Comparison of control strategies for hysteresis attenuation in electromechanical actuators subject to dispersion. (January 2023)

• Record Type: Journal Article
• Authors: Louis, Amigues; Valérie, Pommier-Budinger; Joël, Bordeneuve-Guibé
• Is Part Of: Control engineering practice. Volume 130(2023)
• Publication Date: 2023-01
• Subjects: Hysteresis -- Neural network -- Parameter uncertainty -- Internal parameter estimation -- Sliding mode control; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2022.105348
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: This paper addresses the difficulties of designing highly efficient robust controllers for a class of systems exhibiting high hysteresis with parameters dispersion that limits control accuracy and performance homogeneity over the parametric uncertainties range. Two control strategies to solve the problem are assessed. First, a Reference Model Sliding Mode Control (RMSMC) feedback controller known to be robust to parametric uncertainty is designed to compensate hysteresis, regardless of the hysteresis quantity. Secondly, a strategy based on a feedforward controller with a Neural Network inverse model and a PID feedback controller is proposed. In this case, hysteresis dispersion is addressed through the integration of a backlash estimator for computing the Neural Network inverse model. The control strategies are implemented for position control of a Limited-Angle Torque Motor (LATM) exhibiting uncertain hysteresis. Experimental tests demonstrated the very good accuracy and robustness of the Neural Network inverse model and the PID controller for position tracking when the LATM is subject to dispersion and the benefits of the Reference Model Sliding Mode Control (RMSMC) feedback controller for the rejection of external disturbances.