Search Results (86)

This work addresses the problem of globally stabilizing underactuated systems with two inputs and three degrees of freedom. Through appropriate transformations, such dynamics can be expressed as second-order nonholonomic systems in chained form. A time-varying control framework is developed to overcome the absence of smooth static feedback laws and to drive the system trajectories to the origin. Explicit criteria for selecting feedback gains are derived, and velocity signals are reconstructed using a higher-order sliding-mode observer. A complete stability analysis is provided, ensuring exponential convergence of the closed-loop system under the proposed design. To illustrate its effectiveness, the method is applied to two benchmark problems: an underactuated hovercraft and a vertical takeoff and landing (VTOL) aircraft. Simulation results confirm the practicality and robustness of the proposed observer-based stabilization scheme for second-order chained systems. Full article

This study proposes a sliding-mode-based adaptive control framework for symmetric quad-rotor altitude and attitude tracking under parametric uncertainties and mismatched disturbances. To address mismatched disturbances, a finite-time disturbance observer (DO) is integrated into a high-order terminal sliding mode manifold design. While conventional sliding mode control suffers from dependence on precise dynamic models that are unavailable in quad-rotor applications, we devise a fully connected double hidden layer recurrent neural network (FCDHRNN) with full interlayer feedback to approximate unmodeled dynamics. The structure uses double hidden layer connections to strengthen the approximation ability, and its double-layer structure achieves higher accuracy and generalization ability and uses fewer neurons than the single-hidden-layer network. Through Lyapunov stability analysis, weight adaptation laws are rigorously derived to guarantee finite-time convergence of both tracking errors and estimation residuals. Simulation results show that the proposed scheme has superior performance compared with the existing quad-rotor control scheme. Full article

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains $(k_0^{*},k_1^{*})$ over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective $\tilde{J}$ constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 ($7.36\times {10}^{-5}$ rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 ($8.18\times {10}^{-3}$ rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints ($7.81\times {10}^{-5}$ rad, $8.39\times {10}^{-3}$ rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article

With the continuous expansion of the application field of mobile robots, the diversification and complexity of the application scenarios have put forward higher requirements for their motion control performance. In order to enhance the ability to brake fast and to prevent slipping when the mobile robot is working, a slipping ratio tracking strategy based on an improved integral sliding mode algorithm regulated by a composite convergence law is proposed. The composite convergence law is proposed by integrating the power law with the exponential convergence law. A mathematical model of a single-wheeled mobile robot is established, a relationship curve between slipping ratio and pavement adhesion coefficient is introduced to estimate the pavement adhesion coefficient, and an improved integral sliding mode controller is designed. The stability is verified using Lyapunov stability theory. The slipping ratio is tracked based on the integral sliding-mode controller, and the slipping ratio is corrected to control the wheel slipping ratio near the ideal optimal slipping ratio. Furthermore, simulation experiments are conducted in MATLAB/Simulink (R2020b) under two typical working conditions to compare the proposed control strategy with conventional sliding-mode control regulated by an exponential convergence law. Finally, the mobile robot slipping ratio tracking experiments are conducted to verify the simulation results, which show that the proposed control strategy enables the mobile robot to reach the ideal braking state more quickly and stably, thereby improving the braking effect of the ABS braking system of the mobile robots. Full article

The permanent magnetic synchronous motor (PMSM) is of significant use for the centrifugal hydrogen compressor (CHC) in the hydrogen fuel cell system. In order to satisfy the demand for improving the CHC’s performance, including higher accuracy, higher response speed, and wider speed range, this paper proposes a novel adaptive super-twisting sliding mode observer (ASTSMO)-based position sensorless control strategy for the highspeed PMSM. Firstly, the super-twisting algorithm (STA) is introduced to the sliding mode observer (SMO) to reduce chattering and improve the accuracy of position estimation. Secondly, to increase the convergence speed, the ASTSMO is extended with a linear correction term, where an extra proportionality coefficient is used to adjust the stator current error under dynamic operation. Finally, a novel adaptive law is designed to solve the PMSM’s problems of wide speed change, wide current variation, and inevitable parameters fluctuation, which are caused by the CHC’s complex working environment like frequent load changes and significant temperature variations. In the experimental verification, the position accuracy and dynamic performance of the PMSM are both improved. It is also proved that the proposed strategy can guarantee the stable operation and fast response of the CHC, so as to maintain the reliability and the hydrogen utilization of the hydrogen fuel cell system. Full article

An electro-hydraulic servo loading system for a pavement mechanical properties test device was designed. The simulation analysis and test results showed that the PID control met the design requirements, but the output’s maximum error did not. Therefore, a fast terminal sliding mode control strategy with an extended state observer (ESO) was proposed. A tracking differentiator was constructed to obtain smooth differential signals from the input signals. The order of the system was reduced by considering the third and higher orders of the system as the total disturbance, and the states and the total disturbance of the system were estimated using the ESO. The fast terminal sliding mode control achieved fast convergence of the system within a limited time. The simulation results showed that the proposed control strategy improved the system accuracy and anti-disturbance ability, and system control performance was optimized. Full article

