On Design of Stochastic Model Predictive Control Algorithm Based on Multi-layer Probabilistic Sets
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摘要: 考虑具有乘型不确定性的离散随机系统约束控制问题, 设计了一种基于多层概率集的随机预测控制算法. 多层概率集描述了状态在多步反馈控制律下的一系列不同概率的分布区域, 因此能够同时保证多个不同概率要求的软约束. 通过动态优化多步反馈律, 算法具有较大的可行范围. 之后设计的简化算法在降低计算负担的同时保证了算法的可行范围.Abstract: This paper considers the constrained control problem of discrete-time stochastic systems with multiplicative uncertainty. We design a stochastic model predictive control algorithm based on the formulation of multi-layer probabilistic sets. Multi-layer probabilistic sets describe the distribution regions where the system evolves with different probabilities under multi-step feedback laws, thus enabling the satisfaction of soft constraints at different probabilistic levels. This algorithm has a large applicable region by dynamically optimizing multi-step feedback laws. Furthermore, we propose a simplified algorithm that reduces the computational burden with a guarantee of its applicable region.
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