• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.254-255
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• 2023

This paper discovers a robust managerial strategy for a stochastic inventory of perishable products, where the model experiences changing factors including inner parameters and an external disturbance with unknown form. An analytical solution for the optimization problem can be obtained by applying the Hamilton-Bellman-Jacobi equation, however the policy result cannot completely suppress the oscillation from the external disturbance. Therefore, an intelligent approach named Radial Basis Function Neural Networks is applied to estimate the unknown disturbance and provide a robust controller to manipulate the inventory level more effective. The final results show the outstanding performance of RBFNN controller, where both the estimation error and control error are guaranteed in the predefined limit.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1-4
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• 2004

The dual-mode strategy has been adopted in many constrained MPC methods. The size of stabilizable regions of states of MPC methods depends on the size of underlying feasible and positively invariant set and number of control moves. These results, however, could be conservative because the definition of positive invariance does not allow temporal leave of states from the set, In this paper, a concept of periodic invariance is introduced in which states are allowed to leave a set temporarily but return into the set in finite steps. The periodic invariance can defined with respect to sets of different state feedback gains. These facts make it possible for the periodically invariant sets to considerably larger than ordinary invariant sets. The periodic invariance can be defined for systems with polyhedral model uncertainties. We derive a MPC method based on these periodically invariant sets. Some numerical examples are given to show that the use of periodic invariance yields considerably larger stabilizable sets than the case of using ordinary invariance.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.540-543
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• 1999

This paper describes a speed sensorless algorithm for vector control system with compensated stator resistance and rotor time constant of induction motor using a model reference adaptive system(MRAS). The system are composed of two MRAC, one is a rotor speed estimation and a stator resistor identification by back-EMF observer, other is used to identify rotor time constant by magnetizing current observer, so that the estimation can be cover a very low speed range with a robust control. The suggest control strategy and estimation method have been validated by simulation study. In the simulation using Matlab/Simulik, the proposed speed sensorless vector control system are shown to operate very well in spite of variable rotor time constant and load fluctuation.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.411-413
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• 1992

This paper proposes a simple and high performance hybrid position/force control of robots based on disturbance compensation by using the disturbance observer in task coordinate space. The disturbance observer linealizes system of robot manipulators in task coordinate space and realizes acceleration control. To realize the strict acceleration control, the disturbance observer whose input is a position signal by simple computation, works as if it were a disturbance detector. The inverse kinematics can be simplified, because the disturbance observer in task coordinate space compensates not only the disturbance but also the error due to the simplification of the inverse kinematics. The new strategy is applied to a three-degrees-of freedom direct drive robot. The robust and simple hybrid position/force control is realized experimentally.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.3
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• pp.51-55
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• 2006

An adaptive tracking controller is presented for the vibration reduction of flexible manipulator employed in hazardous area by combining input shaping technique with sliding-mode control. The combined approach appears to be robust in the presence of severe disturbance and unknown parameter which will be estimated by least-square method in real time. In a maneuver strategy, it is found that a hybrid trajectory with a combination of low frequency mode and rigid-body mode results in better performance and is more efficient than the traditional rigid body trajectory alone which many researchers have employed. The feasibility of the adaptive tracking control approach is demonstrated by applying it to the simplified model of robot system. For the applications of the proposed technique to realistic systems, several requirements are discussed such as control stability and large system order resulted from finite element modeling.

• Journal of Institute of Control, Robotics and Systems
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• v.1 no.2
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• pp.88-93
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• 1995

This paper presents a study using neural networks in the design of the tracking controller of robotic systems. Our strategy is to put to use the available knowledge about the robot manipulator, such as estimation models, in the contoller design via the computed torque method, and then to add the neural network to control the remaining uncertainty. The neural network used here learns to provide the inverse dynamics of the plant uncertainty, and acts as an inverse controller. In the simulation study, we verify that the proposed neural network controller is robust not only to structured uncertainties, but also to unstructured uncertainties such as friction models.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.502-502
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• 2000

An input constrained receding horizon predictive control algorithm for uncertain systems with nonzero set points is proposed. For constant nonzero set points, models with uncertainty can be converted into an augmented incremental system through the use of integrators and the problem is transformed into a zero-state regulation problem for the incremental system. But the original constraints on inputs are converted into constraints on the sum of control inputs at each time Instants, which have not been dealt in earlier constrained robust receding horizon control problems. Recursive state bounding technique and worst case minimizing strategy developed in earlier works are applied to the augmented incremental system to yield an of set error free controller. The resulting algorithm is formulated so that it can be solved using LP.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.644-649
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• 2006

This paper presents a robust and superior control performance of a quarter-vehicle electrorheological (ER) suspension system. In order to achieve this goal, a moving sliding mode control algorithm is adopted, and its moving strategy is tuned by fuzzy logic. As a first step, ER damper is designed and manufactured for a passenger vehicle suspension system, and its field-dependent damping force is experimentally evaluated. After formulating the governing equation of motion for the quarter-vehicle ER suspension system, a stable sliding surface and moving algorithm based on fuzzy logic are formulated. The fuzzy moving sliding mode controller is then constructed and experimentally implemented. Control performances of the ER suspension system are evaluated in both time and frequency domains.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.8 s.113
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• pp.822-829
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• 2006

This paper presents a robust and superior control performance of a quarter-vehicle electrorheological (ER) suspension system. In order to achieve this goal, a moving sliding mode control algorithm is adopted, and its moving strategy is tuned by fuzzy logic. As a first step, ER damper is designed and manufactured for a passenger vehicle suspension system, and its field-dependent damping force is experimentally evaluated. After formulating the governing equation of motion for the quarter-vehicle ER suspension system, a stable sliding surface and moving algorithm based on fuzzy logic are formulated. The fuzzy moving sliding mode controller is then constructed and experimentally implemented. Control performances of the ER suspension system are evaluated in both time and frequency domains.