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

Lakhdari, Zakaria; Mokhtari, M.; Léluse, Y.; Provost, J.

doi:10.1016/s1474-6670(17)45351-1

Cited by 13 publications

(7 citation statements)

References 6 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 ) 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%

Time Series Modeling for Analysis and Control

Ohtsu¹,

Peng²,

Kitagawa³

2015

SpringerBriefs in Statistics

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Section: Mimo Rbf-arx Model-based Nonlinear Mpcmentioning

confidence: 99%

Time Series Modeling for Analysis and Control

Ohtsu¹,

Peng²,

Kitagawa³

2015

SpringerBriefs in Statistics

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“…Notice that the state x(t|t), N(t) and the state-matrices [A t , B t ] in (11) can be obtained by (5), (8), (10), and (11) on the basis of the measured input/output data and the offline estimated MIMO RBF-ARX model (4). Furthermore, according to RBF-ARX model (4), (5) and model (12), it can be shown that the future state-matrices ½A tþjjt ; B tþjjt ðj P 1Þ in (12) belong to two convex polytopic sets, respectively, which are given by…”

Section: Nonlinear System Approximation By Mimo Rbf-arx Modelmentioning

confidence: 99%

“…Nevertheless, the identification experiment of many linear models that 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 quasi-linear autoregressive model [10], 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.…”

Section: Introductionmentioning

confidence: 99%

Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant

Peng

Kitagawa

et al. 2011

Applied Mathematical Modelling

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a b s t r a c tFor a class of smooth nonlinear multivariable systems whose working-points vary with time and the future working-points knowledge are unknown, a combination of a local linearization and a polytopic uncertain linear parameter-varying (LPV) state-space model is built to approximate the present and the future system's nonlinear behavior, respectively. The combination models are constructed on the basis of a matrix polynomial multi-input multi-output (MIMO) RBF-ARX model identified offline for representing the underlying nonlinear system. A min-max robust MPC strategy is designed to achieve the systems' output-tracking control based on the approximate models proposed. The closed loop stability of the MPC algorithm is guaranteed by the use of time-varying parameter-dependent Lyapunov function and the feasibility of the linear matrix inequalities (LMIs). The effectiveness of the modeling and control methods proposed in this paper is illustrated by a case study of a thermal power plant simulator.

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“…Therefore, in some cases, an alternative for MPC is to use a linear time-varying autoregressive model, but, as previously mentioned, the fast and accurate online estimation of time-varying coefficients is usually difficult. Reference [12] proposed a MPC scheme for the nonlinear system whose dynamics depend on the working point. This scheme used a quasi-linear autoregressive model with each coefficient an integral rational function of a state variable representing the working point state, and all the function's coefficients estimated online.…”

Section: Nonlinear Predictive Control Using Neuralmentioning

confidence: 99%

Nonlinear Predictive Control Using Neural Nets-Based Local Linearization ARX Model—Stability and Industrial Application

Peng

Nakano

Shioya³

2007

IEEE Trans. Contr. Syst. Technol.

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A Gaussian radial basis function (RBF) neural networks-based local linearization autoregressive with exogenous (ARX) model is utilized for describing the dynamics of a class of smooth nonlinear and nonstationary industrial processes. The dynamics of the underlying processes may be treated as the system operating-point-dependent time-varying locally-linear behavior. The RBF-ARX model is a pseudo-linear ARX model identified offline, and its functional coefficients are composed of the operating-point-dependent RBF neural networks. The RBF-ARX model-based predictive control (MPC) design to the nonlinear process is presented, and stability analysis of the nonlinear MPC under some conditions is discussed. Especially, the feasibility and effectiveness as well as the significant performance improvements of the nonlinear MPC design proposed is demonstrated with a real industrial application to the nitrogen oxide (NO x ) decomposition (de-NO x ) process in thermal power plants.

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

Cited by 13 publications

References 6 publications

Time Series Modeling for Analysis and Control

Time Series Modeling for Analysis and Control

Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant

Nonlinear Predictive Control Using Neural Nets-Based Local Linearization ARX Model—Stability and Industrial Application

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