Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Modern control engineering (3rd ed.)
Modern control engineering (3rd ed.)
Modern Control Systems
Instrumentation and Control: Fundamentals and Applications
Instrumentation and Control: Fundamentals and Applications
Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming
Genetic Programming and Evolvable Machines
Design of optimal disturbance rejection PID controllers usinggenetic algorithms
IEEE Transactions on Evolutionary Computation
A simple line search operator for ridged landscapes
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Self-organizing genetic algorithm based tuning of PID controllers
Information Sciences: an International Journal
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Evolutionary Algorithms (EAs) have been largely applied to optimisation and synthesis of controllers. In spite of several successful applications and competitive solutions, the stochastic nature of EAs and the uncertainty of the results have considerably hindered their use in industrial applications. In this paper we propose a Genetic Algorithm (GA) for tuning controllers for classical first and second order plants with actuator nonlinearities. To increase the robustness of the algorithm we introduce two features: 1) genetic operators that perform directional mutations, 2) selection tournaments organized by genome vicinity. The experiment results show that the proposed GA is able to guarantee high performance and low variance in the results from different runs. The increased reliability, compared to the results from a classical GA, seems to favour particularly the application of Evolutionary Computation (EC) in tuning of control systems, where, thanks to this approach, a large search space can be searched repeatedly with high consistency in the solutions.