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Initial condition problem is crucial to a conventional iterative learning control (ILC) scheme, which steers the tracking error from arbitrary initial value to zero in the time steps equaling to the relative degree of the system undertaken. The implementation may be difficult, due to the practical limitation for the control amplitude. This paper presents an error‐tracking approach to discrete‐time...
In this paper, the problem of error-constrained adaptive iterative learning control is presented for a class of nonlinear systems which performs a given task over a finite time interval repeatedly, where the fuzzy system is used to approximate the unknown nonlinearity. Different from the output tracking control, the error tracking approach is used to deal with arbitrary initial conditions. An improved...
In this paper, the problem of adaptive iterative learning control using neural networks is addressed by an error tracking approach for systems with arbitrary initial states. The desired error trajectory is pre-specified at the design stage. It is shown that the tracking error is ensured to converge to an adjustable neighborhood of a pre-specified one. The performance improvement is made possible in...
This paper presents a characteristic modeling method for continuous/discrete time-varying nonlinear systems, where the model, the first-order time-varying differential equation, is a unified one. Learning identification algorithms are suggested for the purpose of parameter estimation, and the adaptive iterative learning control strategy is proposed for achieving the perfect tracking of the desired...
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