The Experts below are selected from a list of 1077 Experts worldwide ranked by ideXlab platform

Qing Song - One of the best experts on this subject based on the ideXlab platform.

  • ESANN - Adaptive Simultaneous Perturbation Based Pruning Algorithm for Neural Control Systems
    2020
    Co-Authors: Jie Ni, Qing Song
    Abstract:

    It is normally difficult to determine the optimal size of neu- ral networks, particularly, in the sequential training applications such as online Control. In this paper, a novel training and pruning algorithm, Adaptive Simultaneous Perturbation Based Pruning Algorithm (ASPBP), is proposed for the online tuning and pruning the Neural tracking Control System. The conic sector theory is introduced in the design of this robust Neural Control System, which aims at providing guaranteed boundedness for both the input-output signals and the weights of the Neural network.

  • ICARCV - Recurrent Neural network based tracking Control
    2010 11th International Conference on Control Automation Robotics & Vision, 2010
    Co-Authors: Zhao Xu, Qing Song, Danwei Wang
    Abstract:

    In this paper, a recurrent Neural network (RNN) based robust tracking Controller is designed for a class of multiple-input-multiple-output (MIMO) discrete time nonlinear Systems. The RNN is used in the closed-loop System to estimate online unknown nonlinear System function. A multivariable robust adaptive gradient-descent training algorithm is developed to train RNN. The proposed Neural Control System guarantees the stability of the closed-loop System and good tracking performance is achieved.

  • Recurrent Neural network based tracking Control
    2010 11th International Conference on Control Automation Robotics & Vision, 2010
    Co-Authors: Zhao Xu, Qing Song, Danwei Wang
    Abstract:

    In this paper, a recurrent Neural network (RNN) based robust tracking Controller is designed for a class of multiple-input-multiple-output (MIMO) discrete time nonlinear Systems. The RNN is used in the closed-loop System to estimate online unknown nonlinear System function. A multivariable robust adaptive gradient-descent training algorithm is developed to train RNN. The proposed Neural Control System guarantees the stability of the closed-loop System and good tracking performance is achieved.

  • Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation
    IEEE Transactions on Neural Networks, 2008
    Co-Authors: Qing Song, James C. Spall, Jie Ni
    Abstract:

    This paper considers the design of robust Neural network tracking Controllers for nonlinear Systems. The Neural network is used in the closed-loop System to estimate the nonlinear System function. We introduce the conic sector theory to establish a robust Neural Control System, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the Neural network. The Neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed Neural Control System guarantees closed-loop stability of the estimation System, and a good tracking performance. The performance improvement of the proposed System over existing Systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against System disturbance.

  • Dynamic pruning algorithm for multilayer perceptron based Neural Control Systems
    Neurocomputing, 2006
    Co-Authors: Jie Ni, Qing Song
    Abstract:

    Abstract Generalization ability of Neural networks is very important and a rule of thumb for good generalization in Neural Systems is that the smallest System should be used to fit the training data. Unfortunately, it is normally difficult to determine the optimal size of networks, particularly, in the sequential training applications such as online Control. In this paper, an online training algorithm with a dynamic pruning procedure is proposed for the online tuning and pruning the Neural tracking Control System. The conic sector theory is introduced in the design of this robust Neural Control System, which aims at providing guaranteed boundedness for both the input–output signals and the weights of the Neural network. The proposed algorithm is applied to a multilayer perceptron with adjustable weights and a complete convergence proof is provided. The Neural Control System guarantees the closed-loop stability of the estimation, and in turn, a good tracking performance. The performance improvement of the proposed System over existing Systems can be qualified in terms of better generalization ability, preventing weight shifts, fast convergence and robustness against System disturbance.

Jie Ni - One of the best experts on this subject based on the ideXlab platform.

  • ESANN - Adaptive Simultaneous Perturbation Based Pruning Algorithm for Neural Control Systems
    2020
    Co-Authors: Jie Ni, Qing Song
    Abstract:

    It is normally difficult to determine the optimal size of neu- ral networks, particularly, in the sequential training applications such as online Control. In this paper, a novel training and pruning algorithm, Adaptive Simultaneous Perturbation Based Pruning Algorithm (ASPBP), is proposed for the online tuning and pruning the Neural tracking Control System. The conic sector theory is introduced in the design of this robust Neural Control System, which aims at providing guaranteed boundedness for both the input-output signals and the weights of the Neural network.

