An Echo State Gaussian Process-Based Nonlinear Model Predictive Control for Pneumatic Muscle Actuators

• Published in: IEEE transactions on automation science and engineering 2019-07, Vol.16 (3), p.1071-1084
• Main Authors: Huang, Jian, Cao, Yu, Xiong, Caihua, Zhang, Hai-Tao
• Format: Article
• Language: English
• Subjects: Actuators; Algorithms; Computer simulation; Echo state Gaussian process (ESGP); Feedback control; Gaussian process; Model accuracy; Muscles; Nonlinear control; nonlinear model predictive control (NMPC); Nonlinear systems; Optimization; Parameter identification; pneumatic muscle actuator (PMA); Pneumatic systems; Predictive control; Rehabilitation; Rehabilitation robots; Robots; Sequences; Stability analysis; Task analysis; Tracking; Uncertainty
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2018.2867939

Pneumatic muscle actuators (PMAs), a kind of soft/compliant actuators, have been attracted a great deal of attention in the studies of rehabilitation robots. However, the nonlinearities, uncertainties, hysteresis, and time-varying features of PMAs bring a lot of difficulties in their high-precision trajectory tracking tasks. In this paper, an echo state Gaussian process-based nonlinear model predictive control (ESGP-NMPC) is designed for the PMAs. The proposed strategy is comprised of an ESGP, which is suitable for modeling unknown nonlinear systems as well as measuring their uncertainties, and a gradient descent optimization algorithm for calculating the control signal sequences. Based on the Lyapunov theorem, characteristics of the closed-loop system are analyzed to guarantee the asymptotical stability. Both simulations and physical experiments are carried out to illustrate the validity of the proposed control strategy. Compared with other conventional methods, the ESGP-NMPC can achieve a better model fitting for the PMA and control performance for the high-precision tracking tasks. Note to Practitioners -High-precision control of pneumatic muscle actuators (PMAs) is a vital problem when PMAs are utilized as actuators of rehabilitation robots since the patient's safety and the performance of rehabilitation tasks are largely dependent on the accuracy of the actuators. Conventional model-based control approaches usually require relatively accurate identification of system parameters, which is difficult for the PMA, owing to its strong nonlinear and time-varying characteristics. This paper proposes a new model predictive control method based on an echo state Gaussian process that can describe the unknown dynamics of a PMA due to its universal approximation property. Through the optimization method, the controller can be efficiently realized and presents better performances than some comparatives. By applying this approach, it is possible to achieve not only high-precision control of PMAs but also a certain degree of robustness to the load.