Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Applied system identification
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Digital Control Systems
Genetic Algorithms for Control and Signal Processing
Genetic Algorithms for Control and Signal Processing
Genetic Algorithms in Engineering Systems
Genetic Algorithms in Engineering Systems
Control Systems with Actuator Saturation: Analysis and Design
Control Systems with Actuator Saturation: Analysis and Design
International Journal of Systems Science
Stabilizing controller design for uncertain nonlinear systems using fuzzy models
IEEE Transactions on Fuzzy Systems
Brief Linear conditioning for systems containing saturating actuators
Automatica (Journal of IFAC)
A Trajectory-Based Point Tracker Using Chaos Evolutionary Programming
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Hi-index | 0.09 |
This paper presents a novel chaos-evolutionary-programming algorithm (CEPA), which merges a modified chaotic optimization algorithm (COA) with a modified evolutionary-programming algorithm (EPA). Due to the nature of chaotic variable, i.e. pseudo-randomness, ergodicity and irregularity, the CEPA can effectively and quickly search many local minimum or maximum in parallel thereby enhancing the probability of finding the global one. The CEPA is then successfully applied to solve challenging non-convex optimization problems and to obtain the best nominal dual-rate observer-based digital tracker for robust tracking a periodic solution embedded into a hybrid interval chaotic system with saturating inputs and not to track the strange attractor itself. An illustrative example is presented to demonstrate the effectiveness of the proposed algorithm.