• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.504-507
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• 1997

It is well known that Variable Structure Controller(VSC) is robust to parameters variation and disturbance but its performance depends on the design parameters such as switching gain and slope of sliding surface. This paper proposes a more robust VSC that is composed of local VSC's. Each local VSC considers the local system dynamics with narrow parameter variation and disturbance. First we optimize the local VSC's by use of Evolution Strategy, and next we use Artificial Neural Network to generalize the local VSC's and construct the overall VSC in order to cover the whole range of parameter variation and disturbance. Simulation on BLDC motor current control shows that the proposed VSC is superior to the conventional VSC.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.4
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• pp.167-175
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• 2001

This paper presents design of active roll controller of a vehicle and experimental study using the electric actuating roll control system. Firstly, parameter sensitivity analysis is performed based on 3DOF linear vehicle model. The controller is designed in the frame work of gain-scheduled H$\infty$ control scheme considering the varying parameters induced by laden and running vehicle condition. In order to investigate a feasibility of an active control system, experimental work is performed using hardware-in-the -loop setup which has been constructed by the devised electric actuating system and the full vehicle model with tire characteristics. The performance is evaluated by experiment using hardware-in-the -loop simulation under the conditions of some steer maneuvers and parameter variations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1245-1247
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• 2014

This paper presents a robust position tracking controller for motor-driven flexible joint manipulators using only the motor angle measurement. The control problem is not easy because the link position is hard to estimate in the presence of parameter uncertainties. The proposed controller consists of a feedback linearization controller (FLC) and two proportional-integral observers (PIOs) that estimate both system states including the link position and an equivalent disturbance for compensating the parameter uncertainties. Comparative computer simulations are conducted to demonstrate the effectiveness of the proposed control algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.4
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• pp.129-136
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• 2002

This paper presents a $H_{\infty}$ robust controller with parameter estimation in polynomial approach. For good performance of a uncertain system, the parameters are estimated by RLS algorithm. The controller minimizes the sum of $H_{\infty}$ norm between sensitivity function and complementary sensitivity function by employing the Youla parameterization and polynomial approach at the same time. A numerical example and its simulation results are given to show the validity of the proposed controller.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.426-429
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• 1989

The robust self-tuning controller is designed which is guaranteed the asymptotic regulation and tracking in the presence of a bounded parameter perturbation. The global stability in the presence of a finite noise and disturbance is ensured. The controller has a error driven structure, and involves the common model of a disturbance and reference input in the internal model. The adaptive system tunes the controller parameters such that the quadratic performance index which involves a weighting factor is optimized.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.301-305
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• 1996

$\mu$ theory can handle the parametric uncertainty and produces more non-conservative controller than H$_{\infty}$ control theory. However an existing solution of the theory, D-K iteration, creates a controller of huge order and cannot handle the real or mixed real-complex perturbation sets. In this paper, we use genetic algorithms to solve these problems of the D-K iteration method. The Youla parameterization is used to obtain all stabilizing controllers and the genetic algorithms determines the values of the state feedback gain, the observer gain, and Q parameter to minimize $\mu$, the structured singular value, of given system. From an example, we show that this method produces lower order controller which controls a real parameter-perturbed plant than D-K iteration method.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.452-455
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• 1997

It is well known that Variable Structure Controller(VSC) is robust to parameters variation and disturbance but its performance depends on the design parameters such as switching gain and slope of sliding surface. This paper proposes a more robust VSC that is composed of local VSC's. Each local VSC considers the local system dynamics which has narrow parameter variation and disturbance. We optimize the local VSC's by use of Evolution Strategy, and then we use Artificial Neural Network to generalize the local VSC's and construct the overall VSC in order to cover the whole range of parameter variation and disturbance. Simulation on BLDC motor current control has verified the proposed VSC is superior to the conventional VSC.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.10 s.253
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• pp.1249-1254
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• 2006

Existing adaptive observers may cause the parameter drifts due to disturbances even if state estimation errors remain small. To avoid the drift phenomena in the presence of bounded disturbances, several robust adaptive observers have been introduced addressing bounds in state and parameter estimates. However, it is not easy for these observers to manipulate the size of the bounds with the selection of the observer gain. In order to reduce estimation errors, this paper introduces the (equation omitted) gain minimization problem in the adaptive observer structure, which minimizes the (equation omitted) gain between disturbances and estimation errors. The stability condition of the adaptive observer is reformulated as a linear matrix inequality, and the observer gain is optimally chosen by solving the convex optimization problem. The estimation performance is demonstrated through a numerical example.

• Journal of the military operations research society of Korea
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• v.28 no.1
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• pp.47-66
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• 2002

Instead of using signal-to-noise ratio, we attempt to optimize both the mean and variance responses using dual response optimization technique. The alternative experimental strategy analyzes a robust parameter design problem to obtain the best settings that give a target condition on the mean while minimizing its variance. The mean and variance are treated as the two responses of interest to be optimized. Unlike to the crossed array and combined array approaches, our experimental setup requires replicated runs for each control factor's treatment under noise sampling. When the postulated response models are true, they enable the coefficients to be estimated and the desired performance measure to be analyzed more efficiently. The procedure and illustrative example are given for the dual response optimization techniques of nonlinear goal programming.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.60-66
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• 1996

Brushless DC(BLDC) motor have been increasingly used in machine tools and robotics applications due to the reliability and the efficiency. In control of BLDC motor, it is important to construct the controller which is robust to parameter variations and external disturbances. Variable structure controller(VSC) has been known as a powerful tool in robust control of time varying systems. In practical systems, however, VSC has a high frequency chattering which deteriorates system performances. In this paper, a binary controller(BC) which takes the form of VSC and MRAC combined is presented to solve this problem. BC consists of the primary loop controller and the external loop controller to change the gain of primary loop controller smoothly. So it can generate the continuous control input and is insensitive to parameter variations in the given domain. To confirm the validity, various investigations of control characteristics for various design parameters in a position control system of BLDC motor are carried out. (author). 11 refs., 18 figs., 1 tab.