• Journal of Institute of Control, Robotics and Systems
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• v.4 no.5
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• pp.608-615
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• 1998

In this paper a second-order iterative learning control algorithm with feedback is proposed for the trajectory-tracking control of nonlinear dynamic systems with unidentified parameters. In contrast to other known methods, the proposed teaming control scheme utilize more than one past error history contained in the trajectories generated at prior iterations, and a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is given in detail, and the sufficient condition for the convergence of the algorithm is provided. We also discuss the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial condition. The effectiveness of the proposed algorithm is shown by computer simulation result. It is shown that, by adding a feedback term in teaming control algorithm, convergence speed, robustness to disturbances and robustness to unmatched initial conditions can be improved.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.11
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• pp.84-92
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• 1993

Calibration of Engine models is a painstaking process but very important for successful application to automotive industry problems. A combined heuristic and machine learning approach has therefore been adopted to improve the efficiency of model calibration. We developed an intelligent calibration program called ICALIB. It has been used on a daily basis for engine model applications, and has reduced the time required for model calibrations from many hours to a few minutes on average. In this paper, we describe the heuristic control strategies employed in ICALIB such as a hill-climbing search based on a state distance estimation function, incremental problem solution refinement by using a dynamic tolerance window, and calibration target parameter ordering for guiding the search. In addition, we present the application of amachine learning program called GID3*for automatic acquisition of heuristic rules for ordering target parameters.

• The Journal of the Korea Contents Association
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• v.6 no.2
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• pp.93-98
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• 2006

The paper describes a method o( user feedback in order to enhance the retrieval system effectiveness. The existing fuzzification function adapting fuzzy technique has difficulty that 4 type graph is made each time user select components. In this paper, to overcome this weak point of feedback, we proposed the interaction function using gaussian function that gives different learning rate according to choice of components with same function. We suggest the most efficient dynamic interaction function based on comparison of retrieval performance according to parameter of function. And then, we will construct the efficient retrieval system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1561-1564
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• 1997

In the study, simulation result was studied by connecting PID controller in series to the established Neural Networks Controller. Neural Network model is composed of two layers to evaluate tracking performance improvement. The reqular dynamic characteristics was also studied for the expected error to be minimized by using Widrow-Hoff delta rule. As a result of the study, We identified that tracking performance inprovement was developed more in case of connecting PID than Neural Network Contoller and that tracking plant parameter in 251 sample was approached rapidly case of time variable.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.4
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• pp.259-266
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• 2015

This study presents new scheme for various walking pattern of biped robot under the limitted enviroments. We show that the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multilayer backpropagation neural network identification is simulated to obtain a learning control solution of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The main advantage of our scheme is that we do not require any knowledge about the system dynamic and nonlinear characteristic, and can therefore treat the robot as a black box. It is also shown that the neural network is a powerful control theory for various trajectory tracking control of biped robot with same learning-vase. That is, we do net change the control parameter for various trajectory tracking control. Simulation and experimental result show that the neural network is practically feasible and realizable for iterative learning control of biped robot.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.5
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• pp.335-342
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• 2016

When dynamics changes occurred in an existing Bayesian Network (BN), the related parameters embedding on the BN have to be updated to new parameters adapting to changed patterns. In this case, these parameters have to be updated with the consideration of the causalities in the BN. This research suggests a framework for updating parameters dynamically using Expectation Maximization (EM) algorithm and Harmony Search (HS) algorithm among several Meta-Heuristics techniques. While EM is an effective algorithm for estimating hidden parameters, it has a limitation that the generated solution converges a local optimum in usual. In order to overcome the limitation, this paper applies HS for tracking the global optimum values of Maximum Likelihood Estimators (MLE) of parameters. The proposed method suggests a learning and propagation framework of BN with dynamic changes for overcoming disadvantages of EM algorithm and converging a global optimum value of MLE of parameters.

• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.874-881
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• 2010

A automatic flight control system(AFCS) of UAS needs to control its flight path along target path exactly as adjusts flight coefficient itself depending on static or dynamic changes of airplane's features such as type, size or weight. In this paper, we propose system which tunes control gain autonomously depending on change of airplane's feature in flight as adding MLM(Machine Learning Module) on AFCS. MLM is designed with Linear Regression algorithm and Reinforcement Learning and it includes EvM(Evaluation Module) which evaluates learned control gain from MLM and verified system. This system is tested on beaver FDC simulator and we present its analysed result.

• Journal of Power Electronics
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• v.7 no.2
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.

• Journal of Power Electronics
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• v.13 no.1
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• pp.139-160
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• 2013

In this paper, an intelligent robust control system (IRCS) for precision tracking control of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The IRCS comprises a recurrent wavelet-based interval type-2 fuzzy-neural-network controller (RWIT2FNNC), an RWIT2FNN estimator (RWIT2FNNE) and a compensated controller. The RWIT2FNNC combines the merits of a self-constructing interval type-2 fuzzy logic system, a recurrent neural network and a wavelet neural network. Moreover, it performs the structure and parameter-learning concurrently. The RWIT2FNNC is used as the main tracking controller to mimic the ideal control law (ICL) while the RWIT2FNNE is developed to approximate an unknown dynamic function including the lumped parameter uncertainty. Furthermore, the compensated controller is designed to achieve $L_2$ tracking performance with a desired attenuation level and to deal with uncertainties including approximation errors, optimal parameter vectors and higher order terms in the Taylor series. Moreover, the adaptive learning algorithms for the compensated controller and the RWIT2FNNE are derived by using the Lyapunov stability theorem to train the parameters of the RWIT2FNNE online. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed IRCS. All of the control algorithms are implemented on a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the IRCS grants robust performance and precise response regardless of load disturbances and PMSM parameters uncertainties.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2020-2037
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• 2017

In order to improve the energy efficiency of n-Epidemic routing protocol in opportunistic networks, in which a stable end-to-end forwarding path usually does not exist, a novel adjustment strategy for parameter n is proposed using learning atuomata principle. First, nodes dynamically update the average energy level of current environment while moving around. Second, nodes with lower energy level relative to their neighbors take larger n avoiding energy consumption during message replications and vice versa. Third, nodes will only replicate messages to their neighbors when the number of neighbors reaches or exceeds the threshold n. Thus the number of message transmissions is reduced and energy is conserved accordingly. The simulation results show that, n-Epidemic routing protocol with the proposed adjustment method can efficiently reduce and balance energy consumption. Furthermore, the key metric of delivery ratio is improved compared with the original n-Epidemic routing protocol. Obviously the proposed scheme prolongs the network life time because of the equilibrium of energy consumption among nodes.