Robust Model-Predictive Sliding Mode Control for Disturbed Linear Systems With Actuator Constraint and Measurement Noise

This manuscript presents a novel integrated model predictive sliding mode control (MPSMC) scheme for a class of distributed systems subjected to multiple simultaneous practical challenges, including noise and input/output constraints and incremental input limitations. While model predictive control (MPC) design, which predicts the performance of systems, can improve the control performance, it is not a robust control approach. To enhance the robustness of control performance, minimize the impact of constraints and perturbations, stabilize the closed-loop system, and achieve better trajectory tracking, a holistic control methodology that synergistically combines MPC and SMC is proposed for systems with input/output constraints, incremental input constraints, external disturbances, and measurement noise. The combination of MPC and SMC effectively stabilizes systems, achieving minimal overshoot, rapid rise time, and reduced settling time. The impact of external disturbances is mitigated using a filter. The closed-loop system stability analysis is subsequently conducted. Real-time implementation on a flexible-link manipulator and electric vehicle battery dynamic systems, subject to external disturbances, noise, saturation, incremental input constraints, and output constraints, highlights the efficiency and accuracy of the proposed approach.