Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Multilayer feedforward networks are universal approximators
Neural Networks
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Introduction to artificial neural systems
Introduction to artificial neural systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
Design and Implementation of Fuzzy Sliding-Mode Controller for a Wedge Balancing System
Journal of Intelligent and Robotic Systems
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When genetic algorithms (GAs) are applied for PID parameter tuning, since the PID parameters are adjusted almost randomly, it is possible that the plant will be damaged due to abrupt changes in PID parameters. To solve this problem, a neural network will be used to model the plant and the genetic tuning procedure will be performed on the neural network instead of the plant. After determining the PID parameters in this off-line manner, these gains are then applied to the plant for on-line control. Moreover, considering that the neural network model may not be accurate enough, a method is also proposed for on-line fine-tuning of PID parameters. To show the validity of the proposed method, a seesaw system that has one input and two outputs will be used for experimental evaluation,