Chaos Synchronization Using Adaptive Dynamic Neural Network Controller with Variable Learning Rates

This paper addresses the synchronization of chaotic gyros with unknown parameters and external disturbance via an adaptive dynamic neural network control (ADNNC) system. The proposed ADNNC system is composed of a neural controller and a smooth compensator. The neural controller uses a dynamic RBF (DRBF) network to online approximate an ideal controller. The DRBF network can create new hidden neurons online if the input data falls outside the hidden layer and prune the insignificant hidden neurons online if the hidden neuron is inappropriate. The smooth compensator is designed to compensate for the approximation error between the neural controller and the ideal controller. Moreover, the variable learning rates of the parameter adaptation laws are derived based on a discrete-type Lyapunov function to speed up the convergence rate of the tracking error. Finally, the simulation results which verified the chaotic behavior of two nonlinear identical chaotic gyros can be synchronized using the proposed ADNNC scheme

Intelligent tracking control of a DC motor driver using self-organizing TSK type fuzzy neural networks

[[abstract]]In this paper, a self-organizing Takagi–Sugeno–Kang (TSK) type fuzzy neural network (STFNN) is proposed. The self-organizing approach demonstrates the property of automatically generating and pruning the fuzzy rules of STFNN without the preliminary knowledge. The learning algorithms not only extract the fuzzy rule of STFNN but also adjust the parameters of STFNN. Then, an adaptive self-organizing TSK-type fuzzy network controller (ASTFNC) system which is composed of a neural controller and a robust compensator is proposed. The neural controller uses an STFNN to approximate an ideal controller, and the robust compensator is designed to eliminate the approximation error in the Lyapunov stability sense without occurring chattering phenomena. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived to speed up the convergence rates of the tracking error. Finally, the proposed ASTFNC system is applied to a DC motor driver on a field-programmable gate array chip for low-cost and high-performance industrial applications. The experimental results verify the system stabilization and favorable tracking performance, and no chattering phenomena can be achieved by the proposed ASTFNC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[conferencetype]]國際[[conferencedate]]20120918~20120921[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tokyo, Japa

Adaptive TSK-type self-evolving neural control for unknown nonlinear systems

[[abstract]]In this paper, a real-time approximator using a TSK-type self-evolving neural network (TSNN) is studied. The learning algorithm of the proposed TSNN not only automatically online generates and prunes the hidden neurons but also online adjusts the network parameters.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120918~20120922[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Japan,Toky

Design of an adaptive self-organizing fuzzy neural network controller for uncertain nonlinear chaotic systems

[[abstract]]Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Adaptive hermite-polynomial-based CMAC neural control for chaos synchronization

[[abstract]]Gyros are a particularly interesting form of nonlinear systems that have attracted many researchers due to their applications in the navigational, aeronautical and space engineering domains. In this paper, a problem of synchronization between two chaotic gyros based on a mater-slave scheme is studied. An adaptive Hermite-polynomial-based CMAC neural control (AHCNC) system which is composed of a neural controller and a smooth compensator is proposed. The neural controller using a Hermite-polynomial-based CMAC neural network (HCNN) is main controller and the smooth compensator is designed to guarantee system stable in the Lyapunov stability theorem. Finally, the simulation results show that the proposed AHCNC scheme can achieve favorable chaos synchronization after the controller parameters learning.[[sponsorship]]Chinese Automatic Control Society (CACS); National Formosa University Taiwan[[conferencetype]]國際[[conferencedate]]20121130~20121202[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Yunlin, Taiwa

Adaptive hermite-polynomial-based CMAC neural control for chaos synchronization

[[abstract]]An adaptive Hermite-polynomial-based CMAC neural control (AHCNC) system which is composed of a neural controller and a smooth compensator is proposed. The neural controller using a Hermite-polynomial-based CMAC neural network (HCNN) is main controller and the smooth compensator is designed to guarantee system stable in the Lyapunov stability theorem.[[notice]]缺頁數[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20121130~20121202[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Yunlin, Taiwa

Engineering handbook

1995 handbook for the faculty of Engineerin

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties