements in RMSE and gait tracking. Similarly, “
Design and Implementation of a Robotic Hip Exoskeleton for Gait Rehabilitation” [
46] conducts experiments at different walking speeds and during stair ascension, confirming the robustness of its LESO-based control strategies.
The integration of advanced neural networks in [
44,
50] enhances system adaptability and robustness, but these methods rely on extensive parameter tuning, which may limit their practical application. Studies such as [
47,
49] demonstrate the effectiveness of sliding mode control but must address chattering to improve comfort and efficiency. The LESO-based approach in [
46] achieves robustness without additional sensors, but its effectiveness at higher walking speeds remains a challenge.
The referenced studies collectively highlight advancements in rehabilitation exoskeleton control systems, focusing on robustness, precision, and adaptability. Sliding mode control emerges as a common approach due to its resilience to disturbances, while strategies like RBFNN integration and fractional-order dynamics enhance system performance. Despite their strengths, challenges such as parameter tuning, chattering, and real-world validation remain. Future research should emphasize clinical trials and dynamic adaptability to diverse patient needs, ensuring these innovations translate effectively into rehabilitation technologies.
Table 4 summarizes the articles discussed in this section:
The next section will discuss the application of fuzzy logic-based control methods in rehabilitation exoskeleton robots.
Fuzzy Logic-based Control System
A fuzzy logic-based control system is an intelligent control strategy that mimics human reasoning by employing a rule-based approach to handle imprecise, uncertain, or nonlinear systems. Unlike conventional control methods that require precise mathematical models, fuzzy logic control relies on linguistic rules and fuzzy sets to represent and process data. These systems interpret inputs using fuzzy membership functions, applying a set of “if-then” rules to determine outputs. The following section will introduce the recent advancements of Fuzzy Logic-based control systems in exoskeleton robot-assisted rehabilitation applications.
The referenced studies by Abdallah et al. [
53], the twin-double pendulum exoskeleton system [
54], and the fuzzy radial-based impedance controller (RBF-FVI) [
55] all focus on advancing fuzzy logic-based control systems in rehabilitation robotics. These papers explore different applications of fuzzy logic control (FLC) in upper and lower-limb exoskeletons, emphasizing precision, adaptability, and patient-centered design. This analysis compares and contrasts these systems based on criteria such as application focus, control architecture, adaptability, experimental validation, and real-world applicability.
Application Focus
Each study focuses on different rehabilitation needs. Abdallah et al. [
53] developed a fuzzy logic-based optimized stimulation control system (OSCS) for upper-limb rehabilitation. This system integrates a robotic exoskeleton and functional electrical stimulation (FES) to improve the range of motion for patients with motor impairments. The fuzzy logic controller adjusts muscle stimulation parameters based on real-time pain and muscle activity monitoring, enhancing therapy precision.
The twin-double pendulum exoskeleton system [
54] addresses lower-limb rehabilitation and mobility assistance. It uses fuzzy logic control to manage joint movements in a twin-double pendulum model of human legs. This study targets cost-effective and simplified designs, making it suitable for widespread rehabilitation use.
In contrast, the fuzzy radial-based impedance controller (RBF-FVI) [
55] supports patients with movement disorders using a six-degree-of-freedom (6-DOF) lower-limb exoskeleton. Its focus is on trajectory tracking, dynamic force adaptation, and improving human–machine coupling during gait rehabilitation. Unlike the other two studies, the RBF-FVI controller combines fuzzy logic with neural networks to enhance impedance control for smoother movement adaptation.
Control Architecture and Methods
Abdallah et al. [
53] designed a fuzzy logic-based control system with five membership functions for input parameters and three for muscle contractions. These allow precise adjustments to FES parameters like pulse amplitude and rate. The integration of fuzzy logic with FES enhances therapeutic outcomes by balancing muscle stimulation and patient comfort.
The twin-double pendulum exoskeleton system [
54] uses a simpler FLC architecture, relying on fuzzification, inference, and defuzzification to generate control signals for actuators. This approach processes error and error derivation inputs to ensure accurate joint movement control. The minimalist architecture, supported by one encoder and one potentiometer per joint, achieves cost-efficiency without compromising reliability.
The RBF-FVI controller [
55] features a more sophisticated architecture. It combines an inner-loop fuzzy position controller with an outer-loop impedance controller. The radial basis function neural network (RBFNN) in the outer loop dynamically adjusts impedance parameters, enabling real-time adaptation to system uncertainties and improving performance during therapy. This dual-loop design integrates force-position adjustments for improved trajectory tracking and compliance.
Adaptability and Robustness
Adaptability is a critical feature across all studies. Abdallah et al. [
53] focused on real-time adaptation through fuzzy logic, enabling dynamic pain assessment and rehabilitation adjustments. This adaptability ensures that therapy is personalized and minimizes patient discomfort.
