• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.149.5-149
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent´s dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless ...

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.309-314
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• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.425-436
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• 2016

In this paper, a parameter optimization based iterative learning control strategy is presented for permanent magnet synchronous motor control. This paper analyzes the mechanism of iterative learning control suppressing PMSM torque ripple and discusses the impact of controller parameters on steady-state and dynamic performance of the system. Based on the analysis, an optimization problem is constructed, and the expression of the optimal controller parameter is obtained to adjust the controller parameter online. Experimental research is carried out on a 5.2kW PMSM. The results show that the parameter optimization based iterative learning control proposed in this paper achieves lower torque ripple during steady-state operation and short regulating time of dynamic response, thus satisfying the demands for both steady state and dynamic performance of the speed regulating system.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.363-368
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• 2004

In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.236-241
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• 2007

This paper presents an adaptive controller using chaotic dynamic neural networks(CDNN) for nonlinear dynamic system. A new dynamic backpropagation learning method of the proposed chaotic dynamic neural networks is developed for efficient learning, and this learning method includes the convergence for improving the stability of chaotic neural networks. The proposed CDNN is applied to the system identification of chaotic system and the adaptive controller. The simulation results show good performances in the identification of Lorenz equation and the adaptive control of nonlinear system, since the CDNN has the fast learning characteristics and the robust adaptability to nonlinear dynamic system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1569-1572
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• 1997

In this paper, we present an associative memory network (AMN) controller for dynamic robot control. The purpose of using AMN is to reduce the size of required memory in storing and recalling large of daa representing input relationship of nonlinear functions. With the capability AMN can be used to dynamic robot control, which has nonlinear properties inherently. The proposed AMN control scheme has advantages for the inverse dynamics learning no limitatiion of inpur range, and insensitive of payload change. Computer simulations show the effectiveness and feasibility of proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1464-1468
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• 2004

This paper presents a learning controller for repetitive gait control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured according to the walking period through the iterative learning, not calculating the complex dynamics of walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of learning control to biped robotic motion is shown via dynamic simulation with 12-DOF biped walking robot.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.1113-1119
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• 1989

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.263-278
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• 2004

This paper reviews dynamic programming (DP), surveys approximate solution methods for it, and considers their applicability to process control problems. Reinforcement Learning (RL) and Neuro-Dynamic Programming (NDP), which can be viewed as approximate DP techniques, are already established techniques for solving difficult multi-stage decision problems in the fields of operations research, computer science, and robotics. Owing to the significant disparity of problem formulations and objective, however, the algorithms and techniques available from these fields are not directly applicable to process control problems, and reformulations based on accurate understanding of these techniques are needed. We categorize the currently available approximate solution techniques fur dynamic programming and identify those most suitable for process control problems. Several open issues are also identified and discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.139-144
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• 1995

LIBL(Linguistic Instruction Based Leaning) is an effective learning algorithm for fuzzy controller which interpretes and uses natural language of human The possibiliy of the LIBL algorithm to the fuzzy control of dynamic systems is investigated in this paper. Rise time, percent overshoot, and steady stste are proposed as suitable meaning elements for dynamic systems. A supervisor is able to give "higer-level linguistic instruction" to the learning algorithm through these three meaning elements Simulation results for a DC servo motor show the validity of the proposed algorithm.