NN AILC of Nonlinear Time-Delay Systems

Jianming Wei (), Hong Wang () and Fang Liu ()
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Jianming Wei: Naval University of Engineering, College of Weapons Engineering
Hong Wang: Naval Aviation University
Fang Liu: Naval University of Engineering, College of Weapons Engineering

Chapter Chapter 3 in Iterative Learning Control for Nonlinear Time-Delay System, 2022, pp 53-79 from Springer

Abstract: Abstract This chapter presents an adaptive iterative learning control (AILC) scheme for a class of nonlinear systems with unknown time-varying delays and unknown input dead-zone. A novel nonlinear form of dead-zone nonlinearity is presented. The assumption of identical initial condition for iterative learning control (ILC) is removed by introducing boundary layer function. The uncertainties with time-varying delays are compensated for by using appropriate Lyapunov-Krasovskii functional and Young0s inequality. Radial basis function neural networks are used to model the time-varying uncertainties. The hyperbolic tangent function is employed to avoid the problem of singularity. According to the property of hyperbolic tangent function, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closed-loop signals bounded. Finally, simulation examples are presented to verify the effectiveness of the proposed approach.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-6317-9_3

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DOI: 10.1007/978-981-19-6317-9_3

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