Design of fractional order modeling based extended non-minimal state space MPC for temperature in an industrial electric heating furnace. (August 2017)

• Record Type: Journal Article
• Authors: Zhang, Ridong; Zou, Qin; Cao, Zhixing; Gao, Furong
• Is Part Of: Journal of process control. Volume 56(2017)
• Page Start: 13
• Page End: 22
• Publication Date: 2017-08
• Subjects: Fractional order calculus -- Fractional order system -- Model predictive control -- Grünwald-Letnikov definition -- Extended non-minimal state space model; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2017.05.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: An improved approach of extended non-minimal state space (ENMSS) fractional order model predictive control (FMPC) is presented. Grünwald-Letnikov (GL) definition and the fractional order integral is introduced into the cost function. The temperature process control of a heating furnace model is illustrated. Abstract: In this paper, an improved approach of extended non-minimal state space (ENMSS) fractional order model predictive control (FMPC) is presented and tested on the temperature model of an industrial heating furnace. In the fractional order model predictive control algorithm, fractional order single-input single-output (SISO) system is discretized via fractional order Grünwald-Letnikov (GL) definition. The ENMSS fractional order model that contains the state variable and the fractional order output tracking error is formulated by choosing appropriate state variables. Meanwhile, the fractional order integral is introduced into the cost function and the GL definition is used to obtain the discrete form of the continuous cost function. Then the control signals are derived by minimizing the fractional order cost function. Lastly, the temperature process control of a heating furnace is illustrated to reflect the performance of the proposed FMPC method. Simulation results show the effectiveness of the proposed FMPC method.