Robust Model Predictive Control For Polytopic Uncertain Systems

In real control system, the model, which is used to describe the dynamics of controlled system, always has some uncertainty. In order to guarantee the specified performance, model uncertainty must be taken into account properly. Based on the existing theoretical results on model predictive control, this thesis is devoted to the development of the framework of robust model predictive control with guaranteed robust feasibility, robust stability and real-time applicability. To achieve this goal, the relevant theory and approaches of linear matrix inequalities (LMI) are employed in the research work. In this paper, we investigate these problems and subsequently hold some results as follows:According to the uncertain system with polytopic description, two improved methods of synthesizing infinite horizon min-max robust model predictive are proposed. Using the method of off-line design and the on-line synthesis, these algorithms achieves robust stability by solving the min-max optimization problem which can be formulated as a linear matrix inequality (LMI) problem. The effectiveness of the proposed approaches is experimented on the two-mass-spring system and three-tank system with uncertain dynamic behaviour respectively.