Control of dead-time systems using derivative free local search guided population based incremental learning algorithms

Ganapathy, Kanthaswamy; Jerome, Jovitha

doi:10.1007/s11081-012-9194-2

Cited by 7 publications

(2 citation statements)

References 23 publications

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“…The comparison of the tuning factors and the value of the performance index, ISE are tabulated in Table .1. Readers are directed to refer to Kanthaswamy and Jovitha (2012) for IMF and SDPS-based PID and Smith PID schemes derived for all the case studies discussed in this paper. The IMF method when used alone failed for the case of the PID controller, while SDPS found close optimal solution independently without StudGA.…”

Section: Control Of Integrating Processes With Time Delay (Iptd)mentioning

confidence: 99%

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Optimal tuning of PID controllers for dead-time systems using Stud genetic algorithms

Ganapathy

C²,

Jerome

2014

Journal of Vibration and Control

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The tuning of controllers is a field of intrinsic research in the control and automation industry. Proportional-integral-derivative (PID) controllers and their variant forms have been much studied for automatic controller tuning owing to their simplicity and performance. The challenge in autotuning controllers is increased in systems that involve delay. Use of metaheuristics and soft computing methods are increasingly dominant in the autotuning of PID controllers. This paper deals with the design of a PID controller and Smith’s predictor for processes with delay. The algorithm employs a Stud genetic algorithm that is search intensified using derivative free search methods, namely simplex derivative pattern search and implicit filtering. The resulting algorithms are named as DFSGStudGA1 and DFSGStudGA2 respectively. The effectiveness of the discussed schemes are compared against some existing methods of controller tuning for robustness in terms of vector gain margin (VGM) and tolerance to plant uncertainty.

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Section: Control Of Integrating Processes With Time Delay (Iptd)mentioning

confidence: 99%

“…Table 4 shows that the results of PID and Smith PID controllers are not convergent using IMF (Kanthaswamy and Jovitha, 2012) and SDPS working independently. Both methods fail when used independently without an initial starting point guided by StudGA.…”

Section: Control Of An Unstable Processmentioning

confidence: 99%