Search Results (341)

This paper addresses a common challenge in space systems: how to effectively manage multiple actuators under demanding mission conditions. We introduce a flexible, optimization-based algorithm designed to dispatch control commands among a set of available actuators while focusing on minimizing resource usage, such as power and fuel. The method guarantees feasible solutions even in the presence of actuator failures, making it highly suitable for space applications. To illustrate its versatility, we show how the algorithm can be tailored to different mission scenarios with minimal effort. Several benchmark problems were implemented and tested on space-graded hardware for processor-in-the-loop verification. For this purpose, a customized solver was developed, ensuring high numerical efficiency. This paper highlights key results that demonstrate the algorithm’s practical value and mission readiness. Full article

This work provides the first demonstration of FFTCCV as a dual-purpose method, serving both as a real-time diagnostic tool and as a phase- and morphology-engineering strategy. By adjusting the scan rate, FFTCCV directs the crystallographic evolution of Ni (OH)₂ on Ni foam—stabilizing α-nanoflakes at 0.7 V·s⁻¹ and β-platelets at 0.007 V·s⁻¹—while simultaneously enabling electrode-resolved ΔQ tracking and predictive state-of-health (SoH) monitoring. This approach enabled the precise regulation of electrode morphology and phase composition, yielding high areal capacitance (546.5 mF·cm⁻² at 5 mA·cm⁻²) with ~75% retention after 3000 cycles. These improvements advance the development of high-performance micro-supercapacitors, facilitating their integration into wearable and miniaturized devices where compact and durable energy storage is required. Beyond performance enhancement, FFTCCV also enabled continuous monitoring of capacitance during extended operation (up to 40,000 s). By recording both anodic and cathodic responses, the method provided time-resolved insights into device stability and revealed characteristic signatures of electrode degradation, phase transitions, and morphological changes. Such detection allows recognition of early failure pathways that are not accessible through conventional testing. This monitoring capability functions as an embedded health sensor, offering a pathway for predictive diagnosis of supercapacitor failure. Such functionality is particularly important for energy-driven actuators and smart materials, where uninterrupted operation and preventive maintenance are critical. FFTCCV therefore provides a scalable strategy for developing energy-autonomous microsystems with improved performance and real-time state-of-health monitoring. Full article

This research proposes a novel adaptive robust fault-tolerant controller for symmetrical robotic manipulators subject to model uncertainties and actuator failures. The key innovation lies in the design of a new sliding manifold that effectively integrates the advantages of a hyperbolic tangent function-based practical sliding manifold and a fast terminal sliding manifold. This structure not only eliminates the reaching phase and accelerates error convergence but also significantly enhances system robustness while mitigating chattering. Moreover, the proposed manifold ensures the global non-singularity of the equivalent control law, thereby improving overall stability. Another major contribution is an adjustable adaptive strategy that dynamically estimates the unknown bounds of fault information and external disturbances, reducing the reliance on prior knowledge. The stability and convergence of the robotic system under the proposed scheme are theoretically analyzed and guaranteed. Finally, simulation experiments demonstrate the superior performance of the proposed scheme. Full article

Modern automotive steering systems exhibit inherent bilateral symmetry characteristics that can be mathematically described using group theory. When component failures occur, these systems experience quantifiable symmetry-breaking patterns that serve as diagnostic indicators. This research presents an approach that combines group-theoretic principles with machine learning for automotive steering system fault diagnosis. The study introduces a physics-informed neural network architecture that leverages the mathematical structure of bilateral symmetry for enhanced fault detection capabilities. Through systematic analysis of eight distinct fault categories including sensor malfunctions, actuator degradation, control system failures, and mechanical wear patterns, the proposed framework demonstrates that symmetry-breaking signatures provide reliable diagnostic features. The framework integrates symmetric convolutional operations with transformer-based attention mechanisms, optimized through physics-constrained particle swarm algorithms. Experimental validation using both simulation data (12,500 scenarios) and physical test bench measurements shows classification accuracy of 94.2% compared to traditional CNN-LSTM (86.2%), SVM (78.9%), and Random Forest (82.7%) approaches. The bilateral symmetry analysis achieves 91.8% sensitivity for fault detection in controlled laboratory environments. These results establish the practical viability of group-theoretic methods for automotive diagnostics while providing a foundation for condition-based maintenance strategies in intelligent vehicle systems. Full article

