论文标题
基于方案的随机MPC，用于具有不确定动态的系统
Scenario-based Stochastic MPC for systems with uncertain dynamics
论文作者
论文摘要
模型预测控制是具有输入和状态限制的系统的极有效的控制方法。模型预测控制性能在很大程度上取决于开环预测的准确性。对于有不确定性的系统，这反过来取决于有关模型属性和干扰不确定性的信息。在这里，我们对仅通过实现系统轨迹才能获得此类信息感兴趣。我们提出了一个基于一般方案的优化框架，用于当动态仅大致已知时，对受添加性干扰影响的线性系统的随机控制。主要的贡献是在基于确定性方案的有限层最佳控制问题上提供概率保证所需的概率保证所需的概率保证所需的概率保证。我们提供了所提出方法的样本复杂性的理论分析，并在简单的仿真示例中证明了其性能。由于提出的方法利用采样，因此不依赖于模型或干扰分布的明确知识，因此它适用于各种环境。
Model Predictive Control is an extremely effective control method for systems with input and state constraints. Model Predictive Control performance heavily depends on the accuracy of the open-loop prediction. For systems with uncertainty this in turn depends on the information that is available about the properties of the model and disturbance uncertainties. Here we are interested in situations where such information is only available through realizations of the system trajectories. We propose a general scenario-based optimization framework for stochastic control of a linear system affected by additive disturbance, when the dynamics are only approximately known. The main contribution is in the derivation of an upper bound on the number of scenarios required to provide probabilistic guarantees on the quality of the solution to the deterministic scenario-based finite horizon optimal control problem. We provide a theoretical analysis of the sample complexity of the proposed method and demonstrate its performance on a simple simulation example. Since the proposed approach leverages sampling, it does not rely on the explicit knowledge of the model or disturbance distributions, making it applicable in a wide variety of contexts.
