Unstable periodic orbits are the skeleton of a chaotic attractor. We constructed an associative memory based on the chaotic attractor of an artificial neural network, which associates input patterns to unstable periodic orbits. By processing an input, the system is driven out of the ground state to
Stabilizing unstable periodic orbits of chaotic systems via an optimal principle
โ Scribed by Yu-Ping Tian; Xinghuo Yu
- Publisher
- Elsevier Science
- Year
- 2000
- Tongue
- English
- Weight
- 159 KB
- Volume
- 337
- Category
- Article
- ISSN
- 0016-0032
No coin nor oath required. For personal study only.
โฆ Synopsis
A novel time-delayed control method is proposed for stabilizing inherent unstable periodic orbits (UPOs) in chaotic systems. Differing from the commonly used linear time-delayed feedback control form, we adopt an optimal control principle for the design of the time delayed feedback control. We explore the inherent properties of chaotic systems and use the system states and time-delayed system states in forming a performance index so that when the index is minimized, the resulting controller enables stabilization of the desired UPOs. The effectiveness of the method is confirmed by computer simulations.
๐ SIMILAR VOLUMES
## a b s t r a c t State-feedback model predictive control (MPC) of discrete-time linear periodic systems with timedependent state and input dimensions is considered. The states and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. First, periodic controlled