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.

中文翻译：

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具有前瞻性持续激励的模型预测控制

这项工作处理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，该控制和条件仅通过及时展望后退的预测范围来保证持续令人兴奋的闭环序列。早期的工作需要回顾并保留先前的回归数据。相反，我们提出了一种离线生成扰动平衡的持续刺激参考轨迹的程序。使用新方法，我们证明了在自适应模型预测控制与具有遗忘因子的递归最小二乘标识符的影响下非线性系统的指数收敛，尽管有界噪声。在这个阶段，结果在状态和参数估计空间中是局部的。