Robust Model Predictive Control Algorithm With Variable Feedback Gains for Output Tracking

Abstract

The varying-coefficient state-dependent autoregressive with exogenous inputs models are very useful in nonlinear system modeling and control. Considering that state feedback control strategy with a series of variable feedback gains provides more freedom than a constant feedback gain for the robust predictive controller design, this article proposes a robust model predictive control (RPC) algorithm with variable feedback gains for output tracking. In the proposed method, by defining input and output increment sequences of the system, two polytopic state-space models, in which the dynamic behavior of the system is wrapped, are constructed. Then, based on the polytopic state-space models, an RPC with variable feedback gains for output tracking is designed. To further reduce the conservatism, the parameter-dependent Lyapunov functions are constructed for the design of the variable feedback gains in the control strategy. The proposed RPC can expand the feasible region of the robust controller and improve the control performance. The simulation on a continuous stirred tank reactor verified the feasibility and efficacy of the proposed method.