In this paper, we study an adaptive method to select online the pole value for a Laguerre scheme in Model Predictive Control (MPC) that yields high performance. It has been observed that, while still using a small numbers of decision variables, the location of the pole affects the closed-loop behaviour significantly. In the present paper, an adaptive algorithm is developed to systematically improve the closed-loop performance of the system as well as the volume of the feasible region and robust feasible region in the case of using a small numbers of decision variables. In order to do this, a method to select a pole value that yields high performance for the initial condition of the system is proposed. The method generates a lookup table of the high-performance pole value obtained through off-line computations. Then, the table is used to assign the pole in the online process. Closed-loop stability for the scheme is established using sub-optimality arguments. Simulations illustrate the suggested method's effectiveness to achieve a balance between performance, optimality, and computational load.

在本文中，我们研究了一种自适应方法，用于在线选择模型预测控制 (MPC) 中的 Laguerre 方案的极值，该方法可产生高性能。已经观察到，虽然仍然使用少量决策变量，但极点的位置会显着影响闭环行为。本文开发了一种自适应算法，在使用少量决策变量的情况下，系统地提高系统的闭环性能以及可行域和鲁棒可行域的体积。为了做到这一点，提出了一种选择极值的方法，该极值对系统的初始条件产生高性能。该方法生成通过离线计算获得的高性能极值的查找表。然后，该表用于在线过程中分配极点。该方案的闭环稳定性是使用次优参数建立的。仿真说明了所建议的方法在实现性能、最优性和计算负载之间的平衡方面的有效性。