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.

This study introduces a novel adaptive controller employing neural networks, particularly radial basis function (RBF) algorithms, to enhance the control performance of pneumatic artificial muscle (PAM)-based systems. The proposed controller seeks to address the nonlinearities and hysteresis inherent in PAM-based systems by integrating neural approximation. Experimental testing and comparisons with conventional controllers are conducted using an antagonistic configuration of PAMs. The results illustrate the precision and reliability of the proposed controller, suggesting potential for future advancements in trajectory tracking control of PAM-based systems.

In this paper, at first, a teleoperation robot systemwith haptic feedback for rehabilitation is presented. A teleoperation mechanism capable of providing force feedback by means of adjusting the system’s impedance is proposed. The stability of the teleoperation with haptic feedback via a time-delay communication environment is mathematically proved. The proposal operates theoretically with both passive and active assisted movement using teleoperation to rehabilitate of upper limb function. An EMG-moment arm model is proposed for assessing muscle strength. The performance of a two-joint link model using six muscles and a nonlinear relationship between EMG signals and muscle force is confirmed feasible in experiments with five subjects.

In this paper, present the recent advances in bimanual industrial manipulators have led to an increased interest in the specific problems pertaining to dual arm manipulation. This paper presents a control algorithm for dual arm robot that can move the object in a working plane both in translation and rotation ways. Different from other research that extend the control algorithms for a single robot to a dual arm robot because of fixed grasp assumption, this research has considered the frictional contact constraints to guarantee object grasping during moving of the object. Fast terminal sliding mode control (FTSMC) technique is used to design the controller and comparison to traditional and super-twisting sliding mode controls have been done. Simulations show the effectiveness and outperformance of the proposed control algorithm in comparison to considered sliding mode control techniques.