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Purpose – The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle-Torsion Spring (PM-TS) for finger therapy. PM has complex nonlinear dynamics, which makes PM modelling difficult. To realize high-accurate tracking for the robotic hand, an Echo State Network (ESN)-based PID adaptive controller is proposed, even though the plant model is unknown. Design/methodology/approach – To drive a single joint of rehabilitation robotic hand, the paper proposes a new PM-TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Based on the novel actuator, a wearable robotic hand is designed. By employing the model-free approximation capability of ESN, the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand. An ESN together with Recursive Least Square (RLS) is called a RLSESN, where the ESN output weight matrix is updated by the online RLS learning algorithm. Findings – Practical experiments demonstrate the validity of the PM-TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller. In addition, they also verify the effectiveness of the proposed rehabilitation robotic hand. Originality/value – A new PM-TS actuator configuration that uses a PM and a torsion spring for bi-directional movement of joint is presented. By utilizing the new PM-TS actuator, a novel wearable rehabilitation robotic hand for finger therapy is designed. Based on the unknown plant model, the RLSESN_PID controller is proposed to attain satisfactory performance. [ABSTRACT FROM AUTHOR]

To realize a high-accurate trajectory tracking control of the Pneumatic Muscle Actuator (PMA), a comprehensive single-layer neural network (SNN) and Echo State Neural Network (ESN) based predictive control with particle swarm optimization (PSO) is proposed, where PSO optimizes the weight coefficients of the SNN and the ESN state is updated by the online Recursive Least Square (RLS) algorithm for predictive control. Based on Lyapunov theory, the learning convergence theorem is established to guarantee the stability of the closed-loop system. The proposed control algorithm is employed for the trajectory tracking control of PMA. The interface between the xPC target and the virtual instrument was established to realize the real-time control and to make the control more accurate and stable. Both simulations and experiments were performed to verify the proposed methods. The experiments were conducted on the real PMA system, which was connected with the xPC target system. The results demonstrate the validity of PMA as well as the effectiveness of the novel control algorithm. [ABSTRACT FROM AUTHOR]