输入受限的非线性系统模型预测控制

Model predictive control for nonlinear systems with constraint inputs

基于模糊T-S模型对输入受限的非线性离散系统,提出了模型预测控制,导出了预测控制性能指标上界,将稳定性约束、输入约束变换成容易求解的线性矩阵不等式(LMIs)形式。采用了状态反馈控制器和并行补偿分布控制器(PDC),基于李雅普诺夫函数和线性矩阵不等式方法给出滚动时域优化的充分条件,证明了闭环系统的稳定性。仿真结果验证了所提方法的有效性。

Model predictive control is proposed for a class of nonlinear discrete - time systems with constraint inputs based on fuzzy T - S model. The upper bound of predictive cost is derived, and constraints on stability and inputs are transformed into linear matrix inequalities (LMIs) which are easy to be solved. The predictive controllers are state feedback controller and parallel distributed compensation (PDC) controller in the scheme. Sufficient conditions of moving horizon optimization are derived based on LMIs and Lyapunov funciton, stability of closed - loop systems is proved. The simulation results verify the effectiveness of the proposed method.