Abstract
In this paper, a radial basis neural network adaptive sliding mode controller (RBF−NN ASMC) for nonlinear electromechanical actuator systems is proposed. The radial basis function neural network (RBF−NN) control algorithm is used to compensate for the friction disturbance torque in the electromechanical actuator system. An adaptive law was used to adjust the weights of the neural network to achieve real−time compensation of friction. The sliding mode controller is designed to suppress the model uncertainty and external disturbance effects of the electromechanical actuator system. The stability of the RBF−NN ASMC is analyzed by Lyapunov’s stability theory, and the effectiveness of this method is verified by simulation. The results show that the control strategy not only has a better compensation effect on friction but also has better anti−interference ability, which makes the electromechanical actuator system have better steady−state and dynamic performance.
Highlights
The trajectory correction projectile is based on the original projectile and replaced with a guidance part, so that it has the ability to accurately strike
In order to verify the pros and cons of the radial basis function neural network (RBF−NN) adaptive sliding mode controller (ASMC) performance, this article starts from three situations to simulate and analyze the system
Considering the impact of friction disturbance on the electromechanical actuator (EMA) system, the SMC based on the reaching law and the RBF−NN ASMC is simulated respectively
Summary
The trajectory correction projectile is based on the original projectile and replaced with a guidance part, so that it has the ability to accurately strike. As the actuator of the trajectory correction projectile, the main function of the electromechanical actuator (EMA) system is to realize the tracking control of the commanded angular position. The tracking speed and tracking accuracy of the EMA system will have a vital influence on the mobility and accuracy of the trajectory correction projectile. Compared with hydraulic actuators and pneumatic actuators, electromechanical actuators are widely used because of their simple structure, convenient control, and low cost [1]. The EMA system is mainly composed of a controller, a driver, a Brushless DC (BLDC) motor, a ball screw reducer, a speed sensor, and a position sensor [2]. In the case of low speed, it will cause a dead zone, crawling, and tracking error to adversely affect the control performance of the system [3]. Due to the uncertainty of the model and the existence of external disturbances, it brings serious difficulties and challenges to the design of high−performance EMA system controllers
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