Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network

[[abstract]]Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Design of a hybrid controller for voice coil motors with simple self-learning fuzzy control

[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]Nov. 26-28[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Kaohsiung, Taiwa

Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems

[[abstract]]This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

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

Decoupled fuzzy sliding-mode balance control of wheeled inverted pendulums using an 8-bit microcontroller

[[abstract]]A wheeled inverted pendulum (WIP) system is a typical unstable complex nonlinear system widely utilized for educational purposes and control research. The dynamic of a WIP system can be represented as two second-order subsystems which represent the angle of body and the position of wheel. This paper proposes a decoupled fuzzy sliding-mode balance control (DFSBC) system based on a time-varying sliding surface for a WIP system. A decoupled sliding surface which includes the information of two-subsystem is designed to make the state trajectories of both subsystems move toward their sliding surface and then simultaneously approach zeros. The control effort of a WIP system is generated based on the idea that the state can quickly reach the decoupled sliding surface without large overshoot. Moreover, the slope of the decoupled sliding surface is adjusted by a fuzzy system, whose fuzzy rules are constructed based on the idea that the convergence time of the state trajectories can be reduced. Finally, an 8-bit microcontroller-based WIP system is setup. Experimental results show that the proposed DFSBC system can achieve favorable balance control response for the simultaneous control of the angle of body and the position of wheel.[[conferencetype]]國際[[conferencedate]]20120314~20120316[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Hong Kon

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

Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance

[[abstract]]The advantage of using cerebellar model articulation control (CMAC) network has been well documented in many applications. However, the structure of a CMAC network which will influence the learning performance is difficult to select. This paper proposes a dynamic structure CMAC network (DSCN) which the network structure can grow or prune systematically and their parameters can be adjusted automatically. Then, an adaptive dynamic CMAC neural control (ADCNC) system which is composed of a computation controller and a robust compensator is proposed via second-order sliding-mode approach. The computation controller containing a DSCN identifier is the principal controller and the robust compensator is designed to achieve L2 tracking performance with a desired attenuation level. Moreover, a proportional–integral (PI)-type adaptation learning algorithm is derived to speed up the convergence of the tracking error in the sense of Lyapunov function and Barbalat’s lemma, thus the system stability can be guaranteed. Finally, the proposed ADCNC system is applied to control a chaotic system. The simulation results are demonstrated that the proposed ADCNC scheme can achieve a favorable control performance even under the variations of system parameters and initial point.[[notice]]補正完畢[[incitationindex]]SCI[[booktype]]紙本[[booktype]]電子

Design and implementation of an interactive robot for a connect four game

[[abstract]]This paper proposes an interactive robot with entertainment application for a connect four game. The proposed interactive robot contains the main body of the robot mechanism, the servo motor module, the light sensor module, the touch switch module, the text-to-speech module, the servo card and the core board. The main body contains an XY stage to scan the colored disks position which the users put and a putting colored disk institution can be used for the action of the game artificial intelligence. Servo motor module is used to control the position of DC servo motor and light sensor modules to detect the board information. Through the user pressing the touch switch module, it indicates that the user has completed the colored disks action, and it is the robot turn to play colored disks. In the core board selection, this paper takes the advantages of high performance, low price of ARM Cortex-M3 microcontroller (LM3S1138) to be the whole core of the operation. The experimental results of the paper show that the proposed interactive robot can reach the goal to enhance entertainment.[[sponsorship]]Chinese Automatic Control Society (CACS); National Formosa University Taiwan[[conferencetype]]國際[[conferencedate]]20121130~20121202[[booktype]]電子版[[iscallforpapers]]Y[[iscallforpapers]]Y[[conferencelocation]]Yunlin, Taiwa

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