Iterative Learning and Fractional Order Control for Complex Systems

• Published in: Complexity (New York, N.Y.) N.Y.), 2019-01, Vol.2019 (1)
• Main Authors: Bouakrif, Farah, Azar, Ahmad Taher, Volos, Christos K., Muñoz-Pacheco, Jesus M., Pham, Viet-Thanh
• Format: Article
• Language: English
• Subjects: Automation; Bifurcation theory; Chaos theory; Communications systems; Complex systems; Control algorithms; Control methods; Control systems; Control theory; Iterative methods; Mathematical models; Phase diagrams; Robots; Synchronism; Systems science; Tracking control; Tracking errors
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2019/7958625

ILC differs from most existing control methods in the sense that it exploits every possibility of incorporating past control information, such as tracking errors and control input signals, into the construction of the present control action to enable the controlled system to perform progressively better from operation to operation. Since the ILC method was proposed by Uchiyama [8] and presented as a formal theory by Arimoto [9], this technique has been the center of interest of many researchers over the last decades [10–19]. Fractional order control systems have also received considerable attention recently, from both an academic and industrial viewpoint, because of their increased flexibility (concerning integer-order systems) which allows more accurate modeling of complex systems and the achievement of more challenging control requirements [20–24]. Complex dynamics of the fractional-order chaotic system are analyzed by means of Lyapunov exponent spectra, bifurcation diagrams, and phase diagrams. [...]tracking synchronization controllers were theoretically designed and numerically investigated. [...]this system can provide rich encoding keys for chaotic communication.