A multi-crossover genetic approach to multivariable PID controllers tuning
Expert Systems with Applications: An International Journal
Brief paper: Robust PID controller tuning based on the constrained particle swarm optimization
Automatica (Journal of IFAC)
Self-organizing genetic algorithm based tuning of PID controllers
Information Sciences: an International Journal
Evolutionary algorithms based design of multivariable PID controller
Expert Systems with Applications: An International Journal
Diversity enhanced particle swarm optimizer for global optimization of multimodal problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Covariance matrix adaptation evolution strategy based design of centralized PID controller
Expert Systems with Applications: An International Journal
International Journal of Automation and Computing
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
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In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency.