• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.1
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• pp.29-35
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• 2012

This paper introduces an adaptive control method of strong mutation rate and probability for queen-bee genetic algorithms. Although the queen-bee genetic algorithms have shown good performances, it had a critical problem that the strong mutation rate and probability should be selected by a trial and error method empirically. In order to solve this problem, we employed the measure of convergence and used it as a control parameter of those. Experimental results with four function optimization problems showed that our method was similar to or sometimes superior to the best result of empirical selections. This indicates that our method is very useful to practical optimization problems because it does not need time consuming trials.

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.141-145
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• 2001

In this paper, model reference PID genetic controller was proposed in order to overcome the difficulty of reflecting control performance required in the overall control system and defects of the adaptation performance in the PID genetic controller. The proposed controller comprised Inner feedback loop consisting of the PID controller and plant, and outer loop consisting of an genetic algorithm which was designed for tuning a parameter of the controller. A reference model was used for design criteria of a PID controller which characterizes and quantizes the control performance required in the overall control system. Tuning parameter of the controller is performed by the genetic algorithm. The performance of proposed algorithm was verified through experiment for the DC servo motor.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.40.6-40
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• 2001

Fuzzy controls are widely used in industrial fields using experts knowledge base for its high degree of performance. Genetic Algorithm(GA) is one of the numerical method that has an advantage of optimization. In this paper, we present an acquisition method of fuzzy rules using genetic algorithm. Knowledge of the system is the key to generating the control rules. As these rules, a system can be more stable and it reaches the control goal the faster. To get the optimal fuzzy control rules and the membership functions, we use the GA instead of the experts knowledge base. Information of the system is coded the chromosome with suitable phenotype. Then, it is operated by genetic operator, and evaluated by evaluation function. Passing by the decoding process with the fittest chromosome, the genetic algorithm can tune the fuzzy rules and the membership functions automatically ...

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.385-389
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• 1994

So far many researches have studied to control a cart system with a pole on the top of itself (forwards we call it simply a cart system) which is movable only to the directions to which a cart moves, using neural networks and genetic algorithms. Especially which it wag solved by genetic algorithms, it was possible to control a cart system more robustly than ordinary methods using neural networks but it had problems too, i.e., the control time to be achieved was short and the processing time for it was long. However we could control a cart system using standard genetic algorithm longer than ordinary neural network methods (for example error backpropagation) and could see that robust control was possible. Computer simulation was performed through the personal computer and the results showed the possibility of real time control because the cpu time which was occupied by processes was relatively short.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.964-967
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• 1996

In this paper, we present a fuzzy-sliding controller design using genetic algorithm. We can suppress chattering and enhance the robustness of controlled system by using this controller and do that genetic algorithm can easily find out a nearly optimal fuzzy rule performance of this controller is tested by simulation of car system with two pole.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2033-2034
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• 2006

In this paper, we applied genetic algorithm to current control for robust control and stability. In conclusion, we prove that current control of genetic algorithm can be high efficiency.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.241-246
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• 2004

This paper proposed trajectory tracking control of mobile robot. Trajectory tracking control scheme are real coding genetic-algorithm and back-propergation algorithm. Control scheme ability experience proposed simulation. Stable tracking control problem of mobile robots have been studied in recent years. These studios have guaranteed stability of controller, but the performance of transient state has not been guaranteed. In some situations, constant gain controller shows overshoots and oscillations. So we introduce better control scheme using Real coding Genetic Algorithm(RCGA) and neural network. Using RCGA, we can find proper gains in several situations and these gains are generalized by neural network. The generalization power of neural network will give proper gain in untrained situation. Performance of proposed controller will verify numerical simulations and the results show better performance than constant gain controller.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.737-741
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• 1994

We show an application of a genetic algorithm to, control systems including neural networks. Genetic algorithms are getting more popular nowadays because of their simplicity and robustness. Genetic algorithms are global search techniques for optimization and many other problems. A feed-forward neural network which is widely used in control applications usually learns by error back propagation algorithm(EBP). But, when there exist certain constraints, EBP can not be applied. We apply a modified genetic algorithm to such a case. We show simulation examples of two cart-pole nonlinear systems: single pole and double pole.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1185-1188
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• 1996

In this paper we present a genetic approach for trajectory control algorithm of balancing weight for IWR biped walking robot. The biped walking robot, IWR that was made by Automatic Control Lab. of Inha University has a trunk which stabilizes its walking by generating compensation moment. Trunk is composed of a revolute and a prismatic joint which roles balancing weight. The motion of balancing weight is determined by the gait of legs and represented by two linear second order ordinary differential equations. The solution of this equation must satisfy some constraints simultaneously to have a physical meaning. Genetic algorithm search for this feasible motion of balancing weight under some constraints. Simulation results show that feasible motion of balancing weight can be obtained by genetic algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.646-651
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• 1998

Fuzzy control has been used successfully in many practical applications. In traditional methods, experience and control knowledge of human experts are needed to design fuzzy controllers. However, it takes much time and cost. In this paper, an automatic design method for fuzzy controllers using genetic algorithms is proposed. In the method, we proposed an effective encoding scheme and new genetic operators. The maximum number of linguistic terms is restricted to reduce the number of combinatorial fuzzy rules in the research space. The proposed genetic operators maintain the correspondency between membership functions and control rules. The proposed method is applied to a cart centering problem. The result of the experiment has been satisfactory compared with other design methods using genetic algorithms.