  • Robust Neural Network Tracking Controller Using Simultaneous Perturbation Stochastic Approximation
    IEEE Transactions on Neural Networks, 2008
    Co-Authors: Qing Song, James C. Spall, Jie Ni
    Abstract:

    This paper considers the design of robust Neural network tracking Controllers for nonlinear Systems. The Neural network is used in the closed-loop System to estimate the nonlinear System function. We introduce the conic sector theory to establish a robust Neural Control System, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the Neural network. The Neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed Neural Control System guarantees closed-loop stability of the estimation System, and a good tracking performance. The performance improvement of the proposed System over existing Systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against System disturbance.

  • Dynamic pruning algorithm for multilayer perceptron based Neural Control Systems
    Neurocomputing, 2006
    Co-Authors: Jie Ni, Qing Song
    Abstract:

    Abstract Generalization ability of Neural networks is very important and a rule of thumb for good generalization in Neural Systems is that the smallest System should be used to fit the training data. Unfortunately, it is normally difficult to determine the optimal size of networks, particularly, in the sequential training applications such as online Control. In this paper, an online training algorithm with a dynamic pruning procedure is proposed for the online tuning and pruning the Neural tracking Control System. The conic sector theory is introduced in the design of this robust Neural Control System, which aims at providing guaranteed boundedness for both the input–output signals and the weights of the Neural network. The proposed algorithm is applied to a multilayer perceptron with adjustable weights and a complete convergence proof is provided. The Neural Control System guarantees the closed-loop stability of the estimation, and in turn, a good tracking performance. The performance improvement of the proposed System over existing Systems can be qualified in terms of better generalization ability, preventing weight shifts, fast convergence and robustness against System disturbance.

  • Sequential neuron pruning algorithm for RBF network with guaranteed stability
    Proceedings. 2005 IEEE International Joint Conference on Neural Networks 2005., 2005
    Co-Authors: Jie Ni, Qing Song
    Abstract:

    The radial basis function (RBF) network is used in a Neural network Control System and the target is not only to remember the training samples but also to obtain good generalization performance. A rule of thumb for good generalization in Neural Systems is that the smallest System should be used to fit into the training data. Unfortunately, it is usually difficult to determine the optimal size of the RBF networks, particularly, in the sequential training procedure such as the online Control problem. The proposed pruning method in this paper begins with a relatively large network, and certain units of the RBF network are dropped by examining the estimation error increment. The conic sector theory is introduced in the design of this robust Neural Control System, which aims at providing guaranteed boundedness for both the input-output signals and the weights of the Neural network. The performance improvement of the proposed System over existing Systems can be qualified in terms of better generalization ability and preventing weight shifts.

T.n. Zhao - One of the best experts on this subject based on the ideXlab platform.

  • Robust Neural network Controller for variable airflow volume System
    IEE Proceedings - Control Theory and Applications, 2003
    Co-Authors: Q. Song, W.j. Hu, T.n. Zhao
    Abstract:

    The HVAC industry expects the automation and Control Systems to perform well throughout the year without the requirement to re-tune the System and to conserve energy. To satisfy these requirements it is necessary to provide a basic Control algorithm that will respond well to the presence of nonlinear behaviour in HVAC equipment. The PID algorithm has to be enhanced to handle the highly nonlinear functionality, range of operation and robustness. The Neural network is one of the best candidates to deal with these issues. However, it is important to address the stability and disturbance properly, to obtain an optimal performance of the Neural Control System. The design and application of a robust Neural network algorithm are discussed and how it compliments the fixed proportional Control algorithm to provide the desired functionality as well as the adaptation of the variable air volume Control System for a wide range of disturbances and parameter changes.

P.a. Ioannou - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive bounding techniques for stable Neural Control Systems
    Proceedings of 1995 34th IEEE Conference on Decision and Control, 1995
    Co-Authors: M.m. Polycarpou, P.a. Ioannou
    Abstract:

    This paper considers the design of stable adaptive Neural Controllers for uncertain nonlinear dynamical Systems with unknown nonlinearities. The Lyapunov synthesis approach is used to develop state-feedback adaptive Control schemes based on a general class of nonlinearly parametrized Neural network models. The key assumptions are that the System uncertainty satisfies a "strict-feedback" condition and that the network reconstruction error and higher-order terms (with respect to the parameter estimates) satisfy certain bounding conditions. An adaptive bounding design is used to show that the overall Neural Control System guarantees semi-global uniform ultimate boundedness within a neighborhood of zero tracking error.

