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Abstract

Aiming at nonlinear, large delayed time and large load variation characteristics of chemical dosing system in power plants, a PID (Proportional Integral Differential) algorithm with neural network based on multi-model switching and improved Smith pre-compensation is proposed. The algorithm uses Smith pre-compensation to deal with the large delayed time, and uses RBF (Radical Basis Function) neural network to adjust PID parameters to deal with the nonlinear. The multi-model switching control strategy is also adopted to transform the highly nonlinear dosing system of power plant into several linearized sub-models according to load ranges, which overcomes the difficult problem of large load variation and disturbance. To reduce transition time and fluctuations caused by model switching, an improved Smith pre-compensation controller for multi-model switching is proposed, where two parallel Smith predictors are added to the Smith pre-compensation part. The three Smith predictors can match three mathematical sub-models of the control system well. Finally, to improve control effects, genetic algorithm is adopted to automatically optimize the parameters. These simulation results show that the control strategy can obtain higher robustness and steadiness.

Keywords

Chemical dosing system multi-model switching Smith pre-compensation model mismatch Genetic algorithm

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Study of chemical dosing control system in power plants based on multi-model switching and improved smith pre-compensation

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