Browsing by Author "Hung, Kun-Neng"

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Adaptive neuro-wavelet control for switching power supplies
(2007-01-01)
Lin, Chih-Min
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Hung, Kun-Neng
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Hsu, Chun-Fei
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電控工程研究所
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Institute of Electrical and Control Engineering
The switching power supplies can convert one level of electrical voltage into another level by switching action. They are very popular because of their high efficiency and small size. This paper proposes an adaptive neuro-wavelet (ANW) control system for the switching power supplies. In the ANW control system, a neural controller is the main controller used to mimic an ideal controller and a compensated controller is designed to recover the residual of the approximation error. In this study, an online adaptive law with a variable optimal learning-rate is derived based on the Lyapunov stability theorem, so that not only the stability of the system can be guaranteed but also the convergence of controller parameters can be speeded up. Then, the proposed ANW control system is applied to control a forward switching power supply. Experimental results show that the proposed ANW controller can achieve favorable regulation performance for the switching power supply even under input voltage and load resistance variations.
Adaptive SOM-Based Fuzzy Neural Network Controller Design for Multi-Agent System Dispatching and Path Planning
(2012)
Wang, Chi-Hsu
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Hung, Kun-Neng
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電機工程學系
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Department of Electrical and Computer Engineering
In this paper, a new adaptive SOM-based Fuzzy Neural Network controller is proposed for dynamic dispatching and path planning of multi-agent systems. The traditional self-organizing map (SOM) is to exclusively search the real-time shortest paths for all agents to go to their targets. After this traditional dispatching, the weighting factors of our new SOM-based fuzzy neural network (FNN) controller are activated to force the agents toward their corresponding targets. The FNN controller is the main controller which combines the fuzzy rules with the neural network. A monitoring controller is also designed to reduce the error between FNN controller and ideal controller. By the Lyapunov constraints, the weighting factors for the proposed SOM-based FNN controller are updated to guarantee the stability of the path planning system. From the simulation results, it can be obviously seen that the proposed new method is capable of effectively dispatching the agents and controlling their trajectories to the corresponding targets.
DC-DC intelligent power regulator design using wavelet neural network
(2007)
Hsu, Chun-Fei
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Lee, Tsu-Tian
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Hung, Kun-Neng
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電機學院
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College of Electrical and Computer Engineering
DC-DC power converters are the! devices which can convert a certain electrical voltage to another level of electrical voltage by switching action. This paper proposes a wavelet neural network control (WNNC) system for the DC-DC power converters. The WNNC system is comprised of a neural controller and a robust controller. The neural controller is used to mimic an ideal controller and the robust controller is designed to achieve L-2 tracking performance with desired attenuation level. Finally, some experimental results are provided to demonstrate the proposed WNNC system can track the output reference voltage variations and cope with the load resistance variations to ensure the stability while providing fast transient response.
DYNAMIC SYSTEM IDENTIFICATION USING HIGH-ORDER HOPFIELD-BASED NEURAL NETWORK (HOHNN)
(2012-11-01)
Wang, Chi-Hsu
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Hung, Kun-Neng
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電機工程學系
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Department of Electrical and Computer Engineering
The high-order Hopfield-based neural network (HOHNN) with functional link net (FLN) has been developed in this paper for the purpose of uncertain dynamic system identification. In comparison with the traditional Hopfield neural network (HNN) and the high-order neural network (HONN), the compact structure of FLN, with a systematic order mathematical representation combined into the proposed HOHNN, has additional inputs for each neuron for faster convergence rate and less computational load. The weighting factors in HOHNN are tuned via the Lyapunov stability theorem to guarantee the convergence performance of real-time system identification. The robust learning analysis of HOHNN to improve the convergence in the performance is also discussed. The simulation results and computation analysis for different Hopfield-based neural networks are conducted to show the effectiveness of HOHNN in uncertain dynamic system identification.
High-Order Hopfield-based Neural Network for Nonlinear System Identification
(2009)
Wang, Chi-Hsu
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Hung, Kun-Neng
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電控工程研究所
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Institute of Electrical and Control Engineering
The high-order Hopfield neural network (HOHNN) with functional link net has been developed in this paper for the purpose of system identification of nonlinear dynamical system. The weighting factors in HOHNN will be tuned via the Lyapunov stability criterion to guarantee the convergence performance of real-time system identification. In comparison with the traditional Hopfield neural network (HNN), the proposed architecture of HOHNN has additional inputs for each neuron which has the advantages of faster convergence rate and less computational load. The simulation results for both HNN and HOHNN are finally conducted to show the effectiveness of HOHNN in system identification of uncertain dynamical systems. It is obvious from the simulation results that the performance of system identification for HOHNN is better than that of HNN.
Intelligent Adaptive Law for Missile Guidance Using Fuzzy Neural Networks
(2013-06-01)
Wang, Chi-Hsu
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Hung, Kun-Neng
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電機工程學系
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Department of Electrical and Computer Engineering
In this paper, an adaptive fuzzy neural network (FNN) controller is proposed for missile guidance. The objective is for one defending missile (DM) to intercept an attacking missile (AM) in air battle scenario. The adaptive FNN controller is adopted to force the DM toward the AM under the existence of disturbance, and a monitoring controller is also designed to reduce the error between FNN controller and ideal controller. In comparison with the other multi-layered neural network controller, the proposed adaptive FNN controller which combines the fuzzy rules with the neural network can be easily designed. The weighting factors of our new FNN controller are activated to dispatch the DM toward the AM. Using the Lyapunov constraints, the weighting factors for the proposed FNN controller are updated to guarantee the stability of the path planning system. In our illustrated examples, the systematic battle environment is constructed. From the simulation results, the proposed adaptive FNN controller is capable of performing missile guidance, and it also shows that the computation load of our proposed approach is much less than that of using cerebellar model articulation controller (CMAC).
Toward a New Task Assignment and Path Evolution (TAPE) for Missile Defense System (MDS) Using Intelligent Adaptive SOM with Recurrent Neural Networks (RNNs)
(2015-06-01)
Wang, Chi-Hsu
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Chen, Chun-Yao
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Hung, Kun-Neng
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電機資訊學士班
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Undergraduate Honors Program of Electrical Engineering and Computer Science
In this paper, a new adaptive self-organizing map (SOM) with recurrent neural network (RNN) controller is proposed for task assignment and path evolution of missile defense system (MDS). We address the problem of N agents (defending missiles) and D targets (incoming missiles) in MDS. A new RNN controller is designed to force an agent (or defending missile) toward a target (or incoming missile), and a monitoring controller is also designed to reduce the error between RNN controller and ideal controller. A new SOM with RNN controller is then designed to dispatch agents to their corresponding targets by minimizing total damaging cost. This is actually an important application of the multiagent system. The SOM with RNN controller is the main controller. After task assignment, the weighting factors of our new SOM with RNN controller are activated to dispatch the agents toward their corresponding targets. Using the Lyapunov constraints, the weighting factors for the proposed SOM with RNN controller are updated to guarantee the stability of the path evolution (or planning) system. Excellent simulations are obtained using this new approach for MDS, which show that our RNN has the lowest average miss distance among the several techniques.