Ultra-Local Model-based Optimal Predictive Control with Prescribed Performance for Mechatronic Systems Under Input Nonlinearities

To address the trajectory tracking issue for a class of Euler-Lagrange mechatronic systems in the presence of input nonlinearities, this paper proposes an ultra-local model (ULM)-based optimal prescribed performance predictive control (ULM-OPPPC). This method consists of ULM-based prescribed performance predictive controller (ULM-PPPC) and marine predators algorithm (MPA)-based parameter optimizer. Based on the ULM, the designed ULM-PPPC is composed of unknown disturbance estimation (UDE), prescribed performance function (PPF) and predictive control using feedback correction (FC). To decrease the difficulty of controller design and realize model-free control, the ULM is employed to approximate the complex mechatronic systems in a very short time sliding window. Then, a UDE is designed to estimate and eliminate the lumped disturbance which includes the system nonlinear dynamics, external disturbances, and input nonlinearities. A PPF is utilized to constrain the trajectory tracing error through error transformation. A predictive control with FC is proposed to stabilize the closed-loop system and converge its tracking error. However, the control gain of ULM-PPPC is a critical parameter that affects system stability and tracking accuracy. To obtain the optimum control performance, an MPA is used to search for optimal control gain parameter. Finally, the ULM-OPPPC method is completed. Afterward, the simulation results on 2-DOF robotic manipulator with three different input nonlinearities are gotten to demonstrate the effectiveness and superiority of the proposed control approaches.