Guest Editors
Prof. Praveen Agarwal, Anand-ICE, India
Prof. Mahmoud Abdel-Aty, Sohag University, Egypt
Prof. Shaher Momani, Ajman University, UAE
Prof. Shilpi Jain, Poornima College of Engineering, India
Summary
Artificial Intelligence in Fractional-order Systems details the use of fractional calculus (calculus of non-integer order) in the description and modeling of systems and in a range of control designs and practical applications. It is largely self-contained, covering the fundamentals of fractional calculus together with some analytical and numerical techniques and providing MATLAB® codes for the simulation of fractional-order control (FOC) systems. The methods of artificial intelligence process modeling emphasize the efficiency of obtaining an accurate model for the separation process that can be used for controller tuning or as a reference model in the structure of automatic control systems for the concentration of the 13C isotope. The use of fractional calculus can improve and generalize well-established control methods and strategies. Many different FOC schemes can be presented for control and dynamic systems problems. These extend to the challenging control engineering design problems of robust and nonlinear control. Practical material relating to a wide variety of applications including, among others, mechatronics, AI, computer science and engineering, irrigation and water management, and biological systems is also provided. All the control schemes and applications can be presented by researchers with either system simulation results or real experimental results or both.
The aim of this special issue is to collate original research articles that focus on the recent results on artificial intelligence in fractional-order systems, which can be obtained from the novel methods constructed by many researchers, as well as developments in recent methods with new operators or new approximations. It will also welcome review articles discussing the current state of the art.
Keywords
• Artificial intelligence
• Intelligent automation
• Control systems
• Computer science
• mathematical modeling and systems engineering
• Neural network
• 13C isotope
• Approximation and simulation
• Computational methods for the fractional PDEs in physics and engineering
• Complex networks
• Fractional order dynamical and control systems • Hidden attractors in complex systems
• New methods of control and synchronization nonlinear systems
• Novel computation algorithms for studying nonlinear systems
• Oscillations and chaos in dynamic economic models
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