• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.698-706
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• 2016

In order to move mobile robots to desired locations in a minimum time, optimal control problems have to be solved; however, their analytic solutions are almost impossible to obtain due to robot nonlinear equations. This paper presents a method to get optimal control gains of mobile robots using genetic algorithms. Since the optimal control gains of mobile robots depend on the initial conditions, the initial condition range is discretized to form some grid points, and genetic algorithms are applied to provide the optimal control gains for the corresponding grid points. The optimal control gains for general initial conditions may be obtained by use of neural networks. So the optimal control gains and the corresponding grid points are used to train neural networks. The trained neural networks can supply pseudo-optimal control gains. Finally simulation studies have been conducted to verify the effectiveness of the method presented in this paper.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1996.04a
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• pp.125-129
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• 1996

This paper deals with the problem of placement/sizing of distributed piezo actuators to achieve the control objective of vibration suppression. Using the mean square response as a performance index in optimization, we obtain optimal placement and sizing of the actuator. The use of genetic algorithms as a technique for solving optimization problems of placement and sizing is explored. Genetic algorithms are also used for the control strategy. The analysis of the system and response moment equations are carried out by using the Fokker-Planck equation. This paper presents the design and analysis of an active controller and optimal placement/sizing of distributed piezo actuators based on genetic algorithms for a flexible structure under random disturbance, shows numerical example and the result.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2006.06a
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• pp.77-78
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• 2006

A mixed method between $H_2\;and\;H_{\infty}$ control are widely applied to systems which has parameter perturbation and uncertain model to obtain an optimal robust controller. Brushless Direct Current (BLDC) motors are widely used for high performance control applications. Conventional PID controller only provides satisfactory performance for set-point regulation. However, with the presence of nonlinearities, uncertainties and perturbations in the system, conventional PID is not sufficient to achieve an optimal robust controller. This paper presents an approach to ease designing a Mixed $H_2/H_{\infty}$ PID controller for controlling speed of Brushless DC motors and the genetic algorithm is used to solve the optimized problems. Numerical results are shown to prove that the performance in the proposed controller is better than that in the optimal PID controller using LQR approach.

• Journal of Electrical Engineering and Technology
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• v.1 no.4
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• pp.455-460
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• 2006

This paper presents the convergency property of the Auxiliary Problem Principle when it is applied to large-scale Optimal Power Flow problems with Distributed or Parallel computation features. The key features and factors affecting the convergence ratio and solution stability of APP are also analyzed.

• 전기의세계
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• v.28 no.6
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• pp.47-51
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• 1979

An optimal problem in which the dynamics is nonlinear and the cost functional includes a discontinuous integrand is investigated. By using Neustadt's abstract maximum principle, a necessary conditions in the form of Pontryagin's maximum principle is derived and it is further shown that this necessary condition is also a sufficient condition for normal problems with linear-in-the-state systems.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.1
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• pp.18-25
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• 2000

In the two-mass servo system driving a load through a flexible shaft, a shaft torsional vibration is often generated. PI controller has been generally used is speed control of such system because of the simplicity of structure and related theory. This paper presents the inertia ratio of the PI servo control system which can be designed by using optimal pole assignment method is fixed. Therefore, it's difficult to obtain the desired control characteristics for different systems only by PI control algorithm. To solve this problems the two-mass speed control system with PID controller is designed by using pole assignment method and an optimum PID parameters are derived by evaluating ITAE(Integral of time multiplied by the absolute error) performance index. But this design method has some problems due to a trade-off between the fast command following property and the attenuation of disturbances and vibrations. In this paper, 2-DOF PID control method which satisfies the command following property, the reduction of overshoot and the property of disturbance rejection at the same time is proposed. This is a practical speed controller using the desired value filter and the feedforward gain. From several simulations, it's clarified that the proposed 2-DOF PID controller is useful for the two-mass system, in comparison with the conventional PID controller.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.46.4-46
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• 2002

This study was motivated how it might be possible to validate structured sampling with orthogonal array for optimal design of a pin. The Taguchi method by orthogonal array, one of the structured sampling methods, has much advantage that is row cost and time saving for experiments. But this method has been applied in limited areas especially for mechanical problems. In this study, we experimented whether the structured sampling is useful for applying optimal design of mechanical elements. For the experiment, we first set up a mechanical problem which was related to determining optimal parameters associated a pin's crack occurred inside a hole. We, then, calculated combination of...

• 전기의세계
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• v.21 no.3
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• pp.48-52
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• 1972

The application of pontryagin's Maximum Principle to the optimal control eventually leads to the problem of solving the two point boundary value problem. Most of problems have been related to their own special factors, therfore it is very hard to recommend the best method of deriving their optimal solution among various methods, such as iterative Runge Kutta, analog computer, gradient method, finite difference and successive approximation by piece-wise linearization. The gradient method has been applied to the optimal control of two point boundary value problem in the power systems. The most important thing is to set up some objective function of which the initial value is the function of terminal point. The next procedure is to find out any global minimum value from the objective function which is approaching the zero by means of gradient projection. The algorithm required for this approach in the relevant differential equations by use of the Runge Kutta Method for the computation has been established. The usefulness of this approach is also verified by solving some examples in the paper.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.14 no.2
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• pp.125-140
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• 2010

This paper develops a least-squares approach to the solution of the optimal control problem for the Navier-Stokes equations. We recast the optimality system as a first-order system by introducing velocity-flux variables and associated curl and trace equations. We show that a least-squares principle based on $L^2$ norms applied to this system yields optimal discretization error estimates in the $H^1$ norm in each variable.

• Advances in Computational Design
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• v.5 no.3
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• pp.233-260
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• 2020

The main purpose to introduce model based controller in proposed control technique is to provide better and fast learning of the floating dynamics by means of fuzzy logic controller and also cancelling effect of nonlinear terms of the system. An iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle (AUV). The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.