Robust PID Controller Design for Various Processes using Novel Hybrid Metaheuristic Algorithms

[+] Author and Article Information
Agees Kumar C

Professor, Department of Electrical and Electronics Engineering, Arunachala College of Engineering for Women, Vellichanthai, Nagercoil, India

Saranya Rajeshwaran

Anna University, Chennai, India

Kanthaswamy G

Trimble Information Technologies India Pvt. Ltd, Chennai, India

1Corresponding author.

ASME 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 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 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 non-transfer function based multi body vehicle auto steer control design problem.

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