Computer process control with advanced control applications
Computer process control with advanced control applications
Dynamics (2nd ed.): numerical explorations
Dynamics (2nd ed.): numerical explorations
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Process Modeling,Simulation and Control for Chemical Engineers
Process Modeling,Simulation and Control for Chemical Engineers
Chaos and Fractals
A multi-crossover genetic approach to multivariable PID controllers tuning
Expert Systems with Applications: An International Journal
Brief paper: Multivariable PID control with set-point weighting via BMI optimisation
Automatica (Journal of IFAC)
Brief paper: Robust PID controller tuning based on the constrained particle swarm optimization
Automatica (Journal of IFAC)
Self-organizing genetic algorithm based tuning of PID controllers
Information Sciences: an International Journal
Application of chaos and fractal models to water quality time series prediction
Environmental Modelling & Software
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Expert Systems with Applications: An International Journal
Firefly Algorithm for Continuous Constrained Optimization Tasks
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Expert Systems with Applications: An International Journal
Firefly algorithm, stochastic test functions and design optimisation
International Journal of Bio-Inspired Computation
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
Chaos driven evolutionary algorithms for the task of PID control
Computers & Mathematics with Applications
Hybrid GA-BF based intelligent PID controller tuning for AVR system
Applied Soft Computing
A simplified version of mamdani's fuzzy controller: the natural logic controller
IEEE Transactions on Fuzzy Systems
Multimodal size, shape, and topology optimisation of truss structures using the Firefly algorithm
Advances in Engineering Software
Modeling and model predictive control of a nonlinear hydraulic system
Computers & Mathematics with Applications
Hybrid parallel chaos optimization algorithm with harmony search algorithm
Applied Soft Computing
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Nowadays, a variety of controllers used in process industries are still of the proportional-integral-derivative (PID) types. PID controllers have the advantage of simple structure, good stability, and high reliability. A relevant issue for PID controllers design is the accurate and efficient tuning of parameters. In this context, several approaches have been reported in the literature for tuning the parameters of PID controllers using evolutionary algorithms, mainly for single-input single-output systems. The systematic design of multi-loop (or decentralized) PID control for multivariable processes to meet certain objectives simultaneously is still a challenging task. This paper proposes a new chaotic firefly algorithm approach based on Tinkerbell map (CFA) to tune multi-loop PID multivariable controllers. The firefly algorithm is a metaheuristic algorithm based on the idealized behavior of the flashing characteristics of fireflies. To validate the performance of the proposed PID control design, a multi-loop multivariable PID structure for a binary distillation column plant (Wood and Berry column model) and an industrial-scale polymerization reactor are taken. Simulation results indicate that a suitable set of PID parameters can be calculated by the proposed CFA. Besides, some comparison results of a genetic algorithm, a particle swarm optimization approach, traditional firefly algorithm, modified firefly algorithm, and the proposed CFA to tune multi-loop PID controllers are presented and discussed.