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Echo state network based predictive control with particle swarm optimization for pneumatic muscle actuator. (English) Zbl 1347.93126
Summary: 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 (ESNN) based predictive control with particle swarm optimization (PSO) is proposed, where PSO optimizes the weight coefficients of the SNN and the ESNN 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 is established to realize the real-time control and to make the control more accurate and stable. Both simulations and experiments are performed to verify the proposed methods. The experiments are conducted on the real PMA system, which is connected with the xPC target system. The results demonstrate the validity of PMA as well as the effectiveness of the novel control algorithm.

MSC:
93B40 Computational methods in systems theory (MSC2010)
92B20 Neural networks for/in biological studies, artificial life and related topics
90C59 Approximation methods and heuristics in mathematical programming
93E24 Least squares and related methods for stochastic control systems
93C95 Application models in control theory
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