Adaptive Model Predictive Control for a TORA System under Initial Axial and angular Position Dispersions

Abstract

A nonlinear translational oscillator with a rotational actuator system known as TORA is considered in this paper. An online algorithm that uses to stabilize the TORA system targeting of the desired translational and rotational positions is presented. A steady-state condition which assumes a cart position, pendulum angle, cart velocity, and pendulum angular velocity are constant was used to create the TORA reference trajectory. An adaptive model predictive control method was utilized to modulate the motor torque while the discrete plant model and operating conditions were changed at each time step. Five hundred trials using a Monte-Carlo method were simulated to perform the proposed algorithm. Numerical results illustrated that the proposed algorithm is successfully able to stabilize the system and to accurately track the desired reference despite the wide range of perturbations in the initial cart and pendulum conditions.