Master–slave chaos synchronization using adaptive TSK-type CMAC neural control

Design of Adaptive TSK Fuzzy Self-Organizing Recurrent Cerebellar Model Articulation Controller for Chaotic Systems Control

“…Che et al proposed a robust adaptive artificial neural network controller for the synchronization of two synchronous gap junction-coupled neurons in order to synchronize the chaotic system by appropriately selecting the control parameters [9]. Wu et al proposed an adaptive TSK cerebellar model articulation controller for chaotic master-slave system synchronization by using TSK fuzzy rules and demonstrated good control tracking performance [10]. Wen et al proposed a recurrent fuzzy cerebellar model articulation controller for dual-axis piezoelectric actuators with an input space quantization method and self-tuning learning rates [11].…”

Robust Exponential Synchronization for a Class of Master-Slave Distributed Parameter Systems with Spatially Variable Coefficients and Nonlinear Perturbation

“…The study on master-slave systems has become more important for theoretical and practical points in many fields, including communication, mechanical systems, robotics, chemical reactions, and biological systems [1][2][3][4][5][6][7][8][9]. Ever since the discovery of Christian Huygens in 1665 on the synchronization of two pendulum clocks [10], synchronization has received considerable attention for a long time as a typical collective behavior and a basic motion in nature with potential applications in many different areas including secure communication, chaos generators design, chemical reactions, biological systems, and information science [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. The theory of synchronization for master-slave systems, which aims to control the slave system so that the output of the slave system follows the output of the master system [27], is a recent research area extensively investigated nowadays in many industrial and technical processes, such as unmanned aerial vehicle (UAV) team, vehicular platoons, rendezvous of space shuttles, and many other practical control systems (see, e.g., [28,29] and the references therein).…”

“…In this situation, it is important to study the synchronization problem of master-slave PDE systems. Therefore, some researchers have paid attention to the study of synchronization of master-slave PDE systems [17][18][19][20][21][22][23][24][25][26][27][28][29], where "design-then-reduce" approach was employed to take the full advantage of the original PDE model for the controller design [30][31][32][33][34]. References [35][36][37] researched synchronization of neural networks with reaction-diffusion terms.…”

Synchronization of chaotic systems with unknown parameters using adaptive passivity-based control

“…Since then, other hyperchaotic systems have been reported [4][5][6][7][8]. www [9,10], backstepping design [11,12], slidingmode control [13,14], Lyapunov-based control [15,16], LMI approach [17,18], intelligent control [19,20] and adaptive control [21][22][23][24]. The passivity theory is considered as an alternative tool for analyzing stability of nonlinear systems and designing controllers for nonlinear systems [25][26][27].…”