Abstract
In this paper, a novel neural network-based error-track iterative learning control scheme is proposed to tackle trajectory tracking problem for tank gun control systems. Firstly, the system modeling for tank gun control systems is introduced as a preparation of controller design. Then, the reference error trajectory is constructed to deal with the nonzero initial error of iterative learning control. The adaptive iterative learning controller for tank gun control systems is designed by using Lyapunov approach. Adaptive learning neural network is adopted to approximate nonlinear uncertainties, with robust control technique being used compensate the approximation error and external disturbances. As the iteration number increases, the system error can follow the desired error trajectory over the whole time interval, which makes the system state accurately track the reference error trajectory during the predetermined part time interval. Numerical simulations demonstrate the effectiveness of the proposed iterative learning control scheme.
Highlights
Iterative learning control (ILC) is a proper control technique for those uncertain systems which repetitively operating over finite time intervals [1]
The adaptive iterative learning controller is designed by using Lyapunov approach, with adaptive learning neural network being constructed to approximate uncertainties in tank gun control systems (TGCSs)
With the corresponding desired error trajectory being constructed, an error-tracking strategy is utilized to solve the initial problem of ILC
Summary
Iterative learning control (ILC) is a proper control technique for those uncertain systems which repetitively operating over finite time intervals [1]. Q. Yang et al.: Neural Network-Based Error-Tracking ILC for TGCSs With Arbitrary Initial States control [17], [18] and adaptive robust control [19]. It is necessary to carry out further research in adaptive iterative learning controller design for TGCSs with both nonzero initial velocity error and nonzero initial acceleration error. The adaptive iterative learning controller is designed by using Lyapunov approach, with adaptive learning neural network being constructed to approximate uncertainties in TGCSs. Compared to the existing results, the main contributions of this work mainly lie in the several fields as follows:. (2) An adaptive learning radial basis function (RBF) neural network is used to approximate uncertainties in tank gun servo systems. In the rest of this paper, φ(Xk ) is abbreviated as φk , and arguments are sometimes omitted while no confusion occurs
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