Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A multiobjective evolutionary algorithm toolbox for computer-aidedmultiobjective optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiregion fuzzy logic controller for nonlinear process control
IEEE Transactions on Fuzzy Systems
Fuzzy control of pH using genetic algorithms
IEEE Transactions on Fuzzy Systems
A multi-platform, multi-language environment for process modelling, simulation and optimisation
International Journal of Computer Applications in Technology
Design of a fuzzy controller for pH using genetic algorithm
ICS'05 Proceedings of the 9th WSEAS International Conference on Systems
Multi-objective optimization of TSK fuzzy models
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Current sharing of paralleled DC-DC converters using GA-based PID controllers
Expert Systems with Applications: An International Journal
A neuro-fuzzy GA-BP method of seismic reservoir fuzzy rules extraction
Expert Systems with Applications: An International Journal
Reset control of an industrial in-line pH process
ETFA'09 Proceedings of the 14th IEEE international conference on Emerging technologies & factory automation
GA-based neural network for energy recovery system of the electric motorcycle
Expert Systems with Applications: An International Journal
Hybrid model of pH neutralization for a pilot plant
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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The work described in this paper aims at exploring the use of computational intelligence (CI) techniques for designing a Wiener-model controller to perform pH control. First, genetic algorithm (GA) is utilized to identify the static inverse titration relationship of a weak-acid strong-base titration process. The resulting model of the inverse neutralization equation then serves as the component in a Wiener model controller that linearizes the pH process. As the bulk of the system non-linearity is cancelled by the inverse model, a setpoint-weighted Proportional plus Integral plus Derivative (PID) controller is used to generate the control signal. A multi-objective evolutionary algorithm (MOEA) is employed to evolve a pareto optimal set of PID parameters in order to achieve the conflicting goals of fast rise time with small overshoots. Experimental results obtained from a laboratory-scale acid-base titration process are then presented to demonstrate the feasibility of the design methodology.