In this paper, an adaptive sliding mode fault-tolerant control (FTC) scheme is proposed for small Quadrotor Unmanned Aerial Vehicles (QUAVs) used in education and teaching formation in the presence of systematic unknown external disturbances with actuator failures. A radial basis function neural network (RBFNN) is employed to handle the nonlinear interaction function, and a fault-tolerant-based NN extended state observer (NNESO) is designed to estimate the unknown external disturbance. Meanwhile, an adaptive fault observer is developed to estimate and compensate for the fault parameters of the system. To achieve satisfactory trajectory tracking performance for the QUAV, an adaptive sliding mode control (SMC) strategy is designed. This strategy mitigates the strong coupling effects among the design parameters within the QUAV formation. The stability of the closed-loop system is rigorously demonstrated by Lyapunov analysis, and the controlled QUAV formation can achieve the desired tracking position. Simulation results verify the effectiveness of the proposed control method. Full article

In this paper, the concept of symmetry is utilized to design active fault tolerant trajectory-tracking control of autonomous distributed-drive electric vehicles—that is, the construction and the solution of active fault tolerant trajectory-tracking controllers are symmetrical. This paper presents a hierarchical fault tolerant control strategy for improving the trajectory-tracking performance of autonomous distributed-drive electric vehicles (ADDEVs) under steer-by-wire (SBW) system failures. Since ADDEV trajectory dynamics are inherently affected by complex traffic conditions, various driving maneuvers, and other road environments, the main control objective is to deal with the ADDEV trajectory-tracking control challenges of system uncertainties, SBW failures, and external disturbance. First, the differential steering dynamics model incorporating a 3-DOF coupled system and stability criteria based on the phase–plane method is established to characterize autonomous vehicle motion during SBW failures. Then, by integrating cascade active disturbance rejection control (ADRC) with Karush–Kuhn–Tucker (KKT)-based torque allocation, the active fault tolerant control framework of trajectory tracking and lateral stability challenges caused by SBW actuator malfunctions and steering lockup is addressed. The upper-layer ADRC employs an extended state observer (ESO) to estimate and compensate against uncertainties and disturbances, while the lower-layer utilizes KKT conditions to optimize four-wheel torque distribution to compensate for SBW failures. Simulations validate the effectiveness of the controller with serpentine and double-lane-change maneuvers in the co-simulation platform MATLAB/Simulink-Carsim^® (2019). Full article

Ensuring the reliability of dialysis machines and their components, such as sensors and actuators, is critical for maintaining continuous and safe dialysis treatment for patients with chronic kidney disease. This study investigates the application of Artificial Intelligence for detecting drift in dialysis machine components by comparing a Long Short-Term Memory (LSTM) neural network with a traditional linear regression model. Both models were trained to learn normal patterns from time-dependent signals monitoring the performance of specific components of a dialytic machine, such as a weight loss sensor in the present case, enabling the detection of anomalies related to sensor degradation or failure. Real-world data from multiple clinical cases were used to validate the approach. The LSTM model achieved high reconstruction accuracy on normal signals (most errors < 0.02, maximum ≈ 0.08), and successfully detected anomalies exceeding this threshold in complaint cases, where the model anticipated failures up to five days in advance. On the contrary, the linear regression model was limited to detecting only major deviations. These findings highlighted the advantages of AI-based methods in equipment monitoring, minimizing unplanned downtime, and supporting preventive maintenance strategies within dialysis care. Future work will focus on integrating this model into both clinical and home dialysis settings, aiming to develop a scalable, adaptable, and generalizable solution capable of operating effectively across various conditions. Full article

This study develops a new integral sliding mode-based method to address the decentralized adaptive fault-tolerant guaranteed cost control (GCC) problem via a dynamic event-triggered (DET) adaptive dynamic programming (ADP) approach. Firstly, integral sliding mode control technology is applied to eliminate the influence of actuator faults, which can guarantee that the large-scale system states stay on the sliding mode surface. Secondly, the ADP algorithm based on DET mode is employed to improve the control performance for equivalent sliding mode surface and reduce computational and communication overhead. Meanwhile, the GCC method is introduced to ensure that the performance cost function is less than an upper bound while maintaining system stability. Then, through Lyapunov stability analysis, it is proven that the presented DET-GCC method based on ADP algorithm can guarantee that all signals are uniformly ultimately bounded. Finally, the validity of the developed approach is confirmed through the simulation results. Full article