Kunikazu Kobayashi - One of the best experts on this subject based on the ideXlab platform.

  • Robust Reinforcement Learning Control System with H∞tracking performance compensator
    2011 11th International Conference on Control Automation and Systems, 2011
    Co-Authors: Shogo Uchiyama, Takashi Kuremoto, Masanao Obayashi, Kunikazu Kobayashi
    Abstract:

    Robust Control theory generally guarantees robustness and stability of the closed-loop System, however it requires mathematical model of the System to design the Control System. Therefore, it can't often deal with nonlinear Systems because of difficulty of modeling of the System. Other, reinforcement learning method can deal with the nonlinear System without mathematical model, however, it usually doesn't guarantee the stability of Control. In this paper, we propose a “Robust Reinforcement Learning Control System (RRLCS)” through combining reinforcement learning to treat unknown nonlinear Systems and using robust Control theory to guarantee the robustness and stability of the System. As a robust Control method, we adopt H∞Control which is robust to modelling error and disturbance. On the other hand, as a reinforcement learning method, we adopt an Actor-Critic method with minimal amount of computation for the continuous action and state space. Moreover, we analyze the stability of the proposed System using H∞tracking performance and Lyapunov function. Finally, through the computer simulation for Controlling the inverted pendulum System, we show the effectiveness of the proposed method comparing with an Adaptive Fuzzy Control method with H∞tracking performance compensator (AFC) and an Auto-Structuring Fuzzy Neural Control System method (ASFNCS).

  • Intelligent tracking Control method of a target by group of agents with nonlinear dynamics
    2011 11th International Conference on Control Automation and Systems, 2011
    Co-Authors: Masanao Obayashi, Shogo Uchiyama, Takashi Kuremoto, Yuuki Yokoji, Liangbing Feng, Kunikazu Kobayashi
    Abstract:

    This paper proposes the intelligent tracking Control of the target by group of agents with nonlinear dynamics. In the proposed method, agents can exchange only information of positions and the group of agents track the target based on only its position, taking a predefined formation. In the real world, the method that agents do not require a lot of information is useful for the case of existing communication delay of information and weak communication. In addition, each agent shall have the nonlinear dynamics and external disturbance. Therefore an auto- structuring fuzzy Neural Control System (ASFNCS) is introduced to provide appropriate Control inputs while coping well with disturbance and nonlinear dynamics. Thus, while maintaining adequate Control performance, the method can reduce the computational load. In the simulation, it is verified that the proposed method is useful in the point of target tracking performance through distributed agent cooperation behaviors.

  • Robust Reinforcement Learning Control System with H∞ tracking performance compensator
    2011 11th International Conference on Control Automation and Systems, 2011
    Co-Authors: Shogo Uchiyama, Takashi Kuremoto, Masanao Obayashi, Kunikazu Kobayashi
    Abstract:

    Robust Control theory generally guarantees robustness and stability of the closed-loop System, however it requires mathematical model of the System to design the Control System. Therefore, it can't often deal with nonlinear Systems because of difficulty of modeling of the System. Other, reinforcement learning method can deal with the nonlinear System without mathematical model, however, it usually doesn't guarantee the stability of Control. In this paper, we propose a “Robust Reinforcement Learning Control System (RRLCS)” through combining reinforcement learning to treat unknown nonlinear Systems and using robust Control theory to guarantee the robustness and stability of the System. As a robust Control method, we adopt H∞ Control which is robust to modelling error and disturbance. On the other hand, as a reinforcement learning method, we adopt an Actor-Critic method with minimal amount of computation for the continuous action and state space. Moreover, we analyze the stability of the proposed System using H∞ tracking performance and Lyapunov function. Finally, through the computer simulation for Controlling the inverted pendulum System, we show the effectiveness of the proposed method comparing with an Adaptive Fuzzy Control method with H∞ tracking performance compensator (AFC) and an Auto-Structuring Fuzzy Neural Control System method (ASFNCS).