In practical applications, most systems operate based on digital signals obtained through sampling. Applying fractional-order control to spacecraft attitude control is meaningful for achieving better performance, especially in the coordination of the multi-spacecraft attitude system. In this paper, a discrete-time fractional-order sliding mode attitude control problem is studied for multi-spacecraft systems based on the fully actuated system approach. Firstly, a discrete-time disturbance observer based on the fractional-order theory is constructed to estimate the disturbance. Secondly, a discrete-time fractional-order sliding mode controller is designed by combining the transformed fully actuated discrete-time system and the disturbance observer. Subsequently, every spacecraft can track the desired attitude under the designed controller. Finally, the simulation results show that the developed control method achieves faster convergence, smaller overshoot, and higher control accuracy. Full article

This paper proposes an adaptive path tracking control method tailored for intelligent vehicles, aiming to enhance accuracy and stability. Initially, based on the traditional model predictive control (MPC) theory, the lateral speed stability boundary concerning the vehicle yaw rate is derived to establish the constraint conditions. Subsequently, optimal time domain parameters are determined across 100 typical curve conditions using a genetic algorithm. To achieve condition-adaptive path tracking control, speed and road curvature feedback are integrated into the MPC controller, enabling real-time adjustment of optimal control parameters. The simulation results from CarSim and Simulink co-simulation, as well as hardware-in-the-loop (HIL) experiments, demonstrate that the proposed method significantly improves path tracking accuracy for intelligent vehicles under varying curvature path conditions, outperforming both traditional MPC and higher-order sliding mode control (HOSMC) controllers. Full article

In contrast to the majority of model-based terminal sliding mode control (TSMC) approaches that rely on the plant physical model and/or data-driven adaptive pointwise model, this study treats the unknown dynamic plant as a total uncertainty in a black box with enabled control inputs and attainable outputs (either measured or estimated), which accordingly proposes a model-free (MF) nonsingular terminal sliding mode control (MFTSMC) for higher-order dynamic systems to reduce the tedious modelling work and the design complexity associated with the model-based control approaches. The total model-free controllers, derived from the Lyapunov differential inequality, obviously provide conciseness and robustness in analysis/design/tuning and implementation while keeping the essence of the TSMC. Three simulated bench test examples, in which two of them have representatively numerical challenges and the other is a two-link rigid robotic manipulator with two input and two output (TITO) operational mode as a typical multi-degree interconnected nonlinear dynamics tool, are studied to demonstrate the effectiveness of the MFTSMC and employed to show the user-transparent procedure to facilitate the potential applications. The major MFTSMC performance includes (1) finite time ($2.5\pm 0.05$ s) dynamic stabilization to equilibria in dealing with total physical model uncertainty and disturbance, (2) effective dynamic tracking and small steady state error $0\pm 0.002$, (3) robustness (zero sensitivity at state output against the unknown bounded internal uncertainty and external disturbance), (4) no singularity issue in the neighborhood of TSM $\sigma =0$, (5) stable chattering with low amplitude ($\pm 0.01$) at frequency 50 mHz due to high gain used against disturbance $d(t)=100+30\sin (2\pi t)$). The simulation results are similar to those from well-known nominal model-based approaches. Full article

In order to achieve precise vector control of permanent magnet synchronous motors and maintain reliability during operation, it is necessary to obtain more accurate rotor position and rotor angular velocity. However, the installation of sensors can lead to increased motor volume and cost, so it is necessary to use sensorless estimation of rotor position and angular velocity. The switching function of traditional sliding mode observers is a discontinuous sign function, which can lead to serious chattering problems and phase lag problems caused by low-pass filters. Therefore, this article proposes an improved fuzzy hyper spiral sliding mode observer based on the traditional sliding mode observer. Firstly, the observer takes the current as the observation object and uses the difference between the actual current and the observed current and its derivative as the fuzzy input. The sliding mode gain is used as the fuzzy output to tune the parameters of the sliding mode gain. Secondly, in response to the chattering problem caused by traditional sliding mode control methods, the hyper spiral algorithm is adopted and a $sin (arctan(nx)$) nonlinear function is introduced instead of the sign function as the switching function to achieve switch continuous sliding mode control, thereby suppressing the system’s chattering. Finally, the rotor position information is extracted through an orthogonal normalized phase-locked loop to improve observation accuracy. For time-varying nonlinear permanent magnet synchronous motor control systems, fractional order PID can improve the control accuracy of the system and adjust the dynamic performance of the system more quickly compared to traditional PID control algorithms. Therefore, fractional order PID is used instead of traditional PID controllers. By comparing simulation experiments with traditional sliding mode observers and fuzzy improved adaptive sliding mode observers, it was proven that the improved fuzzy super spiral sliding mode observer can effectively suppress chattering and extract rotor position with higher accuracy, a faster response rate, and better dynamic performance. This provides a new approach for the sensorless control strategy of permanent magnet synchronous motors. Full article