Aerodynamic ablation of external control surfaces and structural complexity in hypersonic reentry vehicles (HRVs) pose significant challenges for maneuverability and system reliability. To address these issues, this study develops a novel bank-to-turn (BTT) control strategy integrating a single internal moving mass with differential ailerons, eliminating reliance on ablation-prone elevators/rudders while enhancing internal space utilization. A coupled 7-DOF dynamics model explicitly quantifies inertial-rolling interactions induced by the moving mass, revealing critical stability boundaries for roll maneuvers. To ensure robustness against aerodynamic uncertainties, aileron failures, and high-frequency mass-induced disturbances, a dynamic inversion controller is augmented with an L1 adaptive layer decoupling estimation from control for improved disturbance rejection. Monte Carlo simulations demonstrate: (1) a 20.6% reduction in roll-tracking error (L₂-norm) under combined uncertainties compared to dynamic inversion control, and (2) a 72% suppression of oscillations under aerodynamic variations. Comparative analyses confirm superior transient performance and robustness in worst-case scenarios. This work offers a practical solution for high-maneuverability hypersonic vehicles, with potential applications in reentry vehicle design and multi-actuator system optimization. Full article

Soft electrothermal actuators have attracted increasing attention in soft robotics and wearable systems due to their simple structure, low driving voltage, and ease of integration. However, traditional designs based on homogeneous or layered composites often suffer from interfacial failure and limited deformation modes, restricting their long-term stability and actuation versatility. In this study, we present a programmable soft electrothermal actuator based on a functionally graded structure composed of polydimethylsiloxane (PDMS)/multiwalled carbon nanotube (MWCNTs) composite material and an embedded EGaIn conductive circuit. Rheological and mechanical characterization confirms the enhancement of viscosity, modulus, and tensile strength with increasing MWCNTs content, confirming that the gradient structure improves mechanical performance. The device shows excellent actuation performance (bending angle up to 117°), fast response (8 s), and durability (100 cycles). The actuator achieves L-shaped, U-shaped, and V-shaped bending deformations through circuit pattern design, demonstrating precise programmability and reconfigurability. This work provides a new strategy for realizing programmable, multimodal deformation in soft systems and offers promising applications in adaptive robotics, smart devices, and human–machine interfaces. Full article

Background/Objectives: Hip-level amputees face ambulatory challenges due to the lack of a lower limb and prosthetic hip power. Some hip-level amputees restore mobility by using a prosthesis with hip, knee, and ankle joints. Powered prosthetic joints contain an actuator that provides external flexion-extension moments to assist with movement. Powered knee and powered ankle-foot units are on the market, but no viable powered hip unit is commercially available. This research details the development of a novel powered four-bar prosthetic hip joint that can be integrated into a full-leg prosthesis. Methods: The hip joint design consisted of a four-bar linkage with a harmonic drive DC motor placed in the inferior link and an additional linkage to transfer torque from the motor to the hip center of rotation. Link lengths were determined through engineering optimization. Device strength was demonstrated with force and finite element analysis and with ISO 15032:2000 A100 static compression tests. Walking tests with a wearable hip-knee-ankle-foot prosthesis simulator, containing the novel powered hip, were conducted with three able-bodied participants. Each participant walked back and forth on a level 10 m walkway. Custom hardware and software captured joint angles. Spatiotemporal parameters were determined from video clips processed in the Kinovea software (ver. 0.9.5). Results: The powered hip passed all force and finite element checks and ISO 15032:2000 A100 static compression tests. The participants, weighing 96 ± 2 kg, achieved steady gait at 0.45 ± 0.11 m/s with the powered hip. Participant kinematic gait profiles resembled those seen in transfemoral amputee gait. Some gait asymmetries occurred between the sound and prosthetic legs. No signs of mechanical failure were seen. Most design requirements were met. Areas for powered hip improvement include hip flexion range, mechanical advantage at high hip flexion, and device mass. Conclusions: The novel powered four-bar hip provides safe level-ground walking with a full-leg prosthesis simulator and is viable for future testing with hip-level amputees. Full article

