Improved asynchronous PSO based design of multivariable PID controller

Chaudhary, Naveed Ishtiaq; Raj, Rishi; Kiran, Kuppalli R.; Nema, Swapnil; Padhy, Prabin Kumar

doi:10.1109/care.2013.6733710

Cited by 4 publications

(9 citation statements)

References 13 publications

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“…In Figure 6, the graphical results show the global best position over 50 runs that are achieved by the PSO, IPSO, and ACPSO for tuning the parameters of PID controller. From the ranges of axes in Figure 6, it can be seen that the global best position of the PSO, GA, and IPSO algorithm introduced in the literature 23,[33][34][35]38 are very different of the 50 independent runs. Meanwhile, the global best position of the ACPSO algorithm is within a small range of the 50 independent runs.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…In Figure 3, it can be seen that the diversity of all algorithms are different. The swarm's diversity for the proposed algorithm and the improved PSO algorithm in the study of Chiou et al 38 are stable, but the improved PSO algorithm in the study of Chaudhary et al 35 changes significantly, and the other are drastically decreasing with the iteration. Compared swarm's diversity for all algorithms, it can be obtained that the swarm's diversity for the proposed algorithm is better than these compared algorithms.…”

Section: Test Function Dimensionmentioning

confidence: 89%

“…A, Particle swarm optimization. B, GA. C, Algorithm in the study of Jamian et al D, Algorithm in the study of Alrijadjis et al E, Algorithm in the study of Taeib et al F, Algorithm in the study of Chaudhary et al G, Algorithm in the study of Chiou et al H, Adaptive chaos particle swarm optimization [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Pid Controller Based On Acpsomentioning

confidence: 99%

“…The ACPSO has also been tested against with improved PSO algorithm in the previous studies. 34,35,38 In the experiment, the parameters of PSO, ACPSO, and improved PSO in the previous studies 34,35,38 are set as follows: the max of inertia weight is 0.9 and the min is 0.4, the initial inertia weight is 0.6, acceleration constant c 1 and c 2 is 1.49, the population size is 100, the maximum number of iteration is 100, and the max velocity is 0.2. To be more confident, we repeated each test 50 times.…”

Section: Benchmark and Experimental Settingsmentioning

confidence: 99%

“…Range of Search 32,32] In Table 2, the column named PSO refers to the result achieved by the PSO algorithm, and other columns refer to the result achieved by improved PSO introduced in a number of studies, 34,35,38 and the column named ACPSO refers to the result achieved by the proposed algorithm in this paper.…”

Section: Test Function Dimensionmentioning

confidence: 99%

See 4 more Smart Citations

An adaptive chaos particle swarm optimization for tuning parameters of PID controller

Nie

Wang

Xiao

et al. 2017

Optim Control Appl Methods

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Summary An adaptive chaos particle swarm optimization (ACPSO) is presented in this paper to tune the parameters of proportional‐integral‐derivative (PID) controller. To avoid the local minima, we introduced a constriction factor. Meanwhile, the chaotic searching is combined with the particle swarm optimization to improve the ability of the proposed algorithm. A series of experiment is performed on 6 benchmark functions to confirm its performance. It is found that the ACPSO can get better solution quality in solving the global optimization problems and avoiding the premature convergence. Based on it, the proposed algorithm is applied to tune the PID controller's parameters. The performances of the ACPSO are compared with different inspired algorithms, and these results show that the ACPSO is more robust and efficient when it is used to find the optimal parameters of PID controller.

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