Showing papers by "Ching-Chih Tsai published in 2012"

Backstepping aggregated sliding-mode motion control for automatic 3D overhead cranes

Abstract: This paper presents novel methodologies for modeling and backstepping aggregated sliding-mode motion control of a 3D overhead crane. Lagrangian mechanics is adopted to establish a mathematical model of the system with frictions. A backstepping sliding-mode control method is used to maintain the nutation angle at zero and achieve trajectory tracking simultaneously. The effectiveness and merit of the proposed controller are exemplified by conducting several simulations on a real 3D overhead crane.

LQR motion control of a ball-riding robot

Abstract: The paper presents techniques and design methodologies for modeling and LQR motion control of a ball-riding robot driven by three omnidirectional wheels. A completely dynamic model of the robot moving on a flat terrain is derived using Lagrangian mechanics. Two LQR controllers are synthesized to achieve station keeping and point stabilization. Through computer simulations and experimental results, both proposed controllers together with the built ball-riding robot are successfully shown to give a satisfactory control performance.

Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

Abstract: This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).

Intelligent backstepping sliding-mode control using recurrent interval type 2 fuzzy neural networks for a ball robot with a four-motor inverse-mouse ball drive

Abstract: This paper presents an intelligent adaptive backstepping sliding-mode control using recurrent interval type 2 fuzzy neural networks (RIT2FNN) for motion control of a ball robot with a four-motor inverse mouse-ball driving mechanism actuated by four independent brushless motors simultaneously. The RIT2FNN is used to on-line learning the uncertain part during the controller synthesis. An adaptive backstepping sliding-mode control together with RIT2FNN is proposed to accomplish robust self-balancing, position control and trajectory tracking of the robot in the presence of mass variations, viscous and Coulomb frictions. Computer simulations are conducted for illustration of the effectiveness of the proposed control method.

Self-tuning PID control using recurrent wavelet neural networks

Abstract: This paper presents a novel self-tuning PID control using recurrent wavelet neural networks (RWNN-PID) for a class of highly nonlinear discrete-time time-delay systems. The three-term parameters of the self-tuning PID controller are tuned based on the RWNN, in order to achieve setpoint tracking and eliminate any error caused by step disturbances. Numerical simulations for controlling two highly nonlinear process show disturbance rejection and setpoint tracking performance of the proposed control method, thus clearly indicating effectiveness and merit of the proposed method.

Cascaded fuzzy-PID control using PSO-EP algorithm for Air Source Heat Pumps

Abstract: Heat pump systems are suitable for most commercial air-conditioning and refrigeration applications Currently, heat pumps can be regarded as products that would help satisfy the air-conditioning system demands for medium and small sized buildings, thereby reducing electric power demand peaks in summer and achieving save energy in general This paper presents a cascaded fuzzy-PID (proportionalintegral-derivative) controller for a class of Air Source Heat Pump Systems (ASHPS) The controller's parameters are offline optimally selected by a combination of the well-known particle swarm optimization (PSO) and evolutionary programming (EP) algorithm This proposed cascaded fuzzy-PID control structure retains the best merits of both fuzzy control and cascade control structures The cascade control strategy is shown effective for such a system, and a fuzzy control strategy is shown capable of dealing with systems without accurate models The performance of the proposed cascaded fuzzy-PID control is compared to that of a pure PID control strategy, and the results reveal that the cascade fuzzy-PID control strategy gives superior performance, reduced transient performance and smaller temperature overshoots

Intelligent sliding-mode motion control using fuzzy wavelet networks for automatic 3D overhead cranes

Abstract: This paper develops novel methodologies for modeling and designing an intelligent sliding-mode motion control of an automated 3D overhead crane. Lagrangian mechanics is used to establish a mathematical model of the crane which involved uncertain parameters. Intelligent sliding mode control using fuzzy wavelet neural networks and backstepping are used to propose a motion controller so as to maintain the nutation angle less than 4 degrees and achieve position control simultaneously. The robust performance and merit of the proposed controller are exemplified by conducting several simulations on the 3D overhead crane with actual crane parameters under three different loading conditions.

Intelligent backstepping sliding-mode control using recurrent interval type 2 fuzzy neural networks for a ball-riding robot

Abstract: This paper presents an intelligent backstepping sliding-mode control using recurrent interval type 2 fuzzy neural networks (RIT2FNN) for motion control of a ball-riding robot. After brief description of the dynamic model of the robot with viscous and Coulomb frictions, a backstepping sliding-mode control using hierarchical aggregated sliding control method and RIT2FNN is proposed to accomplish robust trajectory tracking of the robot in the presence of mass variations, terrain-dependent viscous and Coulomb frictions. Computer simulations are conducted to illustrate the effectiveness of the proposed control method.

Adaptive H∞ nonlinear velocity tracking using RBFNN for linear DC brushless motor

Abstract: This article presents an adaptive H ∞ nonlinear velocity control for a linear DC brushless motor. A simplified model of this motor with friction is briefly recalled. The friction dynamics is described by the Lu Gre model and the online tuning radial basis function neural network (RBFNN) is used to parameterise the nonlinear friction function and un-modelled errors. An adaptive nonlinear H ∞ control method is then proposed to achieve velocity tracking, by assuming that the upper bounds of the ripple force, the changeable load and the nonlinear friction can be learned by the RBFNN. The closed-loop system is proven to be uniformly bounded using the Lyapunov stability theory. The feasibility and the efficacy of the proposed control are exemplified by conducting two velocity tracking experiments.

Two-degree-of-freedom control using recurrent fuzzy neural networks for a class of nonlinear discrete-time time-delay systems

Abstract: This paper presents a novel two-degrees-of-freedom control for a class of nonlinear discrete-time time-delay systems. The controller combines a TSK-type recurrent fuzzy neural network (TRFNN) adaptive inverse model feedforward controller with a stochastic adaptive model reference predictive controller (SAMRPC). The former is used to provide command-feedforward control and to improve transient performance, while the SAMRPC controller is employed to eliminate any error caused by disturbances or uncertainties. Numerical simulations for controlling a highly nonlinear process reveal disturbance rejection and set-point tracking performance of the proposed control method. The results clearly indicate effectiveness and merit of the proposed method.

Intelligent adaptive steering control for electric unicycles

Abstract: This paper presents an intelligent adaptive steering control using linear quadratic regulation (LQR) approach and fuzzy cerebella model articulation control (CMAC) method for an electrical unicycle. The fuzzy CMAC is employed to on-line learn unknown frictions between the wheel and the terrain surfaces. The LQR approach is used to design a state feedback controller, in order to simultaneously achieve self-balancing and velocity control for the unicycle with different riders. The performance and merit of the proposed method are well exemplified by conducting simulations on a laboratory-built electric unicycle.