Pneumatic artificial muscles (PAMs) have been widely used in actuation of medical devices due to their intrinsic compliance and high power to weight ratio features. However, the nonlinearity and time-varying nature of PAMs makes it challenging to maintain highperformance tracking control. In this paper, a High-Order Pseudo-Partial Derivative based Model-Free Adaptive Iterative Learning Controller (HOPPD-MFAILC) is proposed to achieve fast convergence speed. The dynamics of PAM is converted into a dynamic linearization model during iterations, meanwhile, a high-order estimation algorithm is designed to estimate the pseudo-partial derivative component of the linearization model by only utilizing the input and output data in previous iterations. The stability and convergence performance of the controller is verified through theoretical analysis. Simulation and experimental results on PAM demonstrate that the proposed HOPPD-MFA ILC can track the desired trajectory with improved convergence and tracking performance. Index Terms-Pneumatic artificial muscle, model-free adaptive control, iterative learning control, convergence. I. INTRODUCTION NEUMATIC artificial muscle (PAM) is a tube-like actuator that largely mimics biological human muscle functions [1]. Compared to traditional electrical motors and hydraulic actuators, the lightweight, high compliance and high power-to-weight ratio of PAMs [2] have fueled their popularity among assistive exoskeletons and rehabilitation robots, such as the upper limb exoskeleton series RUPERT [3] and the lower limb orthotics KAFO [4]. However, unlike the conventional actuators adopted in Lokomat [5] and ArmeoPower [6], the nonlinear and time-varying nature of PAMs may cause Manuscript
A rehabilitation robot plays an important role in relieving the therapists’ burden and helping patients with ankle injuries to perform more accurate and effective rehabilitation training. However, a majority of current ankle rehabilitation robots are rigid and have drawbacks in terms of complex structure, poor flexibility and lack of safety. Taking advantages of pneumatic muscles’ good flexibility and light weight, we developed a novel two degrees of freedom (2-DOF) parallel compliant ankle rehabilitation robot actuated by pneumatic muscles (PMs). To solve the PM’s nonlinear characteristics during operation and to tackle the human-robot uncertainties in rehabilitation, an adaptive backstepping sliding mode control (ABS-SMC) method is proposed in this paper. The human-robot external disturbance can be estimated by an observer, who is then used to adjust the robot output to accommodate external changes. The system stability is guaranteed by the Lyapunov stability theorem. Experimental results on the compliant ankle rehabilitation robot show that the proposed ABS-SMC is able to estimate the external disturbance online and adjust the control output in real time during operation, resulting in a higher trajectory tracking accuracy and better response performance especially in dynamic conditions.
Ankle dysfunction is common in the public following injuries, especially for stroke patients. Most of the current robotic ankle rehabilitation devices are driven by rigid actuators and have problems such as limited degrees of freedom, lack of safety and compliance, and poor flexibility. In this paper, we design a new type of compliant ankle rehabilitation robot redundantly driven by pneumatic muscles (PMs) and cables to provide full range of motion and torque ability for the human ankle with enhanced safety and adaptability, attributing to the PM's high power/mass ratio, good flexibility and lightweight advantages. The ankle joint can be compliantly driven by the robot with full three degrees of freedom to perform the dorsiflexion/plantarflexion, inversion/ eversion, and adduction/abduction training. In order to keep all PMs and cables in tension which is essential to ensure the robot's controllability and patient's safety, Karush-Kuhn-Tucker (KKT) theorem and analytic-iterative algorithm are utilized to realize a hierarchical force-position control (HFPC) scheme with optimal force distribution for the redundant compliant robot. Experiment results demonstrate that all PMs are kept in tension during the control while the position tracking accuracy of the robot is acceptable, which ensures controllability and stability throughout the compliant robot-assisted rehabilitation training.
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