RLSESN‐based PID adaptive control for a novel wearable rehabilitation robotic hand driven by PM‐TS actuators

Abstract

PurposeThe 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/approachTo 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.FindingsPractical 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/valueA 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.

Controller Design Based on Echo State Network with Delay Output for Nonlinear System

“…Compared with the traditional RNN, the advantages of ESN are reflected in the weight selection and weight learning of network, i.e., only the output weight needs to be learned. erefore, ESN not only has the network structure of traditional RNN but also has the characteristics of deep learning, such that ESN can be applied in many fields, for example, time-series prediction [21][22][23][24], filtering or control [25][26][27][28], dynamic pattern recognition [29][30][31], optimization [32], system identification [31,33,34], and big data application [35,36]. us, comparing with the existing controller design methods based on neural network, ESN can avoid lots of adjusting parameters and the limitation of calculation.…”

“…Combining with the advantages of ESN, some improved methods have been applied in many control fields. For example, in [26], a PID adaptive controller based on RLSESN is proposed for realizing high-accurate tracking of the rehabilitation robotic hand. In [27], ESNBIMC is proposed for the pneumatic muscle system.…”

A Survey on Hand Pose Estimation with Wearable Sensors and Computer-Vision-Based Methods

“…For example, the Humanglove [45] has 20 Hall-effect sensors that can measure the joint angles of fingers. Wu et al [46] proposed a wearable rehabilitation robotic hand using Hall-effect sensors that can be worn on the forearm. Another light-weight system called Finexus was designed as a multipoint tracking system by instrumenting the fingertips with electromagnets [47].…”

Trajectory tracking control of robotic transcranial magnetic stimulation

“…On the other hand, when the input value is relatively large, the amount of adjustment of the arm is too large to oscillate back and forth. Therefore, we use an adaptive PD controller to determine the angle of movement of the manipulator (Wu et al, 2012).…”