Particle Swarm Approach for Identification of Unstable Processes
In this paper, a step response based closed loop system identification procedure for a class of time delayed unstable chemical process loops using Particle Swarm Optimization algorithm is proposed. A novel objective function is developed using the time domain specification data to guide the PSO algorithm. The step response based identification is a simple closed loop test with a Proportional (P) controller. The PSO algorithm finds the best possible values for the process model parameters such as process gain (K), process time constant (τ), and the closed loop delay (θ c). The method is tested on a class of unstable process models in the presence and absence of measurement noise. The performance of the proposed PSO based identification procedure is compared with the classical identification scheme existing in the literature. The results evident that, the proposed method helps to accomplish a better transfer function model with considerably reduced model mismatch.
KeywordsUnstable system Particle Swarm Identification Step response Model validation
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