On the Fuzzy Model Predictive Control of Interconnected Nonlinear Systems

Dalhoumi, Latifa; Chtourou, Mohamed; Djemel, Mohamed

doi:10.1007/s13369-016-2412-z

Cited by 2 publications

(1 citation statement)

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“…Nevertheless, the output variations and operating time of the control process were not taken into account. In view of the nonlinearities and various disturbances inevitably existing in a complex multivariable system, the fuzzy model predictive control (FMPC) method has become a desired candidate for the promotion of the control performance [39]. The application of extended state observer-based FMPC algorithms on the boiler-turbine system in the ultrasupercritical unit of the power plant had handled these problems and obtained better tracking ability [40], whereas the rapidity of this method needed to be verified.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fuzzy Model Predictive Control of a Gas Turbine in the Combined Cycle Unit

et al. 2018

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The complex characteristics of the gas turbine in a combined cycle unit have brought great difficulties in its control process. Meanwhile, the increasing emphasis on the efficiency, safety, and cleanliness of the power generation process also makes it significantly important to put forward advanced control strategies to satisfy the desired control demands of the gas turbine system. Therefore, aiming at higher control performance of the gas turbine in the gas-steam combined cycle process, a novel fuzzy model predictive control (FMPC) strategy based on the fuzzy selection mechanism and simultaneous heat transfer search (SHTS) algorithm is presented in this paper. The objective function of rolling optimization in this novel FMPC consists of two parts which represent the state optimization and output optimization. In the weight coefficient selection of those two parts, the fuzzy selection mechanism is introduced to overcome the uncertainties existing in the system. Furthermore, on account of the rapidity of the control process, the SHTS algorithm is used to solve the optimization problem rather than the traditional quadratic programming method. The validity of the proposed method is confirmed through simulation experiments of the gas turbine in a combined power plant. The simulation results demonstrate the remarkable superiorities of the adopted algorithm with higher control precision and stronger disturbance rejection ability as well as less optimization time.

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Section: Introductionmentioning

confidence: 99%