Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Some Guidelines for Genetic Algorithms with Penalty Functions
Proceedings of the 3rd International Conference on Genetic Algorithms
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Selecting concise training sets from clean data
IEEE Transactions on Neural Networks
Enhancement of multi-objective control performance via switching
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Modeling data envelopment analysis by chance method in hybrid uncertain environments
Mathematics and Computers in Simulation
Expert Systems with Applications: An International Journal
Mathematics and Computers in Simulation
| Hi-index | 0.00 |
The multi-criteria predictive control of nonlinear dynamical systems based on Artificial Neural Networks (ANNs) and genetic algorithms (GAs) are considered. The (ANNs) are used to determine process models at each operating level; the control action is provided by minimizing a set of control objective which is function of the future prediction output and the future control actions in tacking in account constraints in input signal. An aggregative method based on the Non-dominated Sorting Genetic Algorithm (NSGA) is applied to solve the multi-criteria optimization problem. The results obtained with the proposed control scheme are compared in simulation to those obtained with the multi-model control approach.