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Meizhen Xia - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Neural Dynamic surface control for full state constrained stochastic nonlinear systems with unmodeled Dynamics
    Journal of the Franklin Institute, 2019
    Co-Authors: Meizhen Xia, Tianping Zhang
    Abstract:

    Abstract This paper solves the problem of adaptive Neural Dynamic surface control (DSC) for a class of full state constrained stochastic nonlinear systems with unmodeled Dynamics. The concept of the state constraints in probability is first proposed and applied to the stability analysis of the system. The full state constrained stochastic nonlinear system is transformed to the system without state constraints through a nonlinear mapping. The unmodeled Dynamics is dealt with by introducing a Dynamic signal and the adaptive Neural Dynamic surface control method is explored for the transformed system. It is proved that all signals of the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded(SGUUB) in mean square or the sense of four-moment. At the same time, the full state constraints are not violated in probability. The validity of the proposed control scheme is demonstrated through the simulation examples.

  • adaptive Neural Dynamic surface control of strict feedback nonlinear systems with full state constraints and unmodeled Dynamics
    Automatica, 2017
    Co-Authors: Tianping Zhang, Meizhen Xia
    Abstract:

    Abstract In this paper, the problem of adaptive Neural network (NN) Dynamic surface control (DSC) is discussed for a class of strict-feedback nonlinear systems with full state constraints and unmodeled Dynamics. By introducing a one to one nonlinear mapping, the strict-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. Radial basis function (RBF) Neural networks (NNs) are used to approximate unknown nonlinear continuous functions. Unmodeled Dynamics is dealt with by introducing a Dynamical signal. Using modified DSC and introducing integral-type Lyapunov function, adaptive NN DSC is developed. Using Young’s inequality, only one parameter is adjusted at each recursive step in the design. It is shown that all the signals in the closed-loop system are semi-global uniform ultimate boundedness (SGUUB), and the full state constraints are not violated. Simulation results are provided to verify the effectiveness of the proposed approach.

  • Adaptive Neural Dynamic Surface Control of Pure-Feedback Nonlinear Systems With Full State Constraints and Dynamic Uncertainties
    IEEE Transactions on Systems Man and Cybernetics: Systems, 2017
    Co-Authors: Tianping Zhang, Meizhen Xia, Qikun Shen
    Abstract:

    In this paper, adaptive Neural Dynamic surface control (DSC) is developed using radial basis function Neural networks (NNs) for a class of pure-feedback nonlinear systems with full state constraints and Dynamic uncertainties. Based on a one-to-one nonlinear mapping, the pure-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. The Dynamic uncertainties are dealt with using a Dynamic signal. Using modified DSC and mean value theorem as well as Nussbaum function, two adaptive NN control schemes are proposed based on the transformed system. The designed control strategy removes the conditions that the upper bound of the control gain is known, and the lower bounds and upper bounds of the virtual control coefficients are known. It is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the full state constraints are not violated. Two numerical examples are provided to illustrate the effectiveness of the proposed approach.

Chenguang Yang - One of the best experts on this subject based on the ideXlab platform.

  • Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle
    IEEE Transactions on Neural Networks and Learning Systems, 2015
    Co-Authors: Bin Xu, Chenguang Yang
    Abstract:

    This paper studies both indirect and direct global Neural control of strict-feedback systems in the presence of unknown Dynamics, using the Dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive Neural controller in the Neural approximation domain, together with the robust controller that pulls the transient states back into the Neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the Neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global Neural DSC design.

  • composite Neural Dynamic surface control of a class of uncertain nonlinear systems in strict feedback form
    IEEE Transactions on Systems Man and Cybernetics, 2014
    Co-Authors: Zhongke Shi, Chenguang Yang, Fuchun Sun
    Abstract:

    This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface control technique is incorporated into radial-basis-function Neural networks (NNs)-based control framework to eliminate the problem of explosion of complexity. To avoid the analytic computation, the command filter is employed to produce the command signals and their derivatives. Different from directly toward the asymptotic tracking, the accuracy of the identified Neural models is taken into consideration. The prediction error between system state and serial-parallel estimation model is combined with compensated tracking error to construct the composite laws for NN weights updating. The uniformly ultimate boundedness stability is established using Lyapunov method. Simulation results are presented to demonstrate that the proposed method achieves smoother parameter adaption, better accuracy, and improved performance.

Tianping Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Neural Dynamic surface control for full state constrained stochastic nonlinear systems with unmodeled Dynamics
    Journal of the Franklin Institute, 2019
    Co-Authors: Meizhen Xia, Tianping Zhang
    Abstract:

    Abstract This paper solves the problem of adaptive Neural Dynamic surface control (DSC) for a class of full state constrained stochastic nonlinear systems with unmodeled Dynamics. The concept of the state constraints in probability is first proposed and applied to the stability analysis of the system. The full state constrained stochastic nonlinear system is transformed to the system without state constraints through a nonlinear mapping. The unmodeled Dynamics is dealt with by introducing a Dynamic signal and the adaptive Neural Dynamic surface control method is explored for the transformed system. It is proved that all signals of the closed-loop system are bounded in probability and the error signals are semi-globally uniformly ultimately bounded(SGUUB) in mean square or the sense of four-moment. At the same time, the full state constraints are not violated in probability. The validity of the proposed control scheme is demonstrated through the simulation examples.

