• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.437-440
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• 1998

In this paper, the design method of fuzzy neural network(FNN) controller using indirect adaptive control technique is presented for controlling chaotic nonlinear systems. Firstly, the fuzzy model identified with a FNN in off-line process. Secondly, the trained fuzzy model tunes adaptively the control rules of the FNN controller in on-line process. In order to evaluate the proposed control method, Indirect adaptive control method is applied to the representative continuous-time chaotic nonlinear systems, that is, the Duffing system and the Lorenz system. Simulations are done to verify the effectivencess of controller.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1002-1007
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• 2014

This paper deals with the adaptive backstepping hovering control for a quadrotor with model parameter uncertainties. In this paper, the backstepping based technique is utilized to design a nonlinear adaptive controller which can compensate for the motor thrust factor and the drag coefficient of a quadrotor. First, the quadrotor nonlinear dynamics is derived using Newton-Euler formulation. In particular, we use the ${\pi}/4$ shifted coordinate for x- and y-axis of a quadrotor. Second, an adaptive backstepping based attitude and altitude tracking control method is presented. The system stability and the convergence of tracking errors are proven using the Lyapunov stability theory. Finally, the simulation results are given to verify the effectiveness of the proposed control method.

• Journal of Power Electronics
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• v.14 no.3
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• pp.549-563
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• 2014

In this paper, the speed regulation problem of permanent synchronous motor (PMSM) systems under the vector control framework is studied. A model reference adaptive controller (MRAC) based on the Lyapunov stability theory is first designed. Since the standard MRAC method provides poor disturbance rejection performance in the case of strong disturbances, a composite control method which combines the MRAC method and the disturbance estimation method, called the MRAC+ESO method, is proposed. An extended state observer (ESO) is introduced to estimate the lumped disturbances. The obtained estimated value acts as a feedforward compensation term to the MRAC controller. A stability analysis of the composite control method is given. Simulation and experimental results are presented and compared to show the effectiveness of the proposed control method.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.741-746
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• 2004

We propose a new technique to the design and real-time implementation of an adaptive controller for robotic manipulator based on digital signal processors in this paper. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved direct Lyapunov method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot consisting of two 4-d.o.f. robots at the joint space and cartesian space.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.154-161
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• 1999

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.79-84
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C31) for robotic manipulators to achieve trajectory tracking by the joint angles. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.5 no.4
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• pp.26-37
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve trajectory tracking by the joint angles Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed control scheme adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller. feedback controller. and PID type time-varying auxiliary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require a an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.78-82
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• 1986

In the process control applications of self tuning control, a major concern of the control problem is to handle an offset caused by load disturbances and random steps occuring at random instance of time. Conventionally an integrator is incorperated in the forward path of the controller to eliminate such an offset. But this approach causes a difficulty if the adaptive part of the resultant controller is to be evaluated. In this paper a method of analyzing the adaptive system and improving the offset effect is suggested for a class of referance model method in the self tuning adaptive control system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.420-420
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• 2000

In this paper, we propose an adaptive controller using RBFN(radial basis function network) for robot manipulators The structure of the proposed controller consists of a RBFN and VSC-1 ike control. RBFN is used in order to approximate かon system, and VSC-like control to guarantee robustness On the basis of the Lyapunov stability theorem, we guarantee the stability for the total system. And the learning law of RBFN is established by the Lyapunov method, Finally, we apply the proposed controller to tracking control for a 2 link SCARA type robot manipulator.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.51-55
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• 1995

An adaptive unified predictive control (UPC) algorithm is applied to a batch polymerization reactor for poly(methyl methancrylate) (PMMA) and the effects of controller parameters are investigated. Computational studies are performed for a batch polymerization system model developed in this study. A transfer function in parametric form is estimated by recursive least squares (RLS) method, and the UPC algorithm is implemented to control the reactor temperature on the basis of this transfer function. The adaptive unified predictive controller shows a better performance than the PID controller for tracking set point changes, especially in the latter part of reaction course when gel effect becomes significant. Various performance can be acquired by selecting adequate values for parameters of the adaptive unified predictive controller; in other words, the optimal set of parameters exists for a given set of reaction conditions and control objective.