基于2维性能参考模型的2维模型预测迭代学习控制策略

将迭代学习控制(Iterative learning control, ILC)系统看作一类具有2维动态特性的控制系统,根据模型预测控制(Model predictive control, MPC)和性能参考模型控制思想, 提出了一种基于2维性能参考模型的2维模型预测迭代学习控制系统设计方案.在该控制系统设计方案中,可以通过选择适当的2 维性能参考模型来构造2 维动态变化的设定值信号和预测控制信号,从而引导迭代学习控制系统收敛到合理的控制性能,并有效避 免系统性能收敛过程中控制输入可能发生的剧烈波动.通过对控制系统的结构分析可知,所得的迭代学习控制器本质上是由沿时 间指标的参考模型预测控制器和沿周期指标的迭代学习控制器组成,闭环系统的收敛性等价于一个2维滤波系统的稳定性.数值仿 真结果证明了该设计方案的有效性和鲁棒性.

Two-dimensional Model Predictive Iterative Learning Control Scheme Based on a Two-dimensional Performance Model

By representing an iterative learning control (ILC) system as a two-dimensional system and using the philosophy of model predictive control (MPC) and performance model reference control, a two-dimensional performance model based model predictive iterative learning control scheme is proposed in this paper. Through the design of two-dimensional dynamics of the performance model to generate more proper reference trajectories and predictive control signals for each cycle, the convergence pattern of the iterative learning control system can be guided to avoid the issue of possibly violent oscillation of input signal. The structure analysis indicates that the resulted control is composed of the time-wise performance model based model predictive control and cycle-wise iterative learning control, and that the convergence of the closed loop control system is equivalent to the stability of a two-dimensional filter. Numerical simulations illustrate the effectiveness and robustness of the proposed control scheme.