2008 · 25 citations · 16 references
ArchitectureComputational EngineeringDesign OptimizationOptimizationProcess ControlController TuningEvolutionary ComputationEvolutionary OptimizationGenetic AlgorithmsMetaheuristic OptimizationParameter OptimizationConvergence Rate AnalysisEvolutionary AlgorithmsOptimization Of Complex SystemsOptimization-based TuningParameter ControlParticle Swarm ControlParticle Swarm OptimizationPid Tuning
PID parameter optimization is an important problem in control field. Particle swarm optimization (PSO) is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. To resolve this problem an advanced particle swarm optimization (APSO) is proposed in this paper. This new algorithm is proposed to augment the original PSO searching speed. This study proposes to use the (APSO) for its fast searching speed. These advanced particle swarm optimization to accelerate the convergence. The algorithms are simulated with MATLAB programming. The simulation result shows that the PID controller with (APSO) has a fast convergence rate and a better dynamic performance.
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James Kennedy, R.C. Eberhart · 2002 · 47.5K citations
Evolutionary computation: Toward a new philosophy of machine intelligence
Thomas B�ck · Complexity · 1997 · 1.5K citations