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Evolutionary Control Systems

Evolutionary Control Systems
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Author(s): Jesús-Antonio Hernández-Riveros (Universidad Nacional de Colombia, Colombia), Jorge Humberto Urrea-Quintero (Universidad de Antioquia, Colombia) and Cindy Vanessa Carmona-Cadavid (Universidad Pontificia Bolivariana, Colombia)
Copyright: 2018
Pages: 43
Source title: Incorporating Nature-Inspired Paradigms in Computational Applications
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-5020-4.ch007


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In control systems, the actual output is compared with the desired value so a corrective action maintains an established behavior. The industrial controller most widely used is the proportional integral derivative (PID). For PIDs, the process is represented in a transfer function. The linear quadratic regulator (LQR) controller needs a state space model. The process behavior depends on the setting of the controller parameters. Current trends in estimating those parameters optimize an integral performance criterion. In this chapter, a unified tuning method for controllers is presented, the evolutionary algorithm MAGO optimizes the parameters of several controllers minimizing the ITAE index, applied on benchmark plants, operating on servo and regulator modes, and representing the system in both transfer functions and differential equation systems. The evolutionary approach gets a better overall performance comparing with traditional methods. The evolutionary method is indeed better than the classical, eliminating the uncertainty in the controller parameters. Better results are yielded with MAGO algorithm than with optimal PID, optimal-robust PID, and LQR.

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