A learning controller based on a nonlinear ARX inverse model identified by a genetic algorithm

The inverse dynamics approach has been widely utilized in the control problem of various practical non-linear systems in recent years. This paper demonstrates a feedforward-feedback controller scheme of a non-linear plant whose dynamics are unknown and uncertain. The feedforward controller, an inverse NARX model (non-linear autoregressive model with exogenous inputs), provides only coarse control, whereas the feedback controller is used to handle unmodelled dynamics and disturbance. The inverse NARX model is derived by inverting the forward NARX model identified using genetic algorithms. A parallel-type NARX model whose outputs of the identification model are fed back into the identification model is adopted in the identification procedure to include the stability examination numerically. Both experimental and simulation results demonstrate that the proposed controller provides very good performance in the problems of input estimation and output tracking.

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Identification of Nonlinear Polynomial Models by Using the Genetic Algorithm

Yang

Fujimoto

Kumamaru

2000

IEEJ Trans.EIS

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, Non-member (Kyushu Institute of Technology), Katsumi Kumamaru, Non-member (Mitsubishi Space Software)In the last two decades, linear-in-parameter nonlinear polynomial models for NARX (Nonlinear Auto Regressive with eXogenous input) systems have received considerable attention. The keypoint of nonlinear polynomial model identification is how to select a set of significant terms employed to approximate the NARX system under study, from a large number of candidates. To this end, the orthogonal least squares method which is a local search procedure, and the genetic algorithm approach which has a high potential for global optimization have been proposed in the literature. However, it is considered that the methods re ported so far in the literature still lack potential to identify the nonlinear polynomial models with relatively high-order. This limits the applicability of the nonlinear polynomial models to the real complex nonlinear systems. Motivated by this fact, in this paper, a new genetic algorithm approach to nonlinear polynomial model identification is proposed. Our contribution in this paper is to introduce a novel hierarchical encoding technique which is considered to be suitable to the structure of the nonlinear polynomial models. Simulation and application results are also included to verify the efficiency of the proposed identification algorithm.

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A learning controller based on a nonlinear ARX inverse model identified by a genetic algorithm

Cited by 3 publications

References 15 publications

A Genetic Algorithm Approach to Identification of Nonlinear Polynomial Models

A Genetic Algorithm Approach to Identification of Nonlinear Polynomial Models

Inversion control of non-linear systems with an inverse NARX model identified using genetic algorithms

Identification of Nonlinear Polynomial Models by Using the Genetic Algorithm

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