论文标题
模型预测控制中的前瞻性持续激发
Forward-looking persistent excitation in model predictive control
论文作者
论文摘要
这项工作涉及自适应模型预测控制中同时调节和模型参数估计的问题。我们提出了一种自适应模型预测控制和条件，该控制能力仅通过时间到逐渐进入后退的预测范围来确保持续令人兴奋的闭环序列。早期的作品需要向后看并保留先前的回归剂数据。取而代之的是，我们提出了一个程序，以使平衡的离线生成持续令人兴奋的参考轨迹。通过新方法，我们证明了在自适应模型预测控制的影响下，非线性系统的指数收敛与递归的最小二乘识别符结合，尽管噪声有限，但遗忘了因子。在此阶段，结果是在状态和参数估计空间中的本地。
This work deals with the problem of simultaneous regulation and model parameter estimation in adaptive model predictive control. We propose an adaptive model predictive control and conditions which guarantee a persistently exciting closed loop sequence by only looking forward in time into the receding prediction horizon. Earlier works needed to look backwards and preserve prior regressor data. Instead, we present a procedure for the offline generation of a persistently exciting reference trajectory perturbing the equilibrium. With the new approach we demonstrate exponential convergence of nonlinear systems under the influence of the adaptive model predictive control combined with a recursive least squares identifier with forgetting factor despite bounded noise. The results are, at this stage, local in state and parameter-estimate space.
