论文标题
数据驱动的MPC的强大约束满意度
Robust Constraint Satisfaction in Data-Driven MPC
论文作者
论文摘要
我们提出了一个纯粹的数据驱动模型预测控制（MPC）方案，以控制未知的线性时间不变系统，并保证存在噪声数据的稳定性和约束满意度。该方案预测基于数据依赖性Hankel矩阵的未来轨迹，如果输入持续令人兴奋，则涵盖了完整的系统行为。本文通过包括合适的约束拧紧来确保闭环轨迹满足所需的时空输出约束，从而扩展了对数据驱动的MPC的先前工作。此外，我们提供估算程序来计算与可控性和可观察性相关的系统常数，这是实施约束收紧所必需的。通过数值示例说明了所提出方法的实用性。
We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data. The scheme predicts future trajectories based on data-dependent Hankel matrices, which span the full system behavior if the input is persistently exciting. This paper extends previous work on data-driven MPC by including a suitable constraint tightening which ensures that the closed-loop trajectory satisfies desired pointwise-in-time output constraints. Furthermore, we provide estimation procedures to compute system constants related to controllability and observability, which are required to implement the constraint tightening. The practicality of the proposed approach is illustrated via a numerical example.
