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Iterative Learning Stochastic MPC with Adaptive Constraint Tightening for Building HVAC Systems

机译：迭代学习随机MPC，采用建设HVAC系统的自适应约束

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Most of the existing stochastic model predictive control (SMPC) algorithms for systems subject to random disturbance are designed offline using the distribution information of the uncertainties. In this paper, we propose an iterative learning based MPC for systems subject to time varying stochastic constraints on states. Different from those existing offline design approaches, except for the boundedness, this algorithm does not require to know the distributions or statistics such as the covariances of the uncertainties and the parameters of the controllers are adjusted online using the observations of past state trajectories. By making use of the iterative nature of the process, pointwise in time stochastic constraints are enforced so that it can handle time-varying constraints. Under some proper assumptions, this iterative procedure is shown to be equivalent to a root-searching problem and stochastic approximation theory is applied to show that the empirical average converges to the prescribed expectation in probability. The proposed algorithm is applied to an HVAC control problem to show its effectiveness.
机译：对于随机干扰的系统的大多数随机模型预测控制（SMPC）算法使用不确定性的分布信息来脱机。在本文中，我们提出了一种基于迭代学习的MPC，用于在各种时随机随机限制的时间变化。与存在的离线设计方法不同，除了有界性之外，该算法不需要知道不确定性的协方差和控制器的参数等分布或统计数据在线调整过去状态轨迹的观察。通过利用该过程的迭代性质，逐时地执行随机限制，以便它可以处理时变的约束。在某种适当的假设下，该迭代程序被示出相当于根本搜索问题，并且应用随机近似理论，以表明经验性平均会聚到规定的概率期望。该算法应用于HVAC控制问题以显示其有效性。

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