Robust Model Predictive Control for Linear Discrete‐Time System With Saturated Inputs and Randomly Occurring Uncertainties

Wang, Jingyuan; Song, Yan; Zhang, Sunjie; Liu, Shuai; Dobaie, Abdullah M.

doi:10.1002/asjc.1565

Cited by 19 publications

(15 citation statements)

References 33 publications

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“…In this paper, ±30% unknown model uncertainties are considered. The unknown model uncertainties ΔA i and ΔB i are random matrixes and of the form [36,[59][60][61][62]:…”

Section: Simulation Resultsmentioning

confidence: 99%

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

Wang

2019

Asian Journal of Control

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A boiler‐turbine unit is a primary module for coal‐fired power plants, and an effective automatic control system is needed for the boiler‐turbine unit to track the load changes with the drum water level kept within an acceptable range. The aim of this paper is to develop a nonlinear tracking controller for the Bell‐Åström boiler‐turbine unit. A Takagi‐Sugeno fuzzy control system is introduced for the nonlinear modeling of the Bell‐Åström boiler‐turbine unit. Based on the Takagi‐Sugeno fuzzy models, a nonlinear tracking controller is developed, and the proposed control law is comprised of a state‐feedforward term and a state‐feedback term. The stability of the closed‐loop control system is analyzed on the basis of Lyapunov stability theory via the linear matrix inequality approach and Schur complement. Moreover, model uncertainties are also considered, and it is proved that with the proposed control law the tracking error converges to zero. To assess the performance of the proposed nonlinear state‐feedback state‐feedforward control strategy, a nonlinear model predictive control strategy and a linear strategy are presented as comparisons. The effectiveness and the advantages of the proposed nonlinear state‐feedback state‐feedforward control strategy are demonstrated by simulations.

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“…In this paper, ±30% unknown model uncertainties are considered. The unknown model uncertainties ΔA i and ΔB i are random matrixes and of the form [36,[59][60][61][62]:…”

Section: Simulation Resultsmentioning

confidence: 99%

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

Wang

2019

Asian Journal of Control

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“…It is, therefore, vitally important to develop some new MPC strategies to guarantee the system robustness against the parameter uncertainties, which readily gives rise to the robust MPC (RMPC) . In recent years, RMPC has become an attractive research topic and many efforts have been made …”

Section: Introductionmentioning

confidence: 99%

“…[10][11][12][13][14][15][16][17] In recent years, RMPC has become an attractive research topic and many efforts have been made. [18][19][20][21][22][23][24][25][26] In the practical engineering, due mainly to the physical limitations of system components, the obstacles via data transmission often stem from the state/actuator saturation, which is often characterized by a certain bounded set. Nonlinearities, which result from the saturation, can rigorously restrict the amount of the transmission data and it might deteriorate the system performance or, even cause the system instability.…”

Section: Introductionmentioning

confidence: 99%

Robust model predictive control for polytopic uncertain systems with state saturation nonlinearities under Round‐Robin protocol

Wang

Song

Wei

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper is concerned with the robust model predictive control (RMPC) problem for polytopic uncertain systems with state saturation nonlinearities under the Round‐Robin (RR) protocol. With respect to the practical application, one of the most commonly encountered obstacles that stem from the physical limitation of system components, ie, state saturation, is adequately taken into consideration. In order to reduce the network transmission burden and improve the utilization of the network from the controller nodes to the actuator node, a so‐called RR protocol is employed to orchestrate the data transmission order. At each transmission instant, only one controller node that obtains the priority is accessible to the shared communication network. Our aim of the underlying problem is to design a set of controllers in the framework of RMPC such that the closed‐loop system is asymptotically stable. By taking the influence of the RR protocol and the state saturation precisely into account, some sufficient criteria are established in terms of the token‐dependent Lyapunov‐like approach. Then, an online optimization problem subjected to some matrix inequality constraints is provided, and the desired controllers can be obtained by solving the certain upper bound of the objective addressed. Finally, a distillation process example is provided to illustrate the effectiveness of the proposed RMPC approach.

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“…Several works have proposed robust constrained model predictive control, which are designed based on linear matrix inequality (LMI) [4][5][6][7][8]. Among different approaches to design a robust model predictive control using LMI, the following two categories are most popular.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control of Nonlinear Discrete Time Systems with Guaranteed Stability

Shamaghdari

Haeri

2018

Asian Journal of Control

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This paper presents the design of a new robust model predictive control algorithm for nonlinear systems represented by a linear model with unstructured uncertainty. The linear model is obtained by linearizing the nonlinear system at an operating point and the difference between the nonlinear and linear model is considered as a Lipschitz nonlinear function. The controller is designed for the linear model, which fulfills the stabilization condition for the nonlinear term. Unlike previous studies that have not considered a valid Lipschitz matrix of nonlinear term in the design process, we propose an algorithm in this paper in which it is considered. Therefore, the closed loop stability of the nonlinear system is guaranteed. A novel SOS optimization problem to determine design parameters is introduced, which leads to improved closed‐loop performance in comparison to a trial and error tuning procedure. Furthermore, an algorithm is presented to enlarge the region of attraction for the nonlinear closed‐loop system. Stability is improved by checking some additional conditions if which the system may be unstable if not considered. The validity of the proposed algorithm is confirmed by examples.

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Robust Model Predictive Control for Linear Discrete‐Time System With Saturated Inputs and Randomly Occurring Uncertainties

Cited by 19 publications

References 33 publications

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

Robust model predictive control for polytopic uncertain systems with state saturation nonlinearities under Round‐Robin protocol

Model Predictive Control of Nonlinear Discrete Time Systems with Guaranteed Stability

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