Title: Lagrangian-based state transition algorithm for tuning multivariable decentralised controller

Authors: G. Saravanakumar; K. Valarmathi; M. Willjuice Iruthayarajan; Seshadhri Srinivasan

Addresses: Department of Instrumentation and Control Engineering, Kalasalingam University, Anand Nagar, Krishanankoil, Srivilliputhur Tamil Nadu, India ' Department of Electronics and Communication Engineering, PSR Engineering College, Sivakasi, Tamilnadu, India ' Department of Electrical and Electronic Engineering, National Engineering College, K.R Nagar, Kovilpatti, Tamil Nadu, India ' International Research Center, Kalasalingam University, Anand Nagar, Krishanankoil, Srivilliputhur, Tamil Nadu, India

Abstract: Tuning PID controllers for multivariable process to optimise process performance is computationally intensive as it requires solution of constrained nonlinear optimisation problem. In this backdrop, evolutionary computing methods have emerged as alternatives to conventional optimisation tools. This investigation proposes the use of LBSTA to exploit its numerical stability and performance to tune multivariable decentralised PID controller considering the physical constraints posed by MIMO process. Simulations on Wood and Berry binary distillation column benchmark problem are used to illustrate the PID tuning rule aimed at reducing IAE subject to physical constraints. Three cases of simulations are performed. Further, the simulations used 20 independent trials by STA with Lagrangian constraints. Constraints imposed on control signal considering physical limitations of the actuator makes the algorithm suitable for industrial applications. Our results show that the LBSTA is not only optimal than unconstrained PBPSO algorithm-based tuning, but also achieves the optimal performance considering the physical limitations.

Keywords: multi-input multi-output; MIMO; proportional integral derivative; PID controllers; controller tuning; Lagrangian based state transition algorithm; LBSTA; distillation column; multivariable control; decentralised control; simulation.

DOI: 10.1504/IJAIP.2016.077497

International Journal of Advanced Intelligence Paradigms, 2016 Vol.8 No.3, pp.303 - 317

Received: 29 Sep 2014
Accepted: 23 Mar 2015
Published online: 04 Jul 2016 *

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