Abstract
Simple and easy to use methods are of great practical demand in the design of Proportional, Integral, and Derivative (PID) controllers. Controller design criteria are to achieve a good set-point tracking and disturbance rejection with minimal actuator variation. Achieving satisfactory trade-offs between these performance criteria is not easily accomplished with classical tuning methods. A particle swarm optimization technique is proposed to design PID controllers. The design method minimizes a compromise cost function based on both the integral absolute error and control signal total variation criteria. The proposed technique is tested on an Arduino-based Temperature Control Laboratory (TCLab) and compared with the Grey Wolf Optimization algorithm. Both TCLab simulation and physical data show that satisfactory trade-offs between the performance and control effort are enabled with the proposed technique.
Highlights
Despite the development of more refined control techniques, the Proportional, Integral, and Derivative (PID) control continues to be ubiquitous for industrial control [1,2]
The design of Proportional, Integral, and Derivative controllers (PID) using a technique based on the particle swarm optimization algorithm is presented in this work
Classical PID tuning rules are dependent of the system dynamics and PID controller structure
Summary
Despite the development of more refined control techniques, the Proportional, Integral, and Derivative (PID) control continues to be ubiquitous for industrial control [1,2]. The PSO design of PID controllers based on an additive compromise cost function involving the Integral Absolute Error (IAE) and Total Variation (TV) is proposed. This technique is validated both with simulation and practical results obtained with the TCLab. The results obtained with the proposed technique are compared with the ones obtained with the GWO algorithm and classical tuning techniques. The proposed technique is compared with the original GWO algorithm, in a TCLab temperature control case study, providing a similar control performance Both the simulation and practical validation with TCLab tests, show an effectiveness to design.
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