The Experts below are selected from a list of 193245 Experts worldwide ranked by ideXlab platform
Feng Ding - One of the best experts on this subject based on the ideXlab platform.
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highly computationally efficient state filter based on the delta operator
International Journal of Adaptive Control and Signal Processing, 2019Co-Authors: Xiao Zhang, Feng Ding, Erfu YangAbstract:The Kalman filter is not suitable for the state estimation of linear systems with multistate delays, and the extended state vector Kalman filtering algorithm results in heavy computational burden because of the large dimension of the state estimation covariance matrix. Thus, in this paper, we develop a novel state estimation algorithm for enhancing the computational efficiency based on the delta operator. The computation analysis and the Simulation Example show the performance of the proposed algorithm.
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joint state and multi innovation parameter estimation for time delay linear systems and its convergence based on the kalman filtering
Digital Signal Processing, 2017Co-Authors: Feng Ding, Xuehai Wang, Li MaoAbstract:Abstract This paper studies the joint state and parameter estimation problem for a linear state space system with time-delay. A multi-innovation gradient algorithm is developed based on the Kalman filtering principle. To improve the convergence rate, a filtering based multi-innovation gradient algorithm is proposed by using the filtering technique. The analysis indicates that the parameter estimates given by the proposed algorithms converge to their true values under the persistent excitation conditions. A Simulation Example is given to confirm that the proposed algorithms are effective.
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novel data filtering based parameter identification for multiple input multiple output systems using the auxiliary model
Automatica, 2016Co-Authors: Yanjiao Wang, Feng DingAbstract:This communique uses the auxiliary model method to study the identification problem of a multiple-input multiple-output (MIMO) system. For such a MIMO system whose outputs are contaminated by an ARMA noise process (i.e., correlated noise), an auxiliary model based recursive least squares parameter estimation algorithm is presented through filtering input-output data. The proposed algorithm has higher estimation accuracy than the existing multivariable identification algorithm. The Simulation Example is given.
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hierarchical multi innovation stochastic gradient algorithm for hammerstein nonlinear system modeling
Applied Mathematical Modelling, 2013Co-Authors: Feng DingAbstract:Abstract This paper decomposes a Hammerstein nonlinear system into two subsystems, one containing the parameters of the linear dynamical block and the other containing the parameters of the nonlinear static block, and presents a hierarchical multi-innovation stochastic gradient identification algorithm for Hammerstein systems based on the hierarchical identification principle. The proposed algorithm is simple in principle and easy to implement on-line. A Simulation Example is provided to test the effectiveness of the proposed algorithm.
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self tuning control based on multi innovation stochastic gradient parameter estimation
Systems & Control Letters, 2009Co-Authors: Jiabo Zhang, Feng DingAbstract:Abstract This paper uses the multi-innovation stochastic gradient (MISG) algorithm to estimate the parameters of discrete-time systems, and presents an MISG based self-tuning control scheme. Furthermore, we prove that the parameter estimation error converges to zero under persistent excitation, and the parameter estimation based control algorithm can asymptotically achieve virtually optimal control, and ensure that the closed-loop systems are stable and globally convergent. The Simulation Example is included.
Siying Zhang - One of the best experts on this subject based on the ideXlab platform.
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brief paper output feedback stabilization for stochastic nonlinear systems whose linearizations are not stabilizable
Automatica, 2010Co-Authors: Yuanwei Jing, Siying ZhangAbstract:This brief paper investigates the problem of output-feedback stabilization for a class of high-order stochastic nonlinear systems which are neither necessarily feedback linearizable nor affine in the control input. Based on the ideas of the homogeneous systems theory and the adding a power integrator technique, an output-feedback controller is constructed to ensure that the equilibrium at the origin of the closed-loop system is globally asymptotically stable (GAS) in probability. The efficiency of the output-feedback controller is demonstrated by a Simulation Example.
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adaptive backstepping controller design using stochastic small gain theorem
Automatica, 2007Co-Authors: Zhaojing Wu, Siying ZhangAbstract:A more general class of stochastic nonlinear systems with unmodeled dynamics and uncertain nonlinear functions are considered in this paper. With the concept of input-to-state practical stability (ISpS) and nonlinear small-gain theorem being extended to stochastic case, by combining stochastic small-gain theorem with backstepping design technique, an adaptive output-feedback controller is proposed. It is shown that the closed-loop system is practically stable in probability. A Simulation Example demonstrates the control scheme.
Tasawar Hayat - One of the best experts on this subject based on the ideXlab platform.
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fitting the exponential autoregressive model through recursive search
Journal of The Franklin Institute-engineering and Applied Mathematics, 2019Co-Authors: Huan Xu, Ahmed Alsaedi, Tasawar HayatAbstract:Abstract This paper focuses on the recursive parameter estimation methods for the exponential autoregressive (ExpAR) model. Applying the negative gradient search and introducing a forgetting factor, a stochastic gradient and a forgetting factor stochastic gradient algorithms are presented. In order to improve the parameter estimation accuracy and the convergence rate, the multi-innovation identification theory is employed to derive a forgetting factor multi-innovation stochastic gradient algorithm. A Simulation Example is provided to test the effectiveness of the proposed algorithms.
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iterative identification algorithms for bilinear in parameter systems with autoregressive moving average noise
Journal of The Franklin Institute-engineering and Applied Mathematics, 2017Co-Authors: Mengting Chen, Ling Xu, Tasawar Hayat, Ahmed AlsaediAbstract:Abstract This paper considers the identification problems of a bilinear-in-parameter system with autoregressive moving average noise. The basic idea is to use the over-parameterization to transform a system into a linear regressive model, and to present a gradient based and a least squares based iterative algorithms for identifying the system parameters. The numerical Simulation Example is given to demonstrate the effectiveness of the proposed algorithms.
Hanxiong Li - One of the best experts on this subject based on the ideXlab platform.
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fuzzy adaptive sliding mode control for mimo nonlinear systems
IEEE Transactions on Fuzzy Systems, 2003Co-Authors: Shaocheng Tong, Hanxiong LiAbstract:A stable adaptive fuzzy sliding-mode controller is developed for nonlinear multivariable systems with unavailable states. When the system states are not available, the estimated states from a semi-high gain observer are used to construct the output feedback fuzzy controller by incorporating the dynamic sliding mode. It is proved that uniformly asymptotic output feedback stabilization can be achieved with the tracking error approaching to zero. A nonlinear system Simulation Example is presented to verify the effectiveness of the proposed controller.
Jing Chen - One of the best experts on this subject based on the ideXlab platform.
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gradient based parameter estimation for input nonlinear systems with arma noises based on the auxiliary model
Nonlinear Dynamics, 2013Co-Authors: Jing Chen, Yan Zhang, Ruifeng DingAbstract:This paper presents a gradient-based iterative identification algorithm and an auxiliary-model-based multi-innovation generalized extended stochastic gradient algorithm for input nonlinear systems with autoregressive moving average (ARMA) noises, i.e., the input nonlinear Box–Jenkins (IN–BJ) systems. The estimation errors given by the gradient-based iterative algorithm are smaller than the generalized extended stochastic gradient algorithm under same data lengths. A Simulation Example is provided.
