论文标题
基于学习的分布强大的模型模型对马尔可夫开关系统的预测性控制具有保证的稳定性和递归可行性
Learning-Based Distributionally Robust Model Predictive Control of Markovian Switching Systems with Guaranteed Stability and Recursive Feasibility
论文作者
论文摘要
我们为具有未知开关概率的机会受到限制的Markovian开关系统提供了数据驱动的模型预测控制方案。使用基础马尔可夫链的样本，估计了过渡概率的歧义集，其中包括具有很高概率的真实条件概率分布。这些集合在线更新，并用于制定时间变化的，规避风险的最佳控制问题。我们证明了由此产生的MPC方案的递归可行性，并表明在每个时间步骤中，原始的机会限制仍然保持满足。此外，我们表明，在足够的置信度降低下，由此产生的MPC方案使闭环系统均值相对于真正但不知名的分布稳定，同时保持不如完全健壮的方法。
We present a data-driven model predictive control scheme for chance-constrained Markovian switching systems with unknown switching probabilities. Using samples of the underlying Markov chain, ambiguity sets of transition probabilities are estimated which include the true conditional probability distributions with high probability. These sets are updated online and used to formulate a time-varying, risk-averse optimal control problem. We prove recursive feasibility of the resulting MPC scheme and show that the original chance constraints remain satisfied at every time step. Furthermore, we show that under sufficient decrease of the confidence levels, the resulting MPC scheme renders the closed-loop system mean-square stable with respect to the true-but-unknown distributions, while remaining less conservative than a fully robust approach.
