A new adaptive sliding mode controller based on the RBF neural network for an electro-hydraulic servo system. (October 2022) - Feng, Hao; Song, Qianyu; Ma, Shoulei; Ma, Wei; Yin, Chenbo; Cao, Donghui; Yu, Hongfu
Abstract: Accuracy and robust trajectory tracking for electro-hydraulic servo systems in the presence of load disturbances and model uncertainties are of great importance in many fields. In this work, a new adaptive sliding mode control method based on the RBF neural networks (SMC–RBF) is proposed to improve the performances of a robotic excavator. Model uncertainties and load disturbances of the electro-hydraulic servo system are approximated and compensated using the RBF neural networks. Adaptive mechanisms are designed to adjust the connection weights of the RBF neural networks in real time to guarantee the stability. A nonlinear term is introduced into the sliding mode to design an adaptive terminal sliding mode control structure to improve dynamic performances and the convergence speed. Moreover, a sliding mode chattering reduction method is proposed to suppress the chattering phenomenon. Three types of step, ramp and sine signals are used as the simulation reference trajectories to compare different controllers on a co-simulation platform. Experiments with leveling and triangle conditions are presented on a robotic excavator. Results show that the proposed SMC–RBF controller is superior to existing proportional integral derivative (PID) and sliding mode controller (SMC) in terms of tracking accuracy and disturbance rejection. Highlights: A new adaptive sliding mode controller based on the RBF neural network is proposed for improving trajectory tracking performances. System models and nonlinear factors of the electro-hydraulic servo system are described in detail. A nonlinear term and a chattering reduction method are synthesized into the control law. A co-simulation platform is built to compare different controllers. Effectiveness of the proposed controller is validated by simulation and experiment.