Hybrid Model Predictive and Sliding Mode Control for Robust Operation of Electric Drives Under Dynamic Load Conditions

Abstract

The high efficiency, reliability and the ability to control the speed with great precision have pushed the wide application of electric drive systems in industrial automation, robotics, electric vehicles, and renewable energy applications. Nevertheless, stabilisation of functioning in dynamic load conditions is still a serious problem since the electric drives have the nonlinear nature and are affected by ambiguity of the parameters and external interrupts. Traditional proportionate-integral (PI) based controllers tend to lose performance in response to sudden changes in load resulting in higher overshoot, slower response and worse accuracy in regulating speed. The paper is a proposal of a hybrid control architecture to combine Model Predictive Control (MPC) with Sliding Mode Control (SMC) to enhance the robustness and dynamics of electric drive systems. The MPC element forecasts system response in the future based on a mathematical model of the motor drive and attempts to optimise the control input variables by a cost-function minimization algorithm whereas the SMC element increases the system robustness by correcting disturbances and parameter variations. The hybrid controller is proposed on the model of a permanent magnet synchronous motor (PMSM) drive and tested in terms of dynamic load disturbance with MATLAB/Simulink simulations. The performance measures such as rise time, settling time, overshoot, and steady-state speed error are compared to the traditional PI, MPC, and SMC controllers. The extensive simulation findings indicate that hybrid MPC-SMC controller can achieve quicker transient response, better disturbance rejection and better speed regulation, and it is thus efficient in high-performing electric drive works in changing load environments at a high rate.