Rehabilitation robots are designed to help patients improve their recovery from injury by supporting them to perform repetitive and systematic training sessions. These robots are not only able to guide the subjects’ lower-limb to a designate trajectory, but also estimate their disability and adapt the compliance accordingly. In this research, a new control strategy for a high compliant lower-limb rehabilitation orthosis system named AIRGAIT is developed. The AIRGAIT orthosis is powered by pneumatic artificial muscle actuators. The trajectory tracking controller based on a modified computed torque control which employs a fractional derivative is proposed for the tracking purpose. In addition, a new method is proposed for compliance control of the robotic orthosis which results in the successful implementation of the assist-as-needed training strategy. Finally, various subject-based experiments are carried out to verify the effectiveness of the developed control system.

Pneumatic artificial muscle (PAM) is a potential actuator in human–robot interaction systems, especially rehabilitation systems. However, PAM is a nonlinear actuator with uncertainty and a considerable delay in characteristics, making control challenging. This study presents a discrete-time sliding mode control approach combined with the adaptive fuzzy algorithm (AFSMC) to deal with the unknown disturbance of the PAM-based actuator. The developed fuzzy logic system has parameter vectors of the component rules that are automatically updated by an adaptive law. Consequently, the developed fuzzy logic system can reasonably approximate the system disturbance. When operating the PAM-based system in multi-scenario studies, experimental results confirm the efficiency of the proposed strategy.

This paper presents a novel and comprehensive control framework for the Rotary Inverted Pendulum (RIP), focusing on a hybrid control strategy that addresses the limitations of conventional methods in nonlinear and complex systems. The proposed controller synergistically combines an Optimized Fuzzy Logic Controller (OFLC) with Sliding Mode Control (SMC), leveraging the strengths of both techniques to achieve superior performance. The integration of Particle Swarm Optimization (PSO) into the OFLC significantly enhances its adaptability and precision, while the SMC law provides robust disturbance rejection and system stability. Another key innovation in this framework is the incorporation of an Extended State Observer (ESO), which ensures accurate state estimation and reduces sensor dependency. The most significant physical outcome of this work is the demonstrated improvement in the system’s stability and robustness, even under external disturbances and uncertainties, showcasing the potential of the proposed control framework to achieve precise, stability control in nonlinear systems like the RIP. Extensive simulations validate the effectiveness of the proposed controller, demonstrating significant improvements in stability, disturbance rejection, and control precision, even under disturbance. The results highlight the potential of this approach as a robust solution for complex control systems, offering a significant advancement in the field of nonlinear system control with wide-ranging applications.

Research on combined control methods for quadrotors has focused on trajectory tracking, robust control, neural networks, parameter optimization, and path planning. While previous studies have not fully integrated all of these aspects, this study presents a comprehensive control framework that seamlessly combines robust control strategies, neural network-based uncertainty approximation, path planning, and optimization to achieve precise and reliable trajectory tracking of quadrotors operating under significant model uncertainties and external disturbances. At the heart of the framework is the Integrated sliding mode control (Intergrated SMC), a design that merges the inherent robustness of sliding mode control with the adaptive approximation capability of radial basis function (RBF) neural networks. The fusion of these two elements not only ensures stability but also strengthens the system’s resilience, delivering high-precision tracking even in the presence of unmodeled dynamics and external disturbances. The framework also incorporates the rapidly-exploring random tree star (RRT*) algorithm for trajectory planning, allowing the generation of collision-free and asymptotically optimal reference paths capable of navigating environments with complex obstacle distributions. In addition, particle swarm optimization (PSO) is employed to systematically tune the controller gains and neural network parameters, thereby enhancing overall control performance. Extensive simulations under varying conditions of model mismatch and disturbances confirm the superior performance of the proposed integrated approach, demonstrating significant improvements in tracking accuracy and disturbance rejection compared to conventional control methods. This unified architecture thus provides a robust and computationally efficient solution for quadrotor trajectory tracking, maintaining high performance even in the presence of model uncertainties and external disturbances.

In lower-limb rehabilitation systems, exoskeleton robots are one of the most important components. These robots help patients to execute repetitive exercises under the guidance of physiotherapists. Recently, pneumatic artificial muscles (PAM), a kind of actuator that acts similarly to human muscles, have been chosen to power the exoskeleton robot for better human–machine interaction. In order to enhance the performance of a PAM-based exoskeleton robot, this article implements an active disturbance rejection control (ADRC) strategy with a nonlinear extended state observer (NLESO). Moreover, the stability of the closed-loop system is proved by Lyapunov’s theory. Finally, the experimental results show that with the proposed control strategy, the rehabilitation robot can effectively track the desired trajectories even when under external disturbance.