Output feedback Lyapunov‐based model predictive control with extended Kalman filter for nonlinear systems subject to random measurement delays and noises

Abstract This paper presents an output feedback Lyapunov‐based model predictive control (OFLMPC) algorithm for dealing with nonlinear systems with random measurement delays and noises. Measurement delays often occur in real control systems and may lead to degraded or even unstable control systems. To solve this problem, this paper considers a nonlinear system containing output measurement delays and introduces a dynamic model for describing the random measurement delays. An extended Kalman filter (EKF)‐based state estimator (SE) is developed to estimate the system states with measurement noises for OFLMPC controller design. An OFLMPC optimization problem is established to minimize the state error and control input of the system by designing a Lyapunov function and introducing weighting coefficients for the state error and control input. Based on the optimization problem, an integrated control strategy is proposed for nonlinear systems with random measurement delays. The effectiveness and performance advantages of the method are verified by numerical simulation and comparison under different complex conditions. Experimental results show that the algorithm can achieve good control performance in nonlinear systems and is robust to random measurement delays. This integrated control strategy has a wide application prospect, especially in the field of automation control, and is of great importance for systems with high real‐time requirements.