An observer-based tracker for hybrid interval chaotic systems with saturating inputs: The chaos-evolutionary-programming approach

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.