Fractional-order model predictive control as a framework for electrical neurostimulation in epilepsy

The steam/water loop is a crucial part of a steam power plant. However, satisfying control performance is difficult to obtain due to the frequent disturbance and load fluctuation. A fractional order model predictive control was studied in this paper to improve the control performance of the steam/water loop. Firstly, the dynamic of the steam/water loop was introduced in large-scale ships. Then, the model predictive control with an extended prediction self adaptive controller framework was designed for the steam/water loop with a distributed scheme. Instead of an integer cost function, a fractional order cost function was applied in the model predictive control optimization step. The superiority of the fractional order model predictive control was validated with reference tracking and load fluctuation experiments.

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Model-Plant Mismatch in Fractional Order Model Predictive Control

Theoretical Developments and Applications of Non-Integer Order Systems - Lecture Notes in Electrical Engineering ◽

10.1007/978-3-319-23039-9_24 ◽

2015 ◽

pp. 281-291 ◽

Cited By ~ 3

Author(s):

Stefan Domek

Keyword(s):

Model Predictive Control ◽

Fractional Order ◽

Predictive Control ◽

Order Model ◽

Model Plant ◽

Fractional Order Model

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Model Predictive Control of General Fractional Order Systems Using a General Purpose Optimal Control Problem Solver: RIOTS

Volume 9: 15th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications ◽

10.1115/detc2019-97489 ◽

2019 ◽

Author(s):

Sina Dehghan ◽

Tiebiao Zhao ◽

YangQuan Chen ◽

Taymaz Homayouni

Keyword(s):

Optimal Control ◽

Model Predictive Control ◽

Fractional Order ◽

Predictive Control ◽

Order System ◽

Problem Solver ◽

Fractional Order Systems ◽

Application Process ◽

Order Model ◽

Integer Order

Abstract RIOTS is a Matlab toolbox capable of solving a very general form of integer order optimal control problems. In this paper, we present an approach for implementing Model Predictive Control (MPC) to control a general form of fractional order systems using RIOTS toolbox. This approach is based on time-response-invariant approximation of fractional order system with an integer order model to be used as the internal model in MPC. The implementation of this approach is demonstrated to control a coupled MIMO commensurate fractional order model. Moreover, the performance and its application process is compared to examples reported in the literature.

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Fractional-Order Model Predictive Frequency Control of an Islanded Microgrid

Energies ◽

10.3390/en12010084 ◽

2018 ◽

Vol 12 (1) ◽

pp. 84 ◽

Cited By ~ 14

Author(s):

Min-Rong Chen ◽

Guo-Qiang Zeng ◽

Yu-Xing Dai ◽

Kang-Di Lu ◽

Da-Qiang Bi

Keyword(s):

Cost Function ◽

Fractional Order ◽

Predictive Control ◽

Frequency Control ◽

Optimal Frequency ◽

Order Model ◽

Fractional Order Calculus ◽

Fractional Order Model ◽

Islanded Microgrid ◽

Fractional Order Integral

Optimal frequency control of an islanded microgrid has been a challenging issue in the research field of microgrids. Recently, fractional-order calculus theory and some related control methods have attempted to handle this issue. In this paper, a novel fractional-order model predictive control (FOMPC) method is proposed to achieve the optimal frequency control of an islanded microgrid by introducing a fractional-order integral cost function into model predictive control (MPC) algorithm. Firstly, a discrete state-space model is derived for the optimal frequency control problem of an islanded microgrid. Afterward, a fractional-order integral cost function is designed to guide the FOMPC algorithm to obtain optimal control law by borrowing the Grünwald-Letnikov (GL) definition of fractional order calculus. Six simulation studies have been carried out to illustrate the superiority of FOMPC to conventional MPC under dynamical load disturbances, perturbed system parameters and random dynamical power fluctuation of wind turbines.

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