Adaptive Control for a Class of Non-affine Nonlinear Systems via Two-Layer Neural Networks

Zhao, Tong; Sui, Shulin

doi:10.1109/wcica.2006.1712486

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…(38) illustrates the output trajectory and the control input attained by PID control when parametric uncertainty is added to the system. The comparison of figures (28) and (38) verifies that the obtained results by the proposed method are superior to those obtained by the PID controller.…”

Section: ) Nominal Casementioning

confidence: 63%

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A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

Sanci,

Uçak,

Günel

2023

IEEE Access

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In this study, a novel inverse optimal controller based on NARMA-L2 modelling technique and online least squares support vector regression (LSSVR) method has been proposed for nonlinear nonaffine systems. Firstly, the nonlinear autoregressive with exogenous inputs (NARX) model of the system is obtained using online LSSVR method, then this model is decomposed into NARMA-L2 submodels. Hence, the non-affine system model is converted to a nonlinear affine system model. The obtained NARMA-L2 submodels are used in computing the inverse optimal control law. Furthermore, the parameters of the inverse optimal controller have also been optimized online using the Levenberg-Marquadt algorithm. The performance of the proposed LSSVR based inverse optimal controller using NARMA-L2 model has been evaluated by simulations carried out on two benchmark systems, and the results show that the LSSVR based NARMA-L2 model and inverse optimal controller attain good modelling and control performances.

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Section: ) Nominal Casementioning

confidence: 63%

“…Tracking performance is illustrated in Fig. (28). The adaptation of the parameters of the inverse optimal controller with integrator and LSSV R N ARM A−L2 system identification parameters can be observed at Fig.…”

Section: ) Parametric Uncertainty Casementioning

confidence: 94%