Adaptation and tracking in system identification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
Continuous-time approaches to system indentification—a survey
Automatica (Journal of IFAC) - Identification and system parameter estimation
An introduction to fuzzy control
An introduction to fuzzy control
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Genetic Algorithms and Robotics
Genetic Algorithms and Robotics
Parallel Genetic Algorithms Population Genetics and Combinatorial Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Paper: A linguistic self-organizing process controller
Automatica (Journal of IFAC)
Theory and applications of adaptive control-A survey
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
| Hi-index | 0.98 |
The development of a controller for a nonlinear system is still a challenging task for control engineers. This paper presents a method for the optimization of a Fuzzy Logic System (FLS) [1] for the control of nonlinear systems. A fine-grained parallel genetic algorithm has been proposed to identify the parameters of the FLS. The proposed method has been applied to control a popular set of benchmark problems, i.e., an inverse pendulum with both constant and varying shaft length and a couple of unjoined inverse pendulums fixed on a single platform. It is argued that, because of its ability to capture the imprecise information that humans can understand very easily in natural language, a fuzzy logic system provides an ideal general frame of reference for modelling any nonlinear system involving uncertainties. In this context, the evolutionary algorithms with their parallel power to search through multidimensional space are effective in estimating the parameters of the fuzzy logic system. The fine-grained parallel genetic algorithm has been executed on a PC-hosted 16-node transputer platform running under the Helios operating system. The quantitative comparison of the fuzzy-evolutionary controller to the LQR controller has been given for one example system, while for the other two systems (for which there are no analytical solutions) the value of the objective function has been provided for future reference.