Abstract
Parameter tuning of PID controller for liquid level control of beer filling machine was studied in this paper, which can meet the demand of accurate controlling in beer production and improve the rapidity under the same conditions. Firstly, an improved genetic algorithm was proposed which has been verified by eight kinds of test functions. Simulation results revealed that, in comparation with other modified particle swarm optimization algorithm and modified genetic algorithm, the algorithm proposed in this work is not only capable to improve the convergence speed and precision under the same experimental conditions but also to improve the probability to converge to the optimal value. Finally, the proposed algorithm was applied to the parameter tuning of the PID controller of beer filling machine for liquid level control. Superior property had been obtained, which implied an effective improvement in the rapidity with the premise of steady-state error exclusion.
Highlights
In order to improve the performance of the control systems, various measures need to be taken [1, 2]
As presented in literature [13], aiming to overcome the shortages of the traditional quantum genetic algorithm, Jianxin FENG et al had addressed the premature problem of the traditional quantum genetic algorithm and improved the convergence speed through the following five aspects including coding method, population initialization, quantum revolving gates, quantum mutation, and the increase of quantum catastrophe. e optimized algorithm presented above had been applied to the parameter optimization of fuzzy self-tuning PID controller
In order to verify the performance under the same experimental conditions, the improved genetic algorithm had been applied to the optimization of eight different kinds of typical test functions. e simulation experimental environment is MATLAB R2009 B version, in which numbers including 50 in the population of the algorithm, 12 in the number of intervals, 200 in the maximum number of iterations, 0.90 in the crossover probability, 0.01 in the mutation probability, 2 × 10−15 in the allowable error, 0.5 in the introduction probability of particle swarm optimization algorithm, and 0.90 and 0.10 in the maximum and minimum inertial weight, respectively, had been set
Summary
Parameter tuning of PID controller for liquid level control of beer filling machine was studied in this paper, which can meet the demand of accurate controlling in beer production and improve the rapidity under the same conditions. An improved genetic algorithm was proposed which has been verified by eight kinds of test functions. Simulation results revealed that, in comparation with other modified particle swarm optimization algorithm and modified genetic algorithm, the algorithm proposed in this work is capable to improve the convergence speed and precision under the same experimental conditions and to improve the probability to converge to the optimal value. The proposed algorithm was applied to the parameter tuning of the PID controller of beer filling machine for liquid level control.
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