• Title/Summary/Keyword: NN controller

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Stochastic intelligent GA controller design for active TMD shear building

  • Chen, Z.Y.;Peng, Sheng-Hsiang;Wang, Ruei-Yuan;Meng, Yahui;Fu, Qiuli;Chen, Timothy
    • Structural Engineering and Mechanics
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    • v.81 no.1
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    • pp.51-57
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    • 2022
  • The problem of optimal stochastic GA control of the system with uncertain parameters and unsure noise covariates is studied. First, without knowing the explicit form of the dynamic system, the open-loop determinism problem with path optimization is solved. Next, Gaussian linear quadratic controllers (LQG) are designed for linear systems that depend on the nominal path. A robust genetic neural network (NN) fuzzy controller is synthesized, which consists of a Kalman filter and an optimal controller to assure the asymptotic stability of the discrete control system. A simulation is performed to prove the suitability and performance of the recommended algorithm. The results indicated that the recommended method is a feasible method to improve the performance of active tuned mass damper (ATMD) shear buildings under random earthquake disturbances.

High Performance Speed Control of IPMSM using Neural Network PI (신경회로망 PI를 이용한 IPMSM의 고성능 속도제어)

  • Lee, Jung-Ho;Choi, Jung-Sik;Ko, Jae-Sub;Chung, Dong-Hwa
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2006.05a
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    • pp.315-320
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    • 2006
  • This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fired gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

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Adaptive Output Feedback Control of Unmanned Helicopter Using Neural Networks (신경회로망을 이용한 무인헬리콥터의 적응출력피드백제어)

  • Park, Bum-Jin;Hong, Chang-Ho;Suk, Jin-Young
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.35 no.11
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    • pp.990-998
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    • 2007
  • Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

Instantaneous Torque Estimation and Switching Angle Control for Optimal Operation of SRM (SRM의 최적운전을 위한 순시토크 추정과 스위칭 각 제어)

  • Baik Won-Sik;Kim Min-Huei;Kim Nam-Hun;Choi Kyeong-Ho;Kim Dong-Hee
    • Proceedings of the KIPE Conference
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    • 2004.07b
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    • pp.944-948
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    • 2004
  • This paper presents a simple torque estimation method and switching angle control of Switched Reluctance Motor (SRM) using Neural Network (NN). SRM has gaining much interest as industrial applications due to the simple structure and high efficiency. Adaptive switching angle control is essential for the optimal driving of SRM because of the driving characteristic varies with the load and speed. The proper switching angle which can increase the efficiency was investigated in this paper. NN was adapted to regulate the switching angle and nonlinear inductance modelling. Experimental result shows the validity of the switching angle controller.

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Neurointerface Using an Online Feedback-Error Learning Based Neural Network for Nonholonomic Mobile Robots

  • Lee, Hyun-Dong;Watanabe, Keigo;Jin, Sang-Ho;Syam, Rafiuddin;Izumi, Kiyotaka
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.330-333
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    • 2005
  • In this study, a method of designing a neurointerface using neural network (NN) is proposed for controlling nonholonomic mobile robots. According to the concept of virtual master-slave robots, in particular, a partially stable inverse dynamic model of the master robot is acquired online through the NN by applying a feedback-error learning method, in which the feedback controller is assumed to be based on a PD compensator for such a nonholonomic robot. A tracking control problem is demonstrated by some simulations for a nonholonomic mobile robot with two-independent driving wheels.

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Electronic Differential System for Electric Vehicle using Neural Network (신경회로망을 이용한 전기자동차용 전자식 차동장치)

  • Lim, Young-Cheol;Park, Jong-Kun;Kim, Tae-Gon;Ryoo, Young-Jae;Lee, Ju-Sang
    • Proceedings of the KIEE Conference
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    • 1997.11a
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    • pp.573-575
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    • 1997
  • In this paper, the electronic differential system for electric vehicle using neural network is proposed and its performance is evaluated. The input features of NN are obtained by processing the encoder and potentiometer during driving. The 3 layered NN with back propagation algorithm has been used. Evaluation experiments show that the proposed controller is effective in controlling of unknown nonlinear plants.

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Adaptive Neural Network Control of a Flexible Joint Manipulator (유연관절로봇의 적응신경망제어)

  • 구치욱;이시복;김정석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 1997.04a
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    • pp.101-106
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    • 1997
  • This paper proposes a stable adaptive neural network control(NNC) for fixable joint manipulators. For designing the stable adaptive NNC, the flexible system dynamics is separated into fast and slow subdynamics according to singular perturbation concept. For the slow subdynamics, an adaptive NNC is designed to warrant the system stability and NN learning by lyapunov stability criterion. And to stabilize the fast dynamics, derivative control loop is installed. Through numerical simulation, the performance of the proposed NNC was compared to that of an adaptive controller designed based on the knowledge of the system dynamics. The proposed NNC shows much improvement over the conventional adaptive controller.

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LQG modeling and GA control of structures subjected to earthquakes

  • Chen, ZY;Jiang, Rong;Wang, Ruei-Yuan;Chen, Timothy
    • Earthquakes and Structures
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    • v.22 no.4
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    • pp.421-430
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    • 2022
  • This paper addresses the stochastic control problem of robots within the framework of parameter uncertainty and uncertain noise covariance. First of all, an open circle deterministic trajectory optimization issue is explained without knowing the unequivocal type of the dynamical framework. Then, a Linear Quadratic Gaussian (LQG) controller is intended for the ostensible trajectory-dependent linearized framework, to such an extent that robust hereditary NN robotic controller made out of the Kalman filter and the fuzzy controller is blended to ensure the asymptotic stability of the non-continuous controlled frameworks. Applicability and performance of the proposed algorithm shown through simulation results in the complex systems which are demonstrate the feasible to improve the performance by the proposed approach.

A Vector-Controlled PMSM Drive with a Continually On-Line Learning Hybrid Neural-Network Model-Following Speed Controller

  • EI-Sousy Fayez F. M.
    • Journal of Power Electronics
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    • v.5 no.2
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    • pp.129-141
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    • 2005
  • A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

Design of a Self-tuning PID Controller for Over-damped Systems Using Neural Networks and Genetic Algorithms (신경회로망과 유전알고리즘을 이용한 과감쇠 시스템용 자기동조 PID 제어기의 설계)

  • 진강규;유성호;손영득
    • Journal of Advanced Marine Engineering and Technology
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    • v.27 no.1
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    • pp.24-32
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    • 2003
  • The PID controller has been widely used in industrial applications due to its simple structure and robustness. Even if it is initially well tuned, the PID controller must be retuned to maintain acceptable performance when there are system parameter changes due to the change of operation conditions. In this paper, a self-tuning control scheme which comprises a parameter estimator, a NN-based rule emulator and a PID controller is proposed, which can cope with changing environments. This method involves combining neural networks and real-coded genetic algorithms(RCGAs) with conventional approaches to provide a stable and satisfactory response. A RCGA-based parameter estimation method is first described to obtain the first-order with time delay model from over-damped high-order systems. Then, a set of optimum PID parameters are calculated based on the estimated model such that they cover the entire spectrum of system operations and an optimum tuning rule is trained with a BP-based neural network. A set of simulation works on systems with time delay are carried out to demonstrate the effectiveness of the proposed method.