• 전기학회논문지
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• 제65권12호
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• pp.2023-2029
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• 2016

This paper addresses a robust controller design technique for a nonlinear underwater glider with disturbances. We consider the buoyancy and pitching moment as control inputs, which generate additional nonlinearity on the plant dynamics. To deal with the nonlinearity, we utilize the optimal linearization technique. The conditions for the optimal linearization and the controller design are formulated in terms of matrix inequalities. The effectiveness of the proposed method is demonstrated through a simulation.

• 대한전기학회논문지
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• 제45권4호
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• pp.590-595
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• 1996

The robust control technique is generally the iterative design method to determine a robust control for perturbed system with prescribed range of perturbation based on the robust stability measure. However, robot manipulator has the structured pertubation and the unstructured one. This paper proposes the robust technique for designing controller such that the trajectory of end-effector of robot manipulator tracks asymptotically the desired trajectory for all allowable variations in the manipulator's parameter. For satisfying asymptotical stability though we can not know the bound of perturbations and the parameter variations, the relation between the unknown parameter and the parameter of nominal system can be derived from Krasovskii theorem and we construct the new robust control using that relation. (author). 12 refs., 6 figs.

• 대한전기학회논문지
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• 제43권7호
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• pp.1186-1192
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• 1994

This paper presents an algorithm to design a robust digital model following servo control system in which optimal linear quadratic regulator problem is used to design the control system that make the step/ramp response of the plant kept close to a specified ideal step/ramp response of the model. The quadratic criterion function for a continuous system is used to design the robust digital servo control system. The feasibility of the design technique is shown by the simulation and the proposed method is applied to the speed control of DC servo motor.

• Journal of Mechanical Science and Technology
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• 제20권10호
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• pp.1662-1669
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• 2006

Since randomness and uncertainties of design parameters are inherent, the robust design has gained an ever increasing importance in mechanical engineering. The robustness is assessed by the measure of performance variability around mean value, which is called as standard deviation. Hence, constraints in robust optimization problem can be approached as probability constraints in reliability based optimization. Then, the FOSM (first order second moment) method or the AFOSM (advanced first order second moment) method can be used to calculate the mean values and the standard deviations of functions describing constraints and object. Among two methods, AFOSM method has some advantage over FOSM method in evaluation of probability. Nevertheless, it is difficult to obtain the mean value and the standard deviation of objective function using AFOSM method, because it requires that the mean value of function is always positive. This paper presented a special technique to overcome this weakness of AFOSM method. The mean value and the standard deviation of objective function by the proposed method are reliable as shown in examples compared with results by FOSM method.

• 제어로봇시스템학회논문지
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• 제10권1호
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• pp.10-14
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• 2004

We present a predictive control algorithm combined with the robust robot control that is constructed on the Lyapunov min-max approach. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about the model, it is an important trend to design a robust control law that guarantees the desired properties of the manipulator under uncertain elements. In the preceding robust control work, we need to tune several control parameters in the admissible set where the desired stability can be achieved. By introducing an optimal predictive control technique in robust control we can find out much more deterministic controller for both the stability and the performance of manipulators. A new class of robust control combined with an optimal predictive control is constructed. We apply it to a simple type of 2-link robot manipulator and show that a desired performance can be achieved through the computer simulation.

• Journal of Advanced Marine Engineering and Technology
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• 제25권4호
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• pp.833-845
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• 2001

Quantitative Feedback Theory(QFT) has been used to design a robust power system stabilizer(PSS) to improve transient and dynamic stabilities of a power system. This design technique is basically accomplished in frequency domain. The most important feature of QFT is that it is able to deal with the design problem of complicated uncertain plants. A basic idea in QFT design is the translation of closed-loop frequency-domain specifications into Nichols chart domains specifying the allowable range of the nominal open-loop response and then to design a controller by using the gain-phase loop shaping technique. This paper introduces a new algorithm to compute QFT bounds more efficiently. The propose QFT design method ensures a satisfactory performance of the PSS under a wide range of power system operating conditions.

• 품질경영학회지
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• 제28권1호
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• pp.78-94
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• 2000

The ever increasing demands for enhanced competitiveness of engineered products require a "designing-in-quality" strategy that can effectively and efficiently incorporate concepts of uncertainty, quality, and robustness into design. Engineered design optimization approaches that are typically carried out with respect to a single objective become inadequate to address these multiple set of requirements. This paper presents a design metric for a multi-attribute robust design problem with designer′s preferences on the performance accuracy and the performance precision. The use of this design metric as the robust optimal design criterion in multi-stage experimentation and modeling technique is presented. The effectiveness of the overall design procedure and the performance of the proposed design metric are tested with the aid of IC design and the results are discussed.

• 대한기계학회논문집A
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• 제24권1호
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• pp.215-224
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• 2000

In order to reduce the variation effects of uncertainties in the engineering environments, new robust optimization method, which considers the uncertainties in design process, is proposed. Both design variables and system parameters are considered as random variables about their nominal values. To ensure the robustness of performance function, a new objective is set to minimize the variance of that function. Constraint variations are handled by introducing probability constraints. Probability constraints are solved by the advanced first order second moment (AFOSM) method based on the reliability theory. The proposed robust optimization method has an advantage that the second derivatives of the constraints are not required. The suggested method is examined by solving three examples and the results are compared with those for deterministic case and those available in literature.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국내학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.173-178
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• 1993

In this paper, we present an algorithmic technique for determining a feedback compensator which will stabilize the interval dynamic system, specifically, the robust regulator design for interval plants. The approach taken here is to allow the system parameters to live within prescribed intervals then design a dynamic feedback compensator which guarantees closed-loop system stable. The main contribution of this paper is the idea of introducing a "simplified Kharitonov's result" for low order polynomials to search for suitable compensator parameters in the compensator parameter space to make the uncertain syste robust. We also design the robust regulator which will D-stabilize (have the closed-loop poles in the left sector only) the dynamic interval system while having good performance. The nuerical examples are given to show the substantially improved robustness which results from our approach. approach.

• 산업경영시스템학회지
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• 제30권2호
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• pp.99-105
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• 2007

According to the automated manufacturing processes followed by the development of computer manufacturing technologies, products or quality characteristics produced on the processes have measured and recorded automatically. Much amount of data daily produced on the processes may not be efficiently analyzed by current statistical methodologies (i.e., statistical quality control and statistical process control methodologies) because of the dimensionality associated with many input and response variables. Although a number of statistical methods to handle this situation, there is room for improvement. In order to overcome this limitation, we integrated data mining and robust design approach in this research. We find efficiently the significant input variables that connected with the interesting response variables by using the data mining technique. And we find the optimum operating condition of process by using RSM and robust design approach.