2013 13th International Conference on Control, Automation and Systems (ICCAS 2013) 2013
DOI: 10.1109/iccas.2013.6703898
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Abstract: Ahstract-To deal with the iterative learning control problem for more general class of uncertain nonlinear systems using only output measurement, an observer based adaptive iterative learning control strategy using filtered fuzzy neural network was proposed in this paper. A model reference control technique is firstly presented to derive a state error observer for state estimation. A mixed time-domain and s-domain representation is then used to develop an error model as a relative degree one stable system with… Show more

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Cited by 2 publications
(2 citation statements)
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References 7 publications
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“…Over the past decades, there has been a considerable development in various observer design methodologies using different approaches [28][29][30][31][32][33][34][35]. Although so many results have been developed, only a few results are available from the point of AILC [36][37][38][39][40][41][42]. How to design an AILC for nonlinear systems using only output measurement is an interesting and challenging issue.…”
Section: Introductionmentioning
confidence: 99%
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“…Over the past decades, there has been a considerable development in various observer design methodologies using different approaches [28][29][30][31][32][33][34][35]. Although so many results have been developed, only a few results are available from the point of AILC [36][37][38][39][40][41][42]. How to design an AILC for nonlinear systems using only output measurement is an interesting and challenging issue.…”
Section: Introductionmentioning
confidence: 99%
“…Wang and Chien introduced an error observer to design an iterative learning controller for robotic systems, where a robust learning component using a filtered fuzzy neural network was presented to solve the problem of unknown nonlinearities [38]. Subsequently, the results in [38] were extended to SISO nonlinear system [39], MIMO nonlinear systems [40], and MIMO nonlinear systems with delayed output [41]. Chen et al extended the result in [36] and proposed an observer-based AILC for nonlinear systems with unknown time-varying parametric uncertainties and the delayed output, where the Lyapunov-Krasovskii-like composite energy function was constructed to prove the boundedness of all closed-loop signals and the convergence of output tracking error [42].…”
Section: Introductionmentioning
confidence: 99%