  • Adaptive Neural Dynamic surface control of MIMO pure-feedback nonlinear systems with output constraints
    Neurocomputing, 2019
    Co-Authors: Heqing Liu, Tianping Zhang, Xiaonan Xia
    Abstract:

    Abstract In this work, the problem of adaptive Neural Dynamic surface control (DSC) with the minimum adjustable parameters is discussed for a class of multi-input multi-output (MIMO) pure-feedback nonlinear systems with unmodeled Dynamics and output constraints. An auxiliary signal designed by the characteristics of unmodeled Dynamics is used to handle the Dynamical uncertainties. The unknown continuous black-box functions produced in the controller design process are approximated by using radial basis function Neural networks (RBFNNs). Based on an one-to-one nonlinear mapping(NM), the MIMO nonaffine nonlinear system with output constraints is transformed into a novel block-structure MIMO nonaffine nonlinear system without output constraints. Based on the transformed system and modified DSC, robust adaptive Neural tracking control scheme is developed. Through theoretical analysis, all the signals in the closed-loop system are shown to be semi-globally uniformly ultimately bounded (SGUUB). A numerical example is provided to demonstrate the effectiveness of the proposed design strategy.

  • adaptive Neural Dynamic surface control of strict feedback nonlinear systems with full state constraints and unmodeled Dynamics
    Automatica, 2017
    Co-Authors: Tianping Zhang, Meizhen Xia
    Abstract:

    Abstract In this paper, the problem of adaptive Neural network (NN) Dynamic surface control (DSC) is discussed for a class of strict-feedback nonlinear systems with full state constraints and unmodeled Dynamics. By introducing a one to one nonlinear mapping, the strict-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. Radial basis function (RBF) Neural networks (NNs) are used to approximate unknown nonlinear continuous functions. Unmodeled Dynamics is dealt with by introducing a Dynamical signal. Using modified DSC and introducing integral-type Lyapunov function, adaptive NN DSC is developed. Using Young’s inequality, only one parameter is adjusted at each recursive step in the design. It is shown that all the signals in the closed-loop system are semi-global uniform ultimate boundedness (SGUUB), and the full state constraints are not violated. Simulation results are provided to verify the effectiveness of the proposed approach.

  • Adaptive Neural Dynamic Surface Control of Pure-Feedback Nonlinear Systems With Full State Constraints and Dynamic Uncertainties
    IEEE Transactions on Systems Man and Cybernetics: Systems, 2017
    Co-Authors: Tianping Zhang, Meizhen Xia, Qikun Shen
    Abstract:

    In this paper, adaptive Neural Dynamic surface control (DSC) is developed using radial basis function Neural networks (NNs) for a class of pure-feedback nonlinear systems with full state constraints and Dynamic uncertainties. Based on a one-to-one nonlinear mapping, the pure-feedback system with full state constraints is transformed into a novel pure-feedback system without state constraints. The Dynamic uncertainties are dealt with using a Dynamic signal. Using modified DSC and mean value theorem as well as Nussbaum function, two adaptive NN control schemes are proposed based on the transformed system. The designed control strategy removes the conditions that the upper bound of the control gain is known, and the lower bounds and upper bounds of the virtual control coefficients are known. It is shown that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the full state constraints are not violated. Two numerical examples are provided to illustrate the effectiveness of the proposed approach.

Jun Wang - One of the best experts on this subject based on the ideXlab platform.

  • Predictor-Based Neural Dynamic Surface Control for Uncertain Nonlinear Systems in Strict-Feedback Form
    IEEE Transactions on Neural Networks and Learning Systems, 2017
    Co-Authors: Zhouhua Peng, Dan Wang, Jun Wang
    Abstract:

    This paper presents a predictor-based Neural Dynamic surface control (PNDSC) design method for a class of uncertain nonlinear systems in a strict-feedback form. In contrast to existing NDSC approaches where the tracking errors are commonly used to update Neural network weights, a predictor is proposed for every subsystem, and the prediction errors are employed to update the Neural adaptation laws. The proposed scheme enables smooth and fast identification of system Dynamics without incurring high-frequency oscillations, which are unavoidable using classical NDSC methods. Furthermore, the result is extended to the PNDSC with observer feedback, and its robustness against measurement noise is analyzed. Numerical and experimental results are given to demonstrate the efficacy of the proposed PNDSC architecture.

  • Road network extraction: a Neural-Dynamic framework based on deep learning and a finite state machine
    International Journal of Remote Sensing, 2015
    Co-Authors: Jun Wang, Jingwei Song, Mingquan Chen, Zhi Yang
    Abstract:

    Extracting road networks from very-high-resolution VHR aerial and satellite imagery has been a long-standing problem. In this article, a Neural-Dynamic tracking framework is proposed to extract road networks based on deep convolutional Neural networks DNN and a finite state machine FSM. Inspired by autonomous mobile systems, the authors train a DNN to recognize the pattern of input data, which is an image patch extracted in a detection window centred at the current location of the tracker. The pattern is predefined according to the environment and associated with the states in the FSM. A vector-guided sampling method is proposed to generate the training data set for the DNN, which extracts massive image-direction pairs from the imagery and existing vector road maps. In the tracking procedure, the size of the detection window is determined by a fusion strategy and the extracted image patches represent the orientation features of the road local environment that can be recognized by the trained DNN. The reactive unit in FSM associates states with behaviours of the tracker while continually modifying the orientation to follow the road and generating a sequence of states and locations. In this way, our framework combines the DNN and FSM. DNN acts as a key component to recognize patterns from a complex and changing environment; FSM translates the recognized patterns to states and controls the behaviour of the tracker. The results illustrate that our approach is more accurate and efficient than the traditional ones.

Fangfei Li - One of the best experts on this subject based on the ideXlab platform.

  • observer based adaptive Neural Dynamic surface control for a class of non strict feedback stochastic nonlinear systems
    International Journal of Systems Science, 2016
    Co-Authors: Zhaoxu Yu, Shugang Li, Fangfei Li
    Abstract:

    The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The Neural network NN approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the Dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment or mean square. Two simulation examples are given to illustrate the effectiveness of the proposed control design.