M. Rajalakshmi1, V. Saravanan2, V. Arunprasad3, C. A. T. Romero4, O. I. Khalaf5, C. Karthik1,*
Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 339-359, 2022, DOI:10.32604/iasc.2022.021696
Abstract In sugar production, model parameter estimation and controller tuning of the nonlinear clarification process are major concerns. Because the sugar industry’s clarification process is difficult and nonlinear, obtaining the exact model using identification methods is critical. For regulating the clarification process and identifying the model parameters, this work presents a state transition algorithm (STA). First, the model parameters for the clarifier are estimated using the normal system identification process. The STA is then utilized to improve the accuracy of the system parameters that have been identified. Metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and State Transition… More >
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