论文标题
基于自我调整管的模型预测控制
Self-Tuning Tube-based Model Predictive Control
论文作者
论文摘要
我们提出了基于自我调整管的模型预测控制（STT-MPC），这是一种基于最小二乘估计仪和多型管的不确定线性系统的自适应鲁棒控制算法。我们的算法利用浓度会导致将系统的不确定性与规定的信心联系起来，并保证该集合的强大约束满意度以及递归可行性和投入到国家稳定性。在不损害算法的渐近性能或提高其计算复杂性的情况下，确保了激发的持久性。我们使用数值实验证明了算法的性能。
We present Self-Tuning Tube-based Model Predictive Control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes. Our algorithm leverages concentration results to bound the system uncertainty set with prescribed confidence, and guarantees robust constraint satisfaction for this set, along with recursive feasibility and input-to-state stability. Persistence of excitation is ensured without compromising the algorithm's asymptotic performance or increasing its computational complexity. We demonstrate the performance of our algorithm using numerical experiments.