The operational performance of Flexible Hinge Piezoelectric Stages (FHPSs), essential components in precision engineering, is fundamentally constrained by the inherent hysteresis of the piezoelectric actuator (PEA). A significant deficiency in prevailing characterization methods is their failure to consider the dynamic nature of the mechanical preload exerted by the flexible hinge. This position-dependent preload induces substantial deviations in the PEA’s response characteristics, thereby compromising the predictive accuracy of conventional design frameworks. To address this limitation, this paper proposes a Force-Dependent Prandtl–Ishlinskii (FPI) model that explicitly formulates the PEA’s hysteretic behavior as a function of variable preload conditions. The FPI model is subsequently integrated into a comprehensive FPI-FFEM characterization framework. Within this framework, a Force-analyzed Finite Element Method (FFEM) is utilized to compute the dynamic preload throughout the actuator’s operational stroke. This information, notably neglected in conventional FEM analysis, is essential to the fidelity of the proposed FPI model. Experimental validation demonstrates the superior fidelity of the FPI model in comparison to the traditional PI model for tracking preload-induced nonlinearities. Furthermore, the complete FPI-FFEM framework exhibits substantially enhanced prediction accuracy relative to both conventional PI-FEM and advanced LDPI-FEM methodologies, as demonstrated by a significant reduction in the Mean Absolute Error (MAE). Full article

In high-dynamic bipedal locomotion control, robotic systems are often constrained by motor torque limitations, particularly during explosive tasks such as jumping. One of the key challenges in reinforcement learning lies in bridging the sim-to-real gap, which mainly stems from both inaccuracies in simulation models and the limitations of motor torque output, ultimately leading to the failure of deploying learned policies in real-world systems. Traditional RL methods usually focus on peak torque limits but ignore that motor torque changes with speed. By only limiting peak torque, they prevent the torque from adjusting dynamically based on velocity, which can reduce the system’s efficiency and performance in high-speed tasks. To address these issues, this paper proposes a reinforcement learning jump-control framework tailored for tendon-driven bipedal robots, which integrates dynamic torque boundary constraints and torque error-compensation modeling. First, we developed a torque transmission coefficient model based on the tendon-driven mechanism, taking into account tendon elasticity and motor-control errors, which significantly improves the modeling accuracy. Building on this, we derived a dynamic joint torque limit that adapts to joint velocity, and designed a torque-aware reward function within the reinforcement learning environment, aimed at encouraging the policy to implicitly learn and comply with physical constraints during training, effectively bridging the gap between simulation and real-world performance. Hardware experimental results demonstrate that the proposed method effectively satisfies actuator safety limits while achieving more efficient and stable jumping behavior. This work provides a general and scalable modeling and control framework for learning high-dynamic bipedal motion under complex physical constraints. Full article

This paper presents an alternating testing strategy to improve the reliability of multi-state safety instrumented systems (SISs) under degradation conditions. A dynamic Bayesian network (DBN) model is developed to assess SIS unavailability, integrating proof-testing parameters and capturing multi-state component behavior. Applied initially to the actuator layer of a SIS with a 1oo3 (one-out-of-three) redundancy structure, the study examines the impact of extended test durations, showing that the alternating strategy reduces non-zero test durations compared to the simultaneous test strategy. The approach is then extended to a complete SIS, with a case study demonstrating its potential to enhance system reliability and optimize maintenance management by considering degradation and redundancy factors. Full article

Ensuring the reliability and robustness of spacecraft systems remains a key challenge, particularly given the limited feasibility of continuous real-time monitoring during on-orbit operations. In the domain of Fault Detection, Isolation, and Recovery (FDIR), no universal strategy has yet emerged. Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. A high-fidelity functional engineering simulator is employed to generate realistic datasets from typical onboard signals, including sensor and actuator outputs. Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. Comparative analysis across multiple failure conditions highlights the strengths and limitations of the proposed strategies. Results indicate that Machine Learning techniques are best applied not as replacements for classical methods, but as complementary tools that enhance robustness through higher-level self-diagnostic capabilities. This synergy enables more autonomous and reliable fault management in spacecraft systems. Full article