Adaptive Predictive Control of a Class of Nonlinear Systems a Case Study

Lakhdari, Zakaria; Mokhtari, M.; Lecluse, Yves; Provost, J.

doi:10.1016/b978-0-08-042375-3.50034-2

Cited by 13 publications

(3 citation statements)

References 4 publications

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“…The third type of method involves using a local linearization approach for representing a nonlinear plant using an on-line estimated affine model (e.g., Lakhdari et al 1995) or an off-line estimated globally nonlinear and locally linear RBF-ARX model (Peng et al 2003(Peng et al , 2004(Peng et al , 2009) and then solving a quadratic programming problem on-line in order to obtain optimal control. In the former case, however, fast and accurate on-line estimation of a complicated model providing a good fit to a nonlinear process may be difficult in actual application.…”

Section: Mimo Rbf-arx Model-based Nonlinear Mpcmentioning

confidence: 99%

Advanced Autopilot Systems

Ohtsu

Peng

Kitagawa

2015

Time Series Modeling for Analysis and Control

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In the previous chapter, we presented three types of autopilot system based on the stationary linear AR model. However, actual sea conditions may change gradually or abruptly due to various factors, and in such a situation, we must consider a nonstationary time series. Furthermore, it may become necessary to consider the nonlinear response of the ship, which will be of particular importance to tracking control. In this chapter, we propose extensions of our statistical optimal controller based on the locally stationary AR model and the RBF-ARX model and develop a noise-adaptive autopilot and a path-tracking autopilot.

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Section: Mimo Rbf-arx Model-based Nonlinear Mpcmentioning

confidence: 99%

Advanced Autopilot Systems

Ohtsu

Peng

Kitagawa

2015

Time Series Modeling for Analysis and Control

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“…About the adaptive control and generalized predictive control problem of nonlinear system model based on dynamic nonlinear approximation, literature [8] is that nonlinear systems are dynamiclly approximated using quadratic interpolation polynomial, and a class of nonlinear systems which is not dependent on the controlled system model of adaptive control algorithm is given. Literature [9] make the nonlinear of system implicit in the model, the change of whose parameters with working point is nonlinear. Literature [10,11] which is based on dynamic nonlinear (2-order) approximation have implemented generalized predictive control of nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear adaptive control based on dynamic approximate any higher-order nonlinearization

Wang

et al. 2009

2009 Chinese Control and Decision Conference

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The adaptive control algorithm, the parameter adaptive recursive predicting and noise estimator unified form are presented for a class of nonlinear discrete-time systems with multiple time delays based on dynamic approximate any higher-order nonlinearization increment minimized recursive predicting model of NARMAX model(with time delays and noise increment any higher-order Hammerstein model).the adaptive control is employed for nonlinear systems with heavy multiple time delays. the simulation result for several typical nonlinear systems are given to demonstrate the correctness and effectiveness of approach proposed..

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“…Nevertheless, the identification experiment of many linear models which are valid only in each small region is not an easy work in practice. Besides, the NMPC designs based on an on-line estimated affine model representing a nonlinear plant, such as the neural network predictor (Liu, et al, 1998) and the quasi-linear autoregressive model (Lakhdari, et al, 1995) were also reported. However, fast and accurate online estimation of a complicated model providing a good fit to a nonlinear process may be difficult in real application.…”

mentioning

confidence: 99%

RBF-Arx Model-Based Robust MPC for Nonlinear Systems

Peng

Gui

Zou

et al. 2005

IFAC Proceedings Volumes

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An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems. First, the nonlinear system is identified off-line by a RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a combination of a local linearization model and a polytopic uncertain linear parameter-varying (LPV) model are built to approximate the present and the future system's nonlinear behavior respectively. Subsequently, based on the approximate models, a min-max robust MPC algorithm with input constraint is designed for the nonlinear systems. The closed loop stability of the MPC strategy is guaranteed by the use of parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities (LMIs). Simulation study to a NOx decomposition process illustrates the effectiveness of the modeling and robust MPC approaches proposed in this paper.

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Adaptive Predictive Control of a Class of Nonlinear Systems a Case Study

Cited by 13 publications

References 4 publications

Advanced Autopilot Systems

Advanced Autopilot Systems

Nonlinear adaptive control based on dynamic approximate any higher-order nonlinearization

RBF-Arx Model-Based Robust MPC for Nonlinear Systems

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