• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2953-2955
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• 1999

In this paper, the concept of evolvable hardware and evolutionary robotics are introduced and constructing the mobile robot controller without human operator is suggested. The robot controller is evolved to avoid obstacles by genetic learning which determines the weights between sensor inputs and motor outputs. Genetic algorithms which is executed in a computer(PC) searches the best weights by interacting with robot performance under it's environment. The experiment is done by real mobile robot Khepera and a simple GA.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.1
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• pp.1155-1161
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• 2001

We propose an adaptive nonlinear control algorithm for high precision tracking and stabilization of LOS(Line-of-Sight). The friction parameters of the LOS gimbal are estimated by off-line evolutionary strategy and the friction is compensated by estimated friction compensator. Especially, as the nonlinear control input in a small tracking error zone is enlarged by the nonlinear function, the steady state error is significantly reduced. The proposed algorithm is a direct adaptive control method based on the Lyapunov stability theory, and its convergence is guaranteed under the limited modeling error or torque disturbance. The performance of the pro-posed algorithm is verified by computer simulation on the LOS gimbal model of a moving vehicle.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.221-226
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• 2006

In this paper, we study an approach to design a Fuzzy PI controller in HVDC(High Voltage Direct Current) system. In the rectifier of traditional HVDC system, turning on, turning off, triggering and protections of thyristors have lots of problems that can make the dynamic instability and cannot damp the dynamic disturbance efficiently. In order to solve the above problems, we adapt Fuzzy PI controller for the fire angle control of rectifier. The performance of the Fuzzy PI controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing(Genetic Algorithms, GAs). Then we can obtain factors of the Fuzzy PI controller by Genetic Algorithms. A comparative study has been performed between Fuzzy PI controller and traditional PI controller, to prove the superiority of the proposed scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.624-629
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• 1998

Evolutionary computation techniques can solve search problems using simulated evolution based on the ‘survival of the fittest’. Recently, the genetic programming (GP) which evolves computer programs using the genetic algorithm was introduced. In this paper, the genetic programming technique is used in order to design a rule based controller consisting of condition-action rules for an unknown system. No a priori knowledge about the structure of the controller is needed. Representation of a solution, functions and terminals in GP are analyzed, and a method of constructing a fuzzy logic controller using the obtained rule based controller is described. A simulation example using a nonlinear system shows the validity and efficiency of the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1210-1218
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• 2011

GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.6
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• pp.573-579
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• 2008

This paper introduces a new approach to develop a fast gait for quadruped robot using GP(genetic programming). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multidimensional space. Several recent approaches have focused on using GA(genetic algorithm) to generate gait automatically and shown significant improvement over previous results. Most of current GA based approaches used pre-selected parameters, but it is difficult to select the appropriate parameters for the optimization of gait. To overcome these problems, we proposed an efficient approach which optimizes joint angle trajectories using genetic programming. Our GP based method has obtained much better results than GA based approaches for experiments of Sony AIBO ERS-7 in Webots environment.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.176-187
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• 2002

This paper focuses on the study of simulation and evolution of Micro Air Vehicles. Micro Air Vehicles or MAVs are small flying robots that are used for surveillance, search and rescue, and other missions. The simulated robots are designed based on realistic characteristics and the brains (controllers) of the robots are generated using genetic algorithms, i .e., simulated evolution. The objective for the experiments is to investigate the effects of robot team size and topology (simulation environment) on the evolution of simulated robots. The testing of team sizes deals with finding an ideal number of robots to be deployed for a given mission. The goal of the topology experiments is to see if there is an ideal topology (environment) to evolve the robots in order to increase their utility in most environments. We compare the results of the various experiments by evaluating the fitness values of the robots i .e., performance measure. In addition, evolved robot teams are tested in different situation in order to determine if the results can be generalized, and statistical analysis is performed to evaluate the evolved results.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.3
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• pp.248-253
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• 2015

In order to adapt to human's life and perform missions, a humanoid robot needs a height at least similar with children's. In this paper, we proposed a humanoid robot which is like a child who is taller than 1m. We presented showing the humanoid robot's kinematics, designing of a three-dimensional model, developing mechanisms, and the hardware structures using servo motors and compact size PC. Through this process, we designed and manufactured child humanoid robot 'CHARLES(Cognitive Humanoid Autonomous Robot with Learning and Evolutionary Systems)' that is robot is 1m 10cm tall and 8.16kg in weight. For robot's walking, we applied to ZMP-based walking technique and the creation algorithm is applied for walking patterns. Through experiments, we analyzed walking patterns according to the creation and changing parameter values.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.809-817
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• 2008

Program plagiarism is widespread due to intelligent software and the global Internet environment. Consequently the detection of plagiarized source code and software is becoming important especially in academic field. Though numerous studies have been reported for detecting plagiarized pairs of codes, we cannot find any profound work on understanding the underlying mechanisms of plagiarism. In this paper, we study the evolutionary process of source codes regarding that the plagiarism procedure can be considered as evolutionary steps of source codes. The final goal of our paper is to reconstruct a tree depicting the evolution process in the source code. To this end, we extend the well-known bioinformatics approach, a local alignment approach, to detect a region of similar code with an adaptive scoring matrix. The asymmetric code similarity based on the local alignment can be considered as one of the main contribution of this paper. The phylogenetic tree or evolution tree of source codes can be reconstructed using this asymmetric measure. To show the effectiveness and efficiency of the phylogeny construction algorithm, we conducted experiments with more than 100 real source codes which were obtained from East-Asia ICPC(International Collegiate Programming Contest). Our experiments showed that the proposed algorithm is quite successful in reconstructing the evolutionary direction, which enables us to identify plagiarized codes more accurately and reliably. Also, the phylogeny construction algorithm is successfully implemented on top of the plagiarism detection system of an automatic program evaluation system.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.