Abstract
For the nonlinear discrete‐time system, higher‐order iterative learning control (HOILC) with optimal control gains based on evolutionary algorithm (EA) is developed in this paper. Since the updating actions are constituted by the tracking information from several previous iterations, the suitably designed HOILC schemes with appropriate control gains usually achieve fast convergence speed. To optimize the control gains in HOILC approach, EA is introduced. The encoding strategy, population initialization, and fitness function in EA are designed according to the HOILC characteristics. With the global optimization of EA, the optimal control gains of HOILC are selected adaptively so that the number of convergence iteration is reduced in ILC process. It is shown in simulation that the sum absolute error, total square error, and maximum absolute error of tracking in the proposed HOILC based on EA are convergent faster than those in conventional HOILC.
Highlights
In real applications such as robot manipulator systems [1,2,3,4,5] and flexible systems [6,7,8], there are many unmanned autonomous systems in complex environments. e exact mathematical model is hard to construct
Since the updating actions are constituted by the tracking information from several previous iterations, the tracking performance of suitably designed higher-order iterative learning control (ILC) (HOILC) is better than that of first-order ILC
E objective of this paper is to develop an evolutionary algorithm (EA)-HOILC method, which generates the control input from the tracking information of several previous iterations. e control gains are optimized by EA to reduce the number of convergence iteration
Summary
In real applications such as robot manipulator systems [1,2,3,4,5] and flexible systems [6,7,8], there are many unmanned autonomous systems in complex environments. e exact mathematical model is hard to construct. E exact mathematical model is hard to construct For these systems, iterative learning control (ILC) is proposed. First-order ILC, which generates the control input from tracking information at last iteration, is widely applied to dynamical systems for perfect tracking in a finite time interval [20,21,22,23,24,25,26]. To achieve faster convergence speed, higher-order ILC (HOILC) adopting the tracking information of many previous iterations to generate the current control input signal was proposed [27,28,29,30,31]. Since the updating actions are constituted by the tracking information from several previous iterations, the tracking performance of suitably designed HOILC is better than that of first-order ILC.
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