System identification
Modeling of dynamic systems
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
Search space boundary extension method in real-coded genetic algorithms
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on evolutionary algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Future Generation Computer Systems - Special issue: Geocomputation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Automatically integrating multiple rule sets in adistributed-knowledge environment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Nonlinear modelling and control of helicopters
Automatica (Journal of IFAC)
Modified model-free adaptive controller for a nonlinear rotor system
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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This paper present a novel and scrutinized parametric modeling of a laboratory scale helicopter, a twin rotor multi input multi output system (TRMS), by employing a real-coded genetic algorithm (GA) technique. The main goal of this work is to emphasise the potential benefits of this architecture for real system identification. Instead of working on the conventional bit by bit operation, both the crossover and mutation operators are real-valued. The effectiveness of the proposed algorithm is demonstrated in comparison to a binary-coded GA in modelling the TRMS. A complete system identification procedure has been carried out, from experimental design to model validation using a laboratory-scale helicopter. In this case, the identified model is characterized by a fourth order linear ARMA structure which describes with very high precision the hovering motion of a TRMS. The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, an analysis of modeling of nonlinear aerodynamic function is needed and carried out in both time and frequency domains based on observed input and output data. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology.