Showing papers by "Ching-Chih Tsai published in 2015"

Adaptive RFWCMAC cooperative formation control for multiple ballbots incorporated with coupling dynamics

Abstract: This paper presents an intelligent consensus-based cooperative formation control using recurrent fuzzy wavelet cerebellar-model-articulation-controller (RFWCMAC) for a team of uncertain multiple ballbots. The dynamic model of each ballbot is formulated as one multivariable second-order underactuated dynamic system model, and the multirobot system is modeled by graph theory. By online learning the system uncertainties using RFWCMAC, an intelligent consensus-based cooperative formation control approach is presented using the Lyapunov stability theory and sliding-mode control approach, in order to carry out formation control in the presence of uncertainties. Simulations are conducted to show the effectiveness and merits of the proposed method.

Intelligent Adaptive Trajectory Tracking Control for an Autonomous Small-Scale Helicopter Using Fuzzy Basis Function Networks

Abstract: This paper presents an intelligent adaptive trajectory tracking controller using fuzzy basis function networks FBFN for an autonomous small-scale helicopter. The FNFN is used online to learn the vehicle mass and the coupling effect between the force and the moments, and the intelligent adaptive controller then is synthesized systematically using a backstepping technique. This controller is designed to accomplish the ultimate boundedness of the closed-loop helicopter dynamics and accommodate agile flight maneuvers. Two nonlinear simulations on hovering and trajectory tracking are conducted to show the effectiveness and merit of the proposed controller, which is also shown to be superior by performance comparison with a well-known neural-network controller.

Adaptive Decoupling Predictive Temperature Control Using Neural Networks for Extrusion Barrels in Plastic Injection Molding Machines

Abstract: In the paper, an adaptive decoupling predictive temperature control using neural networks (NN) is presented for extrusion barrels in plastic injection molding machines. Due to weakly coupling effects, the extrusion barrels are approximated by decoupling linear system models together with independent NN models. These decoupling system parameters and NN models are experimentally determined using the recursive least-squares estimation (RLSE) approach with forgetting factor. The adaptive decoupling predictive PI control laws together with NN compensating terms are developed by minimizing a generalized predictive cost function. A real-time control algorithm is then proposed to achieve temperature control of extrusion barrels. Experimental results on a laboratory-built extrusion barrel are conducted to illustrate the usefulness and applicability of the proposed method are well exemplified by conducting.

Intelligent Adaptive Motion Control Using Fuzzy Basis Function Networks for Electric Unicycle

Abstract: This paper presents two intelligent adaptive controllers, called self-balancing and speed controllers, for self-balancing and motion control, respectively, of an electric unicycle using fuzzy basis function networks (FBFN), which are employed to approximate model uncertainties and unknown friction between the wheel and the terrain surface. Both controllers are established based on the linearized model of the vehicle whose model uncertainties and parameter variations are caused by different riders and terrain. An adaptive backstepping controller together with online learning FBFN and sensing information of the rider's body inclination then is presented to achieve self-balancing motion control. By adding an electronic throttle as the input device of speed commands, a decoupling sliding-mode controller with online learning FBFN is proposed to accomplish self-balancing and speed control. The performance and merit of the two proposed control methods are exemplified by conducting four simulations and three experiments on a laboratory-built electric unicycle.

Cooperative exploration of networked multi-robot systems using minimal information entropy

Abstract: Exploring an unknown environment for a multi-robot system (MRS) is the problem of controlling a team of robots over all points of a given region in an efficient and safety manner. In this paper a cooperative exploration approach is developed by using a robot-sensor-network (RSN) and communication system and the technique of minimal information entropy. The exploration strategy is able to be divided by two stages: collection and coverage stage. In the first stage, considering the neighbors of the ith robot of the MRS, a grid-based approach incorporating with a potential approach is proposed for covering the maximal area in the sense of balance force. Furthermore, in the second stage, a spiral approach incorporating with the nonholonomic trajectory tracking control design is employed. Theoretical proof shows the stability of the proposed cooperative exploration method. Finally, simulations are also conducted to show the effectiveness of the proposed approach.

