
doi: 10.1002/asjc.403
AbstractA new adaptive learning control approach is proposed for a class of first‐order nonlinear systems with two unknown time‐varying parameters and an unknown time‐varying delay. By reconstructing the system equation, all unknown time‐varying terms, including the time‐varying delay, are combined into an unknown periodic time‐varying vector, which is estimated by a periodic adaptive mechanism. By constructing a Lyapunov–Krasovskii‐like composite energy function (CEF), we prove the boundedness of all signals and the convergence of the tracking error. The results are extended to two classes of high‐order nonlinear systems with mixed parameters. Three simulation examples are provided to illustrate the effectiveness of the control algorithms proposed in this paper.Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society
adaptive learning control, nonlinearly parameterized uncertainties, Adaptive control/observation systems, Learning and adaptive systems in artificial intelligence, Nonlinear systems in control theory, time-varying delays, nonlinear systems, Control/observation systems governed by ordinary differential equations, composite energy function
adaptive learning control, nonlinearly parameterized uncertainties, Adaptive control/observation systems, Learning and adaptive systems in artificial intelligence, Nonlinear systems in control theory, time-varying delays, nonlinear systems, Control/observation systems governed by ordinary differential equations, composite energy function
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 23 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
