Output feedback robust distributed model predictive control for parallel systems in process networks with competitive characteristics

Zhang, Shuzhan; Zhao, Ding; Spurgeon, Sarah K.

doi:10.1016/j.conengprac.2021.104842

Cited by 9 publications

(2 citation statements)

References 31 publications

(53 reference statements)

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“…This experiment seeks to control the temperature and concentration of a reactor. The system dynamic equations of the CSTR have been given in [44]. Set the sampling time as T ′ s = 0.45s.…”

Section: B Experimental Trialmentioning

confidence: 99%

Model-Free Adaptive State Feedback Control for a Class of Nonlinear Systems

Gao

Zhao

Yan

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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This paper investigates state feedback control for a class of discrete-time multiple input and multiple output nonlinear systems from the perspective of model-free adaptive control and state observation. The design of a dynamic state feedback control can be efficiently carried out using dynamic linearization and state observation. The stability of the proposed method is guaranteed by theoretical analysis. Numerical simulation tests and experimentation on a continuous stirred tank reactor are carried out to validate the effectiveness of the proposed approach.Note to Practitioners-The growth in the scale of factories and the complexity of associated production processes increases the complexity and time involved in associated mathematical modelling. Data driven approaches to control remove the need to model processes. To the best of the authors' knowledge, existing approaches to model-free adaptive control (MFAC) of general systems are all based on an input-output control paradigm. These methods thus cannot guarantee the stability of the system state. The purpose of this study is to develop a novel Model-Free Adaptive Control (MFAC) approach to achieve control of the system state. In this paper, the assumptions required to achieve model-free adaptive control by state feedback are presented mathematically. A controller design and the associated stability proof are then presented. Numerical simulation and experimentation is conducted to validate the effectiveness of the proposed approach. In future research, state feedback data control in the presence of random disturbances will be investigated.

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Section: B Experimental Trialmentioning

confidence: 99%

Model-Free Adaptive State Feedback Control for a Class of Nonlinear Systems

Gao

Zhao

Yan

et al. 2024

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…In this case, an approach to distributed output feedback RMPC (OFRMPC) is desirable. The majority of such distributed OFRMPC approaches adopt dynamic output feedback and frequently an observer approach is used [26], [27], including a tube-based minimax observer [28], a Luenberger observer [29] and a moving horizon observer [30]. Due to the implementation of the state observer the system order is typically increased [31], [32], [33], which in turn may increase the controller complexity, produce more difficult stability analysis and even reduce control accuracy [34], [35], [33].…”

Section: Introductionmentioning

confidence: 99%