Editorial Message: Special Section on Fuzzy Theory and Its Applications

Abstract: As the guest editors of the special section on fuzzy theory and its applications, we are very pleased to express short remarks on this very section, which is aimed to publish the latest, innovative, and outstanding research results in the International Journal of Fuzzy Systems (IJFS) in a very short review period. This section was called for participation in the 2014 international conference on fuzzy theory and its applications (iFUZZY 2014) held in Kaohsiung, Taiwan, over November 26–28, 2014, and 13 papers were submitted to the special IJFS session. They were initially reviewed and accepted as the oral presentations by the two guest editors. By following the rigorous paper review procedure of IJFS, we invited four IJFS associate editors, Prof. Shun-Feng Su (the current Editor-in-Chief of IJFS), Prof. Chin-Wang Tao, Prof. Chia-Feng Juang, and Prof. Ching-Chih Tsai, as the referees to raise questions and make comments for all the oral presentations in iFUZZY 2014. No-show papers are automatically rejected for possible publication in this special section. Based on the three review criteria on technical contributions, novelty, and completeness, six papers were provisionally accepted and asked for quality improvements according to the reviewers’ comments and suggestions. These six papers have finally been accepted for publication in the IJFS special section on fuzzy theory and its applications after substantial and detailed revisions. By looking into presented contents of these six papers in the special section, we are very delightful to make brief introductions to their contributions and novelty on fuzzy theory and its applications. These articles provide new, interesting, and timely useful results covering L-E-fuzzy lattices, fuzzy inference-enhanced VC-DRSA model for technical analysis in investment decision aid, optimal fuzzy controller design using an evolutionary strategy based on particle swarm optimization for redundant wheeled robots, improving mobile commerce adoption using a new hybrid fuzzy MADM model, performance of soft computing controllers in hemodialysis machines, and a fuzzy-neural adaptive terminal iterative learning control for fed-batch fermentation processes. All the six papers are collected together in the current issue in order to illustrate the main technical achievements of the 2014 IJFS special session in iFUZZY 2014. Last but not least, this editorial message not only delineates the paper submission, rigorous review, and quality assurance of the 2014 IJFS special session papers in detail, but also significantly encourages your kind paper submissions to the 2015 IJFS special session in iFUZZY 2015. We look forward to receiving your paper submissions this year. Guest Editors, 2014 IJFS special section on fuzzy theory and its applications

Adaptive intelligent steering control of ball-riding human transporter

Abstract: This paper presents an adaptive intelligent steering controller using backstepping sliding-mode control and recurrent wavelet fuzzy cerebellar model articulation controller (RWFCMAC) and for an uncertain ball-riding human transporter in presence of significant system uncertainties. The proposed controller operates at two independent modes: self-balancing and station keeping. The self-balancing mode is used to balance by following the rider's two-dimensional inclination angles, while the station-keeping mode is aimed to permit the rider to keep the vehicle at a target position. The RWFCMAC is designed to online learn the uncertainties caused by riders' weights and different parameters. The superior performance and merit of the proposed control methods are well exemplified by conducting two simulations.

Interval Type-2 Fuzzy Maximum Power Point Tracking Control for Wind Power Buck Coversion Systems

Abstract: This paper develops a PI-like interval type-2 (IT2) fuzzy maximum power point tracking (MPPT) control method for a class of wind power generation and battery charging systems with DC/DC buck converters. After the brief model descriptions of the wind power turbine, generator and DC/DC buck converter, the proposed MPPT controller is designed using IT2 fuzzy control logics and its asymptotical stability is discussed. The effectiveness and merit of the proposed MPPT controller are exemplified by conducting several simulations via Matlab/Simulink and three experiments. The proposed controller is shown superior by comparing to a conventional PI MPPT controller and a PI-like type-1 fuzzy MPPT controller.

Interval type-2 fuzzy maximum power point tracking for solar photovoltaic power charging systems

Abstract: This paper presents an interval type-2 fuzzy maximum power point tracking (IT2FMPPT) control method for a solar photovoltaic (PV) power generation systems with a DC/DC buck converters and a battery charging system. After the brief model descriptions of the solar PV power charging system, the maximum power and its optimal voltage of the system at different illuminations are numerically solved using a Newton-Raphson method. Via the DC/DC buck converter and the current feedback control technique, the proposed MPPT controller is synthesized using PI control and IT2 fuzzy control logics. The asymptotical stability of the overall closed-loop system with the proposed IT2FMPPT controller is investigated in some details. The effectiveness and merit of the proposed IT2FMPPT controller are exemplified by conducting simulations via Matlab/Simulink. Through comparative simulations, the proposed IT2FMPPT controller is shown superior in comparison with a conventional PI MPPT controller and a conventional fuzzy MPPT controller.

Nonlinear self-balancing and speed control using WFCMAC for seatless electric unicycles

Abstract: This paper presents an direct adaptive control for an uncertain electric seatless unicycle using wavelet fuzzy cerebella model articulation controller (WFCMAC). The WFCMAC is proposed to approximate the equivalent control part and uncertainties. A nonlinear backstepping sliding-mode controller with on-line adaptive laws is presented to accomplish adaptive robust self-balancing and speed control of the seatless electric unicycle based on the rider's body inclination and the speed of the unicycle. Simulations results indicate that the performance and merit of the proposed method are well illustrated by comparing to two existing controllers.