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This paper combines the fractional-order iterative learning control (FOILC) and the high-gain universal adaptive stabilizer (UAS) into a high-gain adaptive FOILC scheme, which is a feedforward-plus-feedback one. Allowing for the commonality of FOILC and UAS, the proposed scheme requires only the structural information of fractional-order systems, and the convergence condition remains the same with...
This paper reveals a previously ignored problem for fractional order iterative learning control (FOILC) that the fractional order system may have different behaviors when it is initialized differently. To implement a novel scheme of FOILC for this so-called initialized fractional order system, a Dα-type control law is applied, and the convergence condition is derived by using the short memory principle...
The fractional-order Hammerstein system is a cascading system composed of a static nonlinearity followed by a fractional-order linear dynamics, which is a typical nonlinear system satisfying the local Lipschitz condition, and exhibits quasi-linear properties. This paper combines the fractional-order iterative learning control (FOILC) and the fractional-order iterative learning identification (FOILI),...
In this paper, a practical Identification strategy is applied to the optimal design of fractional-order iterative learning control (FOILC). The initialized fractional-order gray-box system with commensurate order or non-commensurate order is identified by using the fractional-order iterative learning Identification and the least square or instrumental variable method. The optimal Dα-type FOILC is...
In this paper, we discuss in time domain the convergence of the iterative process for fractional-order nonlinear systems. A linear adaptive generalized fractional-order iterative learning control scheme is derived, which guarantees the convergence of the algorithm without the knowledge of the system order. The implementation of generalized fractional-order iterative learning controllers can be achieved...
In this paper, the approximate controllability of stochastic integrodifferential systems with nonlocal conditions is considered. Assuming the conditions for the approximate controllability of these linear systems, the sufficient conditions for the approximate controllability of these associated nonlinear stochastic integrodifferential systems in Hilbert spaces is obtained by using B. C. Dhage's multivalued...
Supervised classification of fully polarimetric SAR image using neural network is a common method nowadays. As an effective learning method of neural network, BP algorithm is the most widespread one in the neural network algorithms. However, BP network is easy to fall into local extremum and exists shortcomings such as the slow training process. To this end, this paper presents a method of supervised...
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