Advancing Dynamic Output Feedback Switched Model Predictive Control for Discrete-Time Markov Jump Systems
提出一种动态输出反馈切换模型预测控制策略，用于提升离散时间马尔可夫跳变系统的鲁棒控制性能，并通过直流电机仿真验证了有效性。
This study proposes a novel dynamic output-feedback (DOF) switched model predictive control strategy to robustly enhance the control performance of discrete-time Markov jump systems (MJS). First, a Markov model is constructed to thoroughly describe the system’s switching characteristics. Then, to achieve dynamic parameter adjustment and a more flexible state feedback strategy, a DOF control strategy is proposed, and an appropriate feedback gain based on prediction errors is achieved. Additionally, to comprehensively evaluate the system’s robust performance, an energy-to-peak performance indicator is adopted in this article. Using convex optimization and Lyapunov stability theory, the solution is derived with the help of the linear matrix inequality (LMI) optimization method that considers the infinite horizon cost function. Finally, to demonstrate the performance of the proposed solution, a case study on a numerical simulation and a direct-current motor is conducted. The proposed strategy is fully verified through comparative experiments with other methods for controlling Markov jump systems.