Self-tuning of PID parameters based on the modified particle swarm optimization
Guoming Huang, Dezhao Wu, Wailing Yang, Yuncan Xue
Abstract
Guoming Huang, Dezhao Wu, Wailing Yang, Yuncan Xue
Abstract
In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters. To overcome premature of standard PSO algorithm, a modified PSO (MPSO) based on partial particle moving direction changing was proposed. It holds on the proprieties of simple structure, fast convergence, and at the same time, enhances the variety of the populations, extends the search space, and does not increase the computation complexity. Simulation results show that the algorithms are effective and the designed controller has excellent performance.
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In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters. To overcome premature of standard PSO algorithm, a modified PSO (MPSO) based on partial particle moving direction changing was proposed. It holds on the proprieties of simple structure, fast convergence, and at the same time, enhances the variety of the populations, extends the search space, and does not increase the computation complexity. Simulation results show that the algorithms are effective and the designed controller has excellent performance.
Key concepts: Particle swarm optimization, PID controller, Convergence (economics), Control theory (sociology), Computation, Computer science, Mathematical optimization, Controller (irrigation)