论文标题
具有持久激发条件的强大自适应模型预测控制
Robust adaptive model predictive control with persistent excitation conditions
论文作者
论文摘要
对于具有有界干扰和参数不确定性的约束线性系统，我们提出了一个具有在线参数估计的强大自适应模型预测控制策略。为设置会员参数识别方案引入了执行持续令人兴奋的闭环控制动作的约束。该算法需要在线解决方案的凸面程序，可以满足限制，并确保递归可行性和输入到州的稳定性。在稳定性，可及性和紧密干扰边界的假设下，几乎可以确保收敛到实际系统参数。
For constrained linear systems with bounded disturbances and parametric uncertainty, we propose a robust adaptive model predictive control strategy with online parameter estimation. Constraints enforcing persistently exciting closed loop control actions are introduced for a set-membership parameter identification scheme. The algorithm requires the online solution of a convex program, satisfies constraints robustly, and ensures recursive feasibility and input-to-state stability. Almost sure convergence to the actual system parameters is demonstrated under assumptions on stabilizability, reachability, and tight disturbance bounds.