This study proposes a novel nonlinear adaptive fuzzy hybrid sliding mode (AFHSM) control strategy for the precise trajectory tracking of autonomous mobile robots (AMRs) equipped with four Mecanum wheels. The control design addresses the inherent complexities of such platforms, which include strong system nonlinearities, significant parametric uncertainties, torque saturation effects, and external disturbances that can adversely affect dynamic performance. Unlike conventional approaches that rely on model linearization or dimension reduction, the proposed AFHSM control retains the full nonlinear characteristics of the system to ensure accurate and robust control. The controller is systematically derived from the trajectory-tracking error dynamics between the AMR and the desired trajectory (DT). It integrates higher-order sliding mode (SM) control, fuzzy logic inference, and adaptive learning mechanisms to enable real-time compensation for model uncertainties and external perturbations. In addition, a saturation handling mechanism is incorporated to ensure that the control signals remain within feasible limits, thereby preserving actuator integrity and improving practical applicability. The stability of the closed-loop nonlinear system is rigorously established through the Lyapunov theory, guaranteeing the asymptotic convergence of tracking errors. Comprehensive simulation studies conducted under severe conditions with up to 60 percent model uncertainty confirm the superior performance of the proposed method compared to classical SM control. The AFHSM control consistently achieves lower trajectory and heading errors while generating smoother control signals with reduced torque demand. This improvement enhances tracking precision, suppresses chattering, and significantly increases energy efficiency. These results validate the effectiveness of the AFHSM control approach as a robust and energy-aware control solution for AMRs operating in highly uncertain and dynamically changing environments. Full article

To reduce weight and improve dexterity performance, dexterous robot fingers usually use tendons for transmission, which may lead to complex nonlinear control problems. In order to improve tracking performance in joint space, this paper proposes an anti-interference controller, which synthesizes the nonsingular fast terminal sliding mode technique. A flexible joint dynamic model is established considering the flexibility of the cable-driven mechanism. A nonlinear disturbance observer is adopted to estimate and compensate the system uncertainties and various disturbances, and global fast terminal sliding mode is used to ensure good control performance in both the reaching phase and the sliding mode phase. Furthermore, symmetry is used to simplify dynamic modeling and control design, and the stability of the controller is proven with Lyapunov theory. Finally, the effectiveness of the controller is verified through simulation experiments. The simulation results demonstrate that the proposed controller achieves a steady state in 0.3 s, higher tracking accuracy than the other controllers through quantitative analysis of MAE and MSE metrics, and stronger anti-interference capability, which can satisfy the requirements of finger dexterity operation. Full article

This article presents an improved control strategy based on the traditional sliding-mode controller (SMC), integrated with a generalized higher-order disturbance observer (DOB), to enhance the speed regulation of permanent magnet synchronous motors (PMSMs) during operation. The proposed method is mitigated and employed to smooth system disturbances by utilizing the disturbance observer (DOB) in conjunction with a low-pass filter (LPF). The low-pass filter is employed to smooth the q-axis current component and reduce speed oscillations. Initially, the paper builds upon the conventional control law and introduces a more optimized approach. The stability of the control strategy is then analyzed using Lyapunov stability theory. Different sliding surfaces are compared to develop the proposed SMC. Finally, the novel control method is introduced by integrating the DOB with the LPF. This approach results in improved speed stability and enhanced adaptability compared to traditional SMC techniques. Simulation and experimental results demonstrate that the proposed control algorithm outperforms traditional methods, particularly in terms of the dynamic response and disturbance rejection. Full article

In this study, a PID-nested nonsingular terminal sliding controller is proposed to minimize the chattering phenomenon. By adding both integral and derivative errors of states into the nonsingular terminal sliding manifolds, a composite sliding manifold was created. Compared to nonsingular terminal sliding mode (NTSM) techniques, this sliding manifold can handle higher-order derivatives. The speed of the motor is controlled by a sliding control law determined through a higher-order integral, making the signal continuous, and the sliding manifold is achieved in finite time. A special full-order terminal sliding mode manifold is introduced, which allows the system to converge in finite time while being chattering-free and avoiding the singularity phenomenon of conventional and terminal sliding modes. The controller’s efficiency is demonstrated with faster convergence time and fewer errors than state-of-the-art controllers, which is demonstrated through both simulation and experiment. Full article

This paper addresses the issue of wheelset slip in trains caused by low-adhesion track surfaces and proposes an active anti-slip tracking control strategy. Considering the wide operational range of trains and the complex adhesion conditions between wheels and rails, a comprehensive model of the train, incorporating adhesion effects, is developed and then transformed into a mathematical model with perturbations. To tackle the slip phenomenon on low-adhesion track surfaces, a robust adhesion observer with high dynamic accuracy is designed. Building on this, an active anti-slip strategy is proposed to ensure that the control command does not exceed the maximum traction force available from the track surface. To further enhance controller performance, higher-order sliding mode control is integrated with a saturation compensation law. Finally, a Hardware-in-the-Loop (HIL) platform is constructed using a Digital Signal Processor (DSP) controller and a Modular Test (MT) PXI real-time simulator. The simulator loads the adhesion model, while the DSP controller executes the designed anti-slip control algorithm. Experimental results demonstrate that the proposed controller effectively prevents wheelset slip under low-adhesion conditions and significantly reduces tracking errors along the target speed-displacement curve. Full article