In this paper, a quasi-Newton-type optimized iterative learning control （ILC） algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learnin...In this paper, a quasi-Newton-type optimized iterative learning control （ILC） algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.展开更多
针对传统比例-积分-微分(proportional integral differential,PID)控制器不能实现具有周期性特点的电网谐波电流无稳态误差跟踪的特点,提出了一种新的PID型迭代学习控制算法,同时建立了一种模糊规则并利用模糊推理对PID控制器的比例、...针对传统比例-积分-微分(proportional integral differential,PID)控制器不能实现具有周期性特点的电网谐波电流无稳态误差跟踪的特点,提出了一种新的PID型迭代学习控制算法,同时建立了一种模糊规则并利用模糊推理对PID控制器的比例、积分和微分系数进行在线修正,实现对系统的无差控制。在此基础上,为提高系统的响应速度,将PID迭代学习控制与滑模变结构控制有机结合,提出了复合型变结构控制方法。该方法具有响应速度快、控制精度高、易于实现的特点。仿真和实验结果证明了该控制方法的可行性和有效性,效果优于传统PID控制。展开更多
基金supported by the National Natural Science Foundation of China(Nos.F010114-60974140,61273135)
文摘In this paper, a quasi-Newton-type optimized iterative learning control （ILC） algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.
文摘针对传统比例-积分-微分(proportional integral differential,PID)控制器不能实现具有周期性特点的电网谐波电流无稳态误差跟踪的特点,提出了一种新的PID型迭代学习控制算法,同时建立了一种模糊规则并利用模糊推理对PID控制器的比例、积分和微分系数进行在线修正,实现对系统的无差控制。在此基础上,为提高系统的响应速度,将PID迭代学习控制与滑模变结构控制有机结合,提出了复合型变结构控制方法。该方法具有响应速度快、控制精度高、易于实现的特点。仿真和实验结果证明了该控制方法的可行性和有效性,效果优于传统PID控制。
