Genetic algorithms applied in online autotuning PID parameters of a liquid-level control system

Abstract

In this paper, a simple genetic algorithm (GA) method has been applied in a real-time experiment on a liquid-level control system for online autotuning proportional-integral-derivative (PID) parameters. Our proposed method can automatically choose the best PID parameters for each generation. Then, using the reproduction, crossover and mutation to create the new population for other PID parameters, it can continuously control the liquid-level system until the preset iteration number is reached. Finally, the best PID parameters can be obtained. Furthermore, two selection methods, roulette wheel and tournament, have been compared in real-time experiments. Real-time experimental results are given to demonstrate the effectiveness and usefulness for online tuning PID parameters via this evolution process.

Nonlinear inverted pendulum PID control by an improved Artificial Bees Colony-Predator and prey approach

“…To reduce or cancel the observed overshoot in Figure 6, we use the objective function in (13) to minimize overshoot, steady error, settling time and rise time with β =log (2). We compared also our approach with genetic algorithm (GA).…”

“…They involve three gains to be adjusted [1] with Trial / Error in case of nonlinear systems. Adjusting these parameters is considered as an optimization problem which has been solved by evolutionary algorithms (EAs), including genetic algorithms [2], [3], ant colony optimization [4], particle swarm optimization [5], [6] and biogeography based optimization (BBO) [7].…”

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“…Genetic algorithm (GA) is adopted to optimize the membership functions because of its global optimization and robustness. 25 The length of left, right side and center point of each membership function are the parameters to be optimized by the optimization rule in Eq. (1).…”

Application of Biogeography based optimization in tuning a PID controller for nonlinear systems

“…The proportional part is responsible for following the desired set-point, while the integral and derivative part account for the accumulation of past errors and the rate of change of error in the process respectively [2]. Adjusting the PID parameters is considered as an optimization problem which has been solved by evolutionary algorithms (EAs), including genetic algorithms ( [3], [4], and [5]), ant colony optimization [6] and particle swarm optimization ( [7], [8], [9]). …”