Robust Predictive Controller With Input Incremental Constraintsand Its Application

For systems with polytopic uncertainty, the problem of robust model predictive controller design is studied, especially the approach to tackling the input incremental constraints. In the industrial process, the physical properties of control devices will cause limits on the change rate of control law, which means control input incremental constraints. This category of constraints on the control input increment will cause the problem of asymmetric constraints, and lead to the previous method based on Linear Matrix Inequalities (LMI) to be conservative or failure. This is ignored in many previous researches. Besides, the presence of time-delay, which is always accompanied by material and energy conversion and transmission, usually causes the system instability and reduce the control performance.In this dissertation, we first deal with non-delay systems with control input incremental constraints by augmenting the two kinds of usually used system models with uncertainty. Augmented system maintains the same structural features and optimization problems as the original one, which is suitable for the existing research results, and the difficulty for LMI method due to the asymmetry is dealt with. Then time-delay is considered and an improved online algorithm of robust predictive controller is proposed based on the special structure. By making use of the gain scheduling, the parameter-dependent feedback law is designed based on the multi-step control set. This method improves the control performance. Finally, the air-conditioning is studied with dynamic characteristics such as large inertia, large time delay, multi-variable and nonlinear. The discrete-time model with polytopic uncertainty of the temperature control unit is established, based on which robust model predictive controller is designed according to the method proposed. The simulation results illustrate the advantages of the proposed methods.