Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures

Min-Ying Li, Kang-Di Lu, Yu-Xing Dai and Guo-Qiang Zeng

Complexity, 2020, vol. 2020, 1-26

Abstract:

As the actuator faults in an industrial process cause damage or performance deterioration, the design issue of an optimal controller against these failures is of great importance. In this paper, a fractional-order predictive functional control method based on population extremal optimization is proposed to maintain the control performance against partial actuator failures. The proposed control strategy consists of two key ideas. The first one is the application of fractional-order calculus into the cost function of predictive functional control. Since the knowledge of analytical parameters including the prediction horizon, fractional-order parameter, and smoothing factor in fractional-order predictive functional control is not known, population extremal optimization is employed as the second key technique to search for these parameters. The effectiveness of the proposed controller is examined on two industrial processes, e.g., injection modeling batch process and process flow of coke furnace under constant faults, time-varying faults, and nonrepetitive unknown disturbance. The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:4214102

DOI: 10.1155/2020/4214102

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