• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.754-758
• /
• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using Digital signal processors for robot manipulators. TMS320C50 is used in implementing real-time adaptive control algorithms to provide advanced performance for robot manipulator, In this paper, an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. Parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm, and controller parameters we determined by the pole-placement method. Performance of self-tuning adaptive controller is illusrated by the simulation and experiment for a SCARA robot.

• Journal of the Korean Society for Precision Engineering
• /
• v.18 no.11
• /
• pp.58-66
• /
• 2001

GPC has been reported as a useful self-tuning control algorithm for systems with unknown time-delay and parameters. GPC is easy to understand and implement, and thus has won popularity among many practicing engineers. Despite its success, GPC does not guarantee is nominal stability. So, in this paper, GPC is rederived in frequency domain instead of in the time domain to guarantee its nominal stability. Derivation of GPC in frequency domain involves spectral factorization and Diophantine equation. Frequency domain GPC control law is stable because the zeros of characteristic polynomial are strictly Schur. Recursive least square algorithm is used to identify unknown parameters. To see the effectiveness of the proposed controller, the controller is simulated for a numerical problem that changes in dead-time, in order and in parameters.

• The Journal of Korea Robotics Society
• /
• v.3 no.2
• /
• pp.146-153
• /
• 2008

본 논문에서는 인간 운동 제어 이론과 기계학습을 기반으로 하여 로봇의 접촉 작업 수행을 위한 새로운 운동 학습 전략을 제시하였다. 성공적인 접촉 작업 수행을 위한 본 연구의 전략은 강화학습 기법을 통하여 최적의 작업 수행을 위한 임피던스 매개 변수를 찾는 것이다. 본 연구에서는 최적의 임피던스 매개 변수를 결정하기 위하여 Recursive Least-Square (RLS) 필터 기반 episodic Natural Actor-Critic 알고리즘이 적용되었다. 본 논문에서는 제안한 전략의 효용성을 증명하기 위해 동역학 시뮬레이션을 수행하였고, 그 결과를 통하여 접촉작업에서의 작업 최적화 및 환경이 가지는 불확실성에 대한 적응성을 보여 주었다.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.6
• /
• pp.1453-1462
• /
• 1996

A numerical simulation is carried out on the directiveity synthesis of ultrasonic transducers by point source array. Directive method with combined LMS(Least-Mean-Square) method is practiced by means of a iterative method to realize the desired directivity. The directiviey of quasi-ideal beam with a beam width and a directive arbitrary specified was chosen. On the numerical resut, Proposed algorithm shows higher speed of clculating simulation than that of LMS method, and make adaptive control, which enables the desired directivity. Numerical simulations are carried out by PC(CPU:80486 DX2, RAM 16Mbyte).

• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.913-916
• /
• 1988

Indirect adaptive pole assignment PID controllers for unknown systems with time varying delay, is proposed. Unknown system parameters are estimated by recursive least square method, and time varying delay is estimated using indirect predictors. Since the order of parameter vectors didn't increase, the computational burden is not largely increased in spite of using indirect adaptive control method with time varying delay estimation. Computer simulation is performed to illustrate the efficiency of the proposed method.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.578-580
• /
• 1998

The paper presents an approach to the structure identification based on the CART (Classification And Regression Tree) algorithm and to the parameter identification by hybrid learning method in neuro-fuzzy system. By using the CART algorithm, the proposed method can roughly estimate the numbers of membership function and fuzzy rule using the centers of decision regions. Then the parameter identification is carried out by the hybrid learning scheme using BP (Back-propagation) and RLSE (Recursive Least Square Estimation) from the numerical data. Finally, we will show it's usefulness for fuzzy modeling to truck backer upper control.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.734-736
• /
• 1998

In this paper we obtain a discrete mathmatical model of a Gas turbine control system from experimental data. we find appropriate input signal and parameter estimation algorithm for identification of the gas turbine control system. Under these conditions experimental data are collected from real system and parameters are estimated by the recursive least square algorithm. The computer simulation results show that the proposed experimental procedure is appropriate for the identification of the gas turbine control system. The model validation is excuted by real data from the Gunsan Gas Turbine Power Plant.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.24 no.2
• /
• pp.280-291
• /
• 1987

In this paper, the linear autoregressive model of EMG signal for four basic arm functions was presented and parameters for each function were estimated. The signal identification was carried out using function discrimination algorithm. It was validated that EMG signal was a widesense stationary process and the linear autoregressive model of EMG signal was constructed through approximating it to Gaussian process. It was confined that Levinson-Durbin algoridthm is a more appropriate one than the recursive least square method for parameter estimation of the linear model. Optimal function discrimination was acquired when sampling frequency was 500Hz and two electrodes were attached to bicep and tricep muscle, respectively. Parameter values were independent of variance and the number of minimum data for function discrimination was 200. Bayesian discrimination method turned out to be a better one than parallel filtering method for functional discrimination recognition.

• Proceedings of the KIPE Conference
• /
• 2018.07a
• /
• pp.240-242
• /
• 2018

본 논문은 배터리 등가 회로 모델의 파라미터를 실시간으로 추정 가능한 온라인 업데이트 알고리즘을 비교 분석한다. 온라인 업데이트 알고리즘은 EKF (Extended Kalman Filter)와 RLS (Recursive Least Square)를 기반으로 구성되며 Li-ion 배터리를 이용한 실험 결과를 통하여 각 알고리즘의 연산량, 정확도 등을 분석한다. 분석된 결과를 바탕으로 적용 시스템의 환경과 특성에 따라 적합한 온라인 업데이트 알고리즘을 제안한다.

• Management & Information Systems Review
• /
• v.3
• /
• pp.201-214
• /
• 1999

Adaptive correlator receiver with neural network based on complex multilayer perceptron is persented for suppressing interference of narrow-band of direct spread spectrum communication systems. Recursive least square algorithm with backpropagation error is used for fast convergence and better performance in adaptive correlator scheme. According to signal noise and transmission power, computer simulation results show that bit error ratio of adaptive correlator using neural network improved that of adative transversal filter of direct sequence spread spectrum considering of jamming and narrow-band interference. Bit error ratio of adaptive correlator with neural network is reduced about 10-1 than that of adaptive transversal filter where interference versus signal ratio is 5dB.