Self-organizing fuzzy sliding-mode control for a voice coil motor

[[abstract]]Voice coil motor (VCM) is widely known as its topquality of free friction, low noise, fast transient response and well repeatability. Yet the dynamic characteristic of a VCM is nonlinear and time-varying, thus the model-based conventional controller is difficult to achieve high-precision control performance for a VCM. To attack this problem, a selforganizing fuzzy sliding-mode control (SFSC) system is proposed in this paper. All of the fuzzy rules are online grown and pruned by the structure learning phase and the parameter learning phase is designed to tune the controller parameter in the gradient-descent-learning algorithm. From the experiment results, it shows that the proposed SFSC system can successfully control a VCM with favorable control response with enhanced disturbance rejection performance.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]April 9-11[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa

Master-slave chaos synchronization using adaptive dynamic sliding-mode neural control system

[[abstract]]This paper proposes an adaptive dynamic sliding-mode neural control (ADSMNC) system composed of a neural controller and a switching compensator.[[notice]]補正完畢[[incitationindex]]SC

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

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

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

Fuzzy control, sliding-mode control, decoupled sliding surface, microcontroller, wheeled inverted pendulum[[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.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120314~20120316[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]China,Hong Kon

Design of an intelligent exponential-reaching sliding-mode control via recurrent fuzzy neural network

[[conferencetype]]國際[[conferencedate]]20140618~20140620[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Taichung, Taiwa

Adaptive probabilistic fuzzy neural control for nonlinear chaotic systems

[[conferencetype]]國際[[conferencedate]]20140711~20140713[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Shanghai, Chin

A case study of connect-four interactive robots

[[sponsorship]]IEEE Systems, Man & Cybernetics Society (SMC); Asian Control Associations (ACA); The Society of Instrument and Control Engineers (SICE, Japan); The Institute of Control, Robotics and Systems (ICROS, Korea)[[conferencetype]]國際[[conferencedate]]20131202~20131204[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Nantou, Taiwa

Design of adaptive B-spline neural network controller via backstepping approach

[[conferencetype]]國際[[conferencedate]]20140909~20140912[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Sapporo, Japa

[[alternative]]Design of an Interactive Robot Using Kinect Sensor Approach

計畫編號：NSC102-2221-E032-052 研究期間：201308~201407 研究經費：552,000[[sponsorship]]行政院國家科學委員