Adaptive RBF Neural Network Sliding Mode Control for a DEAP Linear Actuator

doi:10.23940/ijpe.17.04.p7.400408

Abstract

Abstract:

Dielectric electro-active polymer (DEAP) is a new smart material named “artificial muscles”, which has a remarkable potential in the field of biomimetic robots. However, hysteresis nonlinearity widely exists in this material, which will reduce the performance of tracking precision and system stability. To deal with this situation, a radial basis function (RBF) neural network combined with sliding mode control algorithm is presented for a second-order DEAP linear actuator. Firstly, an inverse hysteresis operator based on Prandtl-Ishlinskii (P-I) model is used to eliminate hysteresis behavior. Secondly, an adaptive RBF neural network sliding mode controller is designed to obtain high tracking accuracy and keep system stability. The proposed algorithm makes the tracking error converge to zero and keeps the system globally stable in the case of external disturbances and parameter variations. Simulation results demonstrate that the proposed controller has the superiority to a pure sliding mode controller.

Submitted on January 14, 2017; Revised on May 14, 2017; Accepted on June 17, 2017
References: 18

Dehui Qiu, Yu Chen, and Yuan Li. Adaptive RBF Neural Network Sliding Mode Control for a DEAP Linear Actuator [J]. Int J Performability Eng, 2017, 13(4): 400-408.

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References 0

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