The twin-double pendulum system [
54] prioritizes robustness and simplicity, demonstrating consistent performance under varying conditions, including different load weights and limb sizes. Its ability to adapt to external changes with minimal sensors highlights its efficiency in cost-sensitive environments.
The RBF-FVI controller [
55] stands out for its high adaptability due to the RBFNN. This neural network allows the system to handle dynamic uncertainties and real-time force variations during human–robot interaction. However, sudden torque changes during gait transitions suggest further refinement is needed to enhance system responsiveness and stability.
Experimental Validation
Each study validates its control system using simulations and experiments. Abdallah et al. [
53] conducted clinical trials that demonstrated significant improvements in patients’ range of motion. These trials confirm the system’s effectiveness in real-world applications, emphasizing its therapeutic potential.
The twin-double pendulum system [
54] underwent MATLAB/Simulink simulations to evaluate tracking errors and robustness. Results showed low hip and knee joint tracking errors, ranging from 1.73 to 3.47 degrees, validating its reliability. While cost-effective and robust, this system lacks extensive real-world testing on diverse patient populations.
The RBF-FVI controller [
55] underwent both simulations and hardware tests. It achieved minimal joint angle errors and superior trajectory tracking compared to traditional impedance control methods. Compliance control ensured safe interaction forces during rehabilitation, although sudden torque changes during gait phase transitions highlighted areas for improvement. The study suggests future tests on stroke patients to further validate its efficiency in clinical settings.
Real-World Applicability
The real-world applicability of each system varies. Abdallah et al. [
53] demonstrated strong clinical relevance by integrating fuzzy logic with FES in upper-limb exoskeletons. The LabVIEW interface ensures precise control and real-time adjustments, making it suitable for practical rehabilitation scenarios. However, the focus on upper limbs limits its applicability to other rehabilitation needs.
The twin-double pendulum exoskeleton [
54] emphasizes simplicity and cost-effectiveness. Its minimalist design reduces manufacturing costs, making it accessible for widespread use in rehabilitation settings. However, its reliance on simulations limits the demonstration of real-world benefits, and further clinical testing is necessary to confirm its effectiveness across diverse patient groups.
The RBF-FVI controller [
55] offers significant potential for real-world use due to its advanced architecture. It combines fuzzy logic, impedance control, and RBFNN to deliver smooth and adaptive rehabilitation movements. However, the complexity of this system may pose challenges in cost-sensitive or resource-limited settings. Future clinical trials on patients with neurological impairments are needed to assess its practical impact comprehensively.
Performance Metrics
In terms of performance, Abdallah et al. [
53] achieved significant improvements in the patient range of motion by dynamically adjusting muscle stimulation based on real-time pain assessment. This approach prioritizes patient comfort while ensuring effective rehabilitation.
The twin-double pendulum system [
54] demonstrated low tracking errors for hip and knee joints in simulations. While its cost-effective design makes it appealing, further improvements in accuracy and clinical testing are needed to strengthen its case for rehabilitation use.
The RBF-FVI controller [
55] excelled in trajectory tracking, outperforming conventional impedance control systems. Its ability to adapt to dynamic uncertainties ensures precise and stable movement. However, the system requires optimization to handle sudden torque changes during gait transitions, highlighting a trade-off between complexity and responsiveness.
Each study has unique strengths and limitations. Abdallah et al. [
53] effectively combined fuzzy logic and FES for upper-limb rehabilitation, achieving precise and adaptive control. However, its narrow focus on upper limbs limits its broader applicability.
The twin-double pendulum system [
54] excels in simplicity, cost-efficiency, and robust control under varying conditions. Its minimal sensor setup reduces costs, but the lack of extensive clinical validation limits its demonstrated impact in practical rehabilitation scenarios.
The RBF-FVI controller [
55] stands out for its advanced architecture and adaptability. By integrating RBFNN, it addresses system uncertainties and enhances trajectory tracking. However, its complexity and reliance on expensive components may limit accessibility in low-resource settings. The need for further optimization and testing with diverse patient groups also highlights areas for improvement.
The referenced studies illustrate the potential of fuzzy logic control systems in rehabilitation robotics. Abdallah et al. [
53] highlight the benefits of integrating fuzzy logic with FES for adaptive and patient-specific upper-limb rehabilitation. The twin-double pendulum system [
54] demonstrates the feasibility of cost-effective and robust control for lower-limb exoskeletons, focusing on simplicity and affordability. The RBF-FVI controller [
55] showcases the power of combining fuzzy logic, impedance control, and neural networks to deliver adaptive and precise rehabilitation movements for lower-limb exoskeletons.
Each study contributes valuable insights, but further clinical testing and optimization are needed to address limitations and enhance real-world applicability. Combining the strengths of these approaches such as the simplicity of [
54] with the advanced adaptability of [
55] could lead to more effective and accessible rehabilitation systems. These advancements underscore the growing role of fuzzy logic control in creating intelligent, patient-centered rehabilitation technologies.
Table 5 summarizes the articles discussed in this section: