scholarly journals Stabilising model predictive control for discrete-time fractional-order systems

Automatica ◽  
2017 ◽  
Vol 75 ◽  
pp. 24-31 ◽  
Author(s):  
Pantelis Sopasakis ◽  
Haralambos Sarimveis
Keyword(s):  
Model Predictive Control ◽  
Fractional Order ◽  
Predictive Control ◽  
Discrete Time ◽  
Fractional Order Systems

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

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.

Model Predictive Control of Fractional Order Systems

2014 ◽  
Vol 9 (3) ◽  
Author(s):  
Aymen Rhouma ◽  
Faouzi Bouani ◽  
Badreddine Bouzouita ◽  
Mekki Ksouri
Keyword(s):  
Model Predictive Control ◽  
Dynamic Behavior ◽  
Fractional Order ◽  
Predictive Control ◽  
Internal Model ◽  
Direct Method ◽  
Control Law ◽  
Thermal System ◽  
Fractional Order Systems ◽  
Derivation Operator

This paper provides the model predictive control (MPC) of fractional order systems. The direct method will be used as internal model to predict the future dynamic behavior of the process, which is used to achieve the control law. This method is based on the Grünwald–Letnikov's definition that consists of replacing the noninteger derivation operator of the adopted system representation by a discrete approximation. The performances and the efficiency of this approach are illustrated with practical results on a thermal system and compared to the MPC based on the integer ARX model.

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