This paper investigates a dynamic output feedback adaptive integral sliding mode predictive control (AISMPC) approach based on a dual-loop Kalman filter (DLKF) for linear discrete-time systems. The considered system class is subjected to bounded disturbances and noise, with constraints on both the inputs and states. The DLKF introduces one loop to estimate the states and another loop to estimate the disturbances. The dynamic output feedback AISMPC consists of two parts: one component is an output feedback adaptive integral sliding mode control (AISMC) to handle disturbances, and the other component is an output feedback robust model predictive control (RMPC) to achieve optimal control with constraints. It is proved that the closed-loop system is stabilized in a neighborhood of the origin. Finally, numerical simulation tests are carried out to validate the effectiveness of the proposed approach.
Xiao et al. (Thu,) studied this question.