Abstract
This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.
Highlights
Nonlinearity and model uncertainty for practical nonlinear systems present great challenge for the controller design
Neural network, owing to their good generalization and nonlinear approximation ability, is widely used to identify model free nonlinear systems and exhibit higher performance compared to other identification methods. e reported neural network identifiers may be classified into two categories on the basis of the neural network structure used, namely, static neural network [3] and dynamic neural network [4, 5]. e main drawback of the static neural network is that the function approximation treatment makes it easy to fall into local optimum
We propose a new parametric neural network (PNN)-based indirect adaptive tracking control method for model free nonlinear systems. e notable contributions of the study are listed as follows: (1) A PNN identifier with a more parsimonious form is derived by extracting the parameter matrix of correlation weights multiplied by the correlation input and output state
Summary
Nonlinearity and model uncertainty for practical nonlinear systems present great challenge for the controller design. Popular learning rule such as backpropagation algorithm is used to design the online weight updating laws of dynamic neural networks, and suitable candidate of Lyapunov function is proposed to ensure the stability of the system [10, 11]. In order to solve the locally minimal convergence problem caused by backpropagation algorithms, a novel updating law of multilayer dynamic neural networks is proposed in [12], where the global asymptotic error stability is guaranteed by defining a Lyapunov function candidate based on quadratic functions of the weights and the estimation errors. We propose a new PNN-based indirect adaptive tracking control method for model free nonlinear systems. E simulation results of an AFS/DYC system demonstrate the improved performance of the proposed method than the conventional neural network-based adaptive tracking control method.
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