基于多层概率集的随机系统预测控制

针对具有Markov跳变特点的一类离散随机系统,研究了输入量概率约束下的状态反馈预测控制问题。采用多层概率集的概念和方法,给出了具有多个不同概率软约束下的预测控制器设计算法,在多步反馈律的控制下,系统状态以指定概率进入不同的椭圆内,保证了系统的稳定性,而且扩大了控制问题的可行范围,改善了系统性能。最后仿真实例证明了所提方法的有效性。

Predictive control for stochastic systems based on multi-layer probabilistic sets

Aiming at a class of discrete-time stochastic systems with Markov jump features, the state-feedback predictive control problem under probabilistic constraints of input variables is researched. On the basis of the concept and method of the multi-layer probabilistic sets, the predictive controller design algorithm with the soft constraints of different probabilities is presented. Under the control of the multi-step feedback laws, the system state moves to different ellipses with specified probabilities. The stability of the system is guaranteed, the feasible region of the control problem is enlarged, and the system performance is improved. Finally, a simulation example is given to prove the effectiveness of the proposed method.