论文标题
基于样本的持续激发，自适应稳健的预测性控制
Adaptive robust predictive control with sample-based persistent excitation
论文作者
论文摘要
我们提出了一种强大的自适应模型预测控制（MPC）策略，并对具有未知参数和有界干扰的约束线性系统进行了在线估计。应用于预测轨迹的基于样本的测试用于通过在封闭环系统上强制执行激发条件的持续性来确保参数估计的收敛。控制法强大地满足限制，并保证可行性和投入到国家的稳定性。参数集估计值与实际系统参数矢量的收敛性可以在障碍界的到达和紧密性的条件下保证。
We propose a robust adaptive Model Predictive Control (MPC) strategy with online set-based estimation for constrained linear systems with unknown parameters and bounded disturbances. A sample-based test applied to predicted trajectories is used to ensure convergence of parameter estimates by enforcing a persistence of excitation condition on the closed loop system. The control law robustly satisfies constraints and has guarantees of feasibility and input-to-state stability. Convergence of parameter set estimates to the actual system parameter vector is guaranteed under conditions on reachability and tightness of disturbance bounds.
