• 제어로봇시스템학회:학술대회논문집
• /
• 1998.10a
• /
• pp.519-524
• /
• 1998

This paper presents a robust control scheme of free-joint manipulators to overcome actuator failures and uncertain-ties in Cartesian space where tasks are planned. The control scheme has the adaptation law for the upper bound on the norm of uncertainties through the Lyapunov function approach. To solve the dynamic singularity problem in the controller, the singular and nonsingular regions are investigated based on a computer simulation. Then a singularity-free Cartesian trajectory planning is achieved in order to guarantee the availability of the control scheme. To illustrate the validity of the proposed control scheme, simulation results for a three-link planar robot arm with a free joint are shown.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.3
• /
• pp.46-54
• /
• 1997

In this paper, we psropose the feedback linearization technique for a nonlinear system using genetic algorithms (GAs) and fuzzy logic system. The proposed control scheme approximates the nonlinear term of a nonlinear system using the fuzzy logic system and computes the control input for cancelling the nonlinear term. Then in the fuzzy logic system, the number and shape of membership function of the premise aprt will be tuned to minimize the control error boundary using GAs. And the parameters of the consequence of fuzzy rule will be tuned by the adaptive laws based on lyapunov stability theory in order to guarantee the closed loop stability of control system. The evolution of fuzzy logic system is processed during the on-line adaptive control. The effectiveness of proposed method will be demonstrated by computer simulation of simple nonlinear sytem.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.13 no.1
• /
• pp.1-11
• /
• 1988

In this paper, the boundedness of adjustable parameters for the model reference adaptive control(MRAC) system using a constant reference signal is discussed. This analysis is motivated by that it is possibel to verify the existence, boundedness and bounded range of the parameter as well as the stability of the adaptive system with an alternative propoerty of Lyapunov function. For two adaptive laws; a general gradient mothod(GGM) and a least square method(LSM), unique solution set in parameter space can be estabilished by a new approach suggeste here. Computer simulation results to show the effect of parameter space analysis are also examined.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.51 no.2
• /
• pp.83-91
• /
• 2002

This paper presents a new speed and position sensorless control method of permanent magnet synchronous motors based on the sliding mode observer. Since the parameter of the dynamic equation such as machine inertia or viscosity friction coefficient are not well known and these values can be easily changed generally during normal operation, there are many restrictions in the actual implementation. The proposed adaptive sliding mode observer applies adaptive scheme so that observer may overcome the problem caused by using the dynamic equation. Furthermore, using the Lyapunov Function, the adaptive sliding mode observer can estimate rotor speed as well as stator resistance. The feasibility of the Proposed observer is verified cia the experiments.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.3 no.1
• /
• pp.113-120
• /
• 1999

In this paper, chaotic signals of a Chua's oscillator are effectively controlled to low periodic signal(1-periodic signal, 2-periodic signal, etc) or equilibrium point using the linear state feedback technique. The proposed linear state feedback technique has characteristics, that any solution of the Chua's oscillator can be a goal of the control(fixed point, periodic orbit, etc). The controller has a very simple structure, which does not require adjusting system parameters.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.199-204
• /
• 2006

In this paper, we propose an adaptive control algorithm to improve the position accuracy and reduce the nonlinear friction effects for linear motion servo system. Especially, the considered system includes not only the variation of the mass of the mover but also the friction change by the normal force. To adapt to these problems, we designed the controller with the mass estimator and the compensator by observing the variation of normal force. Finally, the numerical simulation results are presented in order to show the effectiveness of the proposed method to improve the position accuracy compared to other control methods.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2003.06a
• /
• pp.183-187
• /
• 2003

When a flexible arm is rotated by a motor about an axis through the arm's fixed end, transverse vibration may occur. The motor torque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible arm so that it is arrested at the end of rotation. In this paper, we propose nonlinear observer for one-link flexible am. Then based on the error dynamic equation between the plant dynamic equation and the nonlinear observer dynamic equation of the flexible one-link am, Lyapunov candidate function is applied to achieve a stable deterministic nonlinear feedback controller for the regulation of joint angle.

• Proceedings of the KIEE Conference
• /
• 1999.07g
• /
• pp.2992-2994
• /
• 1999

This paper investigates the direct adaptive control of nonlinear systems using RBFN(radial basis function networks). The structure of the controller consists of a fixed PD controller and a RBFN controller in parallel. An adaptation law for the weight adjustment is developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Also, the tracking errors between the system outputs and the desired outputs converge to zero asymptotically. To evaluate the performance of the controller, the proposed method is applied to the trajectory control of the two-link manipulator.

• Proceedings of the KIEE Conference
• /
• 1996.11a
• /
• pp.386-388
• /
• 1996

This paper presents the learning controller for robot manipulators to track the desired trajectory exactly. The learning controller, based on the Lyapunov theory, consists of a fixed PD action and a repetitive action for the purpose of feedforward compensation which is adjusted utilizing a linear combination of the velocity and position errors. The learning controller Is often used In case of the desired trajectories are periodic tasks, and has advantage that it periodically converges to zero even if we don't know the exact dynamic parameters. In this paper, we show that the position and velocity errors of robot manipulators converge to zero sa time goes infinite for the input is periodic function and show a good trajectory tracking performance In the cartesian space.

• Proceedings of the KIEE Conference
• /
• 1996.11a
• /
• pp.58-61
• /
• 1996

For a class of single-input single-output continuous-time nonlinear systems, a multilayer neural network-based controller that feedback-linearizes the system is presented. Control action is used to achieve tracking performance for a state-feedback linearizable but unknown nonlinear system. The multilayer neural network(NN) is used to approximate nonlinear continuous function to any desired degree of accuracy. The weight-update rule of multilayer neural network is derived to satisfy Lyapunov stability. It is shown that all the signals in the closed-loop system are uniformly bounded. Initialization of the network weights is straightforward.