• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2684-2686
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• 2005

This paper presents design of nonlinear controller based on genetic-fuzzy algorithm. Motor system that is included at a humanoid robot has many nonlinear parameters such as saturation, backlash and so on. So, it is hard to control a humanoid robot because of these nonlinearities. Also, tracking following ability is also reduced by these nonlinearities. In this paper, fuzzy PID controller is proposed for reducing efficiency by saturation. At that time, genetic algorithm is supplied at making fuzzy rule in order to make optimal fuzzy PID controller. Also, disturbance observer is used to reduce the efficiency of backlash. All these processes are verified by simulation and experiment in the real humanoid robot.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.272-278
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• 2017

In this paper, we consider a single-channel amplitude comparison monopulse system(SCACMS). The monopulse ratio curve(MR-C) of the SCACMS can be controlled by an amplitude difference between sum and different signal, a phase difference and the coefficient of the signal processor. We first propose the SCACMS with multiple variables, and then apply a genetic algorithm to optimize the multiple variables in terms of minimizing a root mean square error. The simulation results show that when three variables of the SCACMS are jointly optimized, the linear region of the MR-C can be extended approximately 187 % compared to that of two variables.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2167-2170
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• 2002

In this paper, model-based tuning rules of the PID controller are proposed incorporating with genetic algorithms. Three sets of optimal PID parameters for set-point tracking are obtained based on the first-order time delay model and a genetic algorithm as a optimization tool which minimizes performance indices(IAE, ISE and ITAE). Then tuning rules are derived using the tuned parameter sets, potential rule models and a genetic algorithm. Simulation is carried out to verify the effectiveness of the proposed rules.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.4
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• pp.498-504
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• 1998

This paper presents an optimal control algorithm for the overhead crane. To control the swing motion and the position tracking of the payload of the overhead crane a state feedback control algorithm is applied. by using a hybrid genetic algorithm the feedback gains of the state feedback is optimized to minimize the cost function composed of position errors and payload swing angle under unknown constant disturbances. Computer simulation is performed to demonstrate the effectiveness of the proposed control algorithm.

• Journal of IKEEE
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• v.1 no.1 s.1
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• pp.19-30
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• 1997

In this paper we design the optimal satellite-tracking antenna $H{\infty}$ control system using genetic algorithms. To do this, we give gain and dynamics parameters to the weighting functions and apply genetic algorithms with reference model to the optimal determination of weighting functions and design parameter ${\gamma}$ that are given by Glover-Doyle algorithm which can design $H{\infty}$ controller in the state space. These weighting functions and design parameter ${\gamma}$ are optimized simultaneously in the search domain guaranteeing the robust stability of closed-loop system. The effectiveness of this satellite-tracking antenna $H{\infty}$ control system is verified by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.399-405
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• 2007

This paper deals with the controller for a stable walking of a humanoid robot using genetic algorithm. A humanoid robot has instability during walking because it isn't fixed on the ground, and its nonlinearities of the joints increase its instability. If controller isn't robust, the robot may fall down at the ground during walking because of its nonlinearities. To solve this problem, robust controller is required to reduce the effect of nonlinearities and to gain the good tracking performance. In this paper, motion controller that is based on fuzzy-sliding mode controller is proposed. This controller can remove the effect of the saturation by limitation of the input voltage. It also includes compensator for reducing the effect of the nonlinearity by backlash and PI controller improving the tracking performance. In here, genetic algorithm is used for searching the optimal gains of the controller. From the given controller, a humanoid robot can moved more preciously. All the processes are investigated through simulations and are verified experimentally in a real joint system for a humanoid robot.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.607-612
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• 1998

In this paper, a controller using fuzzy-genetic algorithms is proposed for pat-tracking of WMR. A fuzzy controller is implemented so as to adjust appropriate crossover rate and mutation rate. A genetic algorithms is also implemented to have adaptive adjustment of control gain during optimizing process. To check effectiveness of this algorithms, computer simulation is applied.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.581-584
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• 1998

This paper presents a scheme for fuzzy modelling and control of continuous-time nonlinear systems using a genetic algorithm. A fuzzy model is characterized by fuzzy "if-then" rules whose consequence part has a linear dynamic equation as subsystem of the system. The parameters of the fuzzy model are adjusted by a genetic algorithm. Then a tracking controller which guarantees stability of the overall system is designed. The simulation result demonstrates the effectiveness of the proposed method.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.93-100
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• 2003

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

• Proceedings of the KSME Conference
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• 2000.04b
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• pp.650-654
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• 2000

Particle Imaging Velocimetry (PIV) is becoming one of essential methods to measure velocity fields of fluid flows. In this paper, a genetic algorithm capable of tracking same particle pairs on two separated images is introduced. The fundamental of the developed technique is based on that on-to-one correspondence is found between two tracer particles selected in two image planes by taking advantage of combinatorial optimization of the genetic algorithm. The fitness function controlling reproductive success in the genetic algorithm is expressed by physical distances between the selected tracer particles. The capability of the developed genetic algorithm is verified by a computer simulation on a farced vortex flow.