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Research Papers

Robust Proportional Integral Derivative Controller Design for Various Processes Using Novel Hybrid Metaheuristic Algorithms

[+] Author and Article Information
C. Agees Kumar

Professor
Department of Electrical and Electronics
Engineering,
Arunachala College of Engineering for Women,
Vellichanthai, Nagercoil 629 203, India
e-mail: ageesofficials@gmail.com

Saranya Rajeshwaran

Department of Computer
Science and Engineering,
Anna University,
Chennai 600 025, India
e-mail: saranya.rajeshwaran@gmail.com

Kanthaswamy Ganapathy

Trimble Information Technologies India Pvt. Ltd.,
Chennai 600 113, India
e-mail: kanthaswamy.g@gmail.com

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received November 6, 2016; final manuscript received January 11, 2018; published online March 13, 2018. Assoc. Editor: Douglas Bristow.

J. Dyn. Sys., Meas., Control 140(8), 081006 (Mar 13, 2018) (10 pages) Paper No: DS-16-1537; doi: 10.1115/1.4039186 History: Received November 06, 2016; Revised January 11, 2018

This paper compares the effectiveness of the proposed hybrid metaheuristic algorithms for a class of unstable systems with time delay to that of the existing ones. The local search and global methods of optimization are combined to yield more effective hybrid metaheuristic algorithms. These algorithms are used to tune the proportional–integral–derivative (PID) controllers, satisfying the robust stabilizing vector gain margin (VGM). Six global heuristic algorithms namely ant colony optimization (ACO), particle swarm optimization (PSO), biogeography-based optimization (BBO), population-based incremental learning (PBIL), evolution strategy (ES), and stud genetic algorithms (StudGA) are combined with the local search property of derivative free optimization methods such as simplex derivative based pattern search (SDPS) and implicit filtering (IMF) to yield hybrid metaheuristic algorithms. The efficacy of the proposed control schemes in terms of various time domain specifications and stabilizing VGM are compared with some existing methods for unstable process with time delay (UPTD) systems. The performance of the proposed control schemes particularly in the context of uncertainty in the plant is demonstrated using a case study. The efficacy of the proposed control scheme is illustrated with a nontransfer function based multibody vehicle autosteer control design problem.

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Figures

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Fig. 1

Smith predictor scheme

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Fig. 7

Tracking response achieved using autosteered vehicle control system with the proposed controller—dymola- and simulink-based cosimulation based on operating specification that desired steering wheel velocity is 486 deg/s

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Fig. 4

Robustness comparison of various metaheuristic PID schemes applied to uncertain (25% perturbed) UPTD

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Fig. 5

Robustness measure using VGM

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Fig. 6

Autosteered vehicle control system with electric power assist steering

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Fig. 2

Performance comparison of response of UPTD using metaheuristic methods using PID

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Fig. 3

Performance comparison of response of UPTD using metaheuristic methods based Smith PID

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Fig. 8

Tracking response achieved using autosteered vehicle control system with the proposed controller—dymola- and simulink-based cosimulation based on operating specification that desired steering wheel velocity is 420 deg/s

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