Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics

Fang, Haiyang; Zhu, Guomin; Stojanović, Vladimir; Nie, Rong; He, Shuping; Luan, Xiaoli; Liu, Fei

doi:10.1002/rnc.5350

Cited by 92 publications

(25 citation statements)

References 42 publications

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“…Recently, Stojanovic et al presented a robust identification algorithm for fault detection in the presence of non‐Gaussian noises—application to hydraulic servo drives, 7 studied the state and parameter joint estimation problem of linear stochastic systems in presence of faults and non‐Gaussian noises 8 . Dong et al discussed robust fault detection filter design problem for a class of discrete‐time conic‐type nonlinear Markov jump systems with jump fault signals 9 and Fang et al proposed an adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics 10 …”

Section: Introductionmentioning

confidence: 99%

Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation

Wang

Zhang

2021

Adaptive Control & Signal

115

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Summary This article considers the parameter estimation for a fractional‐order nonlinear finite impulse response system with colored noise. For the fractional‐order systems, the challenge and difficulty are to identify the order and parameters of the systems simultaneously under colored noise disturbances. In order to reduce the problem of redundant parameter estimation, the output form of the system can be expressed by a linear combination of unknown parameters through the separation of the key term separation. A key term separation auxiliary model gradient‐based iterative algorithm is derived by using the negative gradient search. Meanwhile, to achieve the higher estimation accuracy, we propose a key term separation auxiliary model multiinnovation gradient‐based iterative algorithm by utilizing the multiinnovation theory. Finally, the simulation results test the effectiveness of the proposed algorithms.

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Section: Introductionmentioning

confidence: 99%

Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation

Wang

Zhang

2021

Adaptive Control & Signal

115

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“…For a nonlinear Markov jump system, ref. [30] converts the nonlinear terms into linear forms using the neural network linear differential inclusion techniques. Additionally, an asynchronous fault detection filter is given in [31], and sufficient conditions for the stable resultant Markov jump systems are devised.…”

Section: Introductionmentioning

confidence: 99%

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Chen,

Zheng

et al. 2022

Preprint

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The conventional direction of arrival (DOA) estimation methods are performed with multiple receiving channels. In this paper, a changeling DOA estimation problem is addressed in a different scenario with only one full-functional receiving channel. A new unmanned aerial vehicle (UAV) swarm system using multiple lifted reconfigurable intelligent surface (RIS) is proposed for the DOA estimation. The UAV movement degrades the DOA estimation performance significantly, and the existing atomic norm minimization (ANM) methods cannot be used in the scenario with array perturbation. Specifically, considering the position perturbation of UAVs, a new atomic norm-based DOA estimation method is proposed, where an atomic norm is defined with the parameter of the position perturbation. Then, a customized semi-definite programming (SDP) method is derived to solve the atomic norm-based method, where different from the traditional SDP method, an additional transforming matrix is formulated. Moreover, a gradient descent method is applied to refine the estimated DOA and the position perturbation further. Simulation results show that the proposed method achieves much better DOA estimation performance in the RIS-aided UAV swarm system with only one receiving channel than various benchmark schemes.

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“…1,[6][7][8][9][10][11] In case the relationship between input/output data exhibits nonlinear phenomena, a nonlinear model approximation is needed. The nonlinearities can be represented by various models such as basis function expansions, 12 Volterra models, [13][14][15] Gaussian processes, 16,17 neural networks, 18,19 trees, 20,21 and so on. The nonlinear model can also be approximated by a linear model with time-variant parameters via robust Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Noniterative tensor network‐based algorithm for Volterra system identification

Tan

Callafon

2022

Intl J Robust & Nonlinear

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Volterra model serves as one of the powerful alternatives to approximate nonlinear dynamics on the basis of input/output observations. The kernel representation of a Volterra model is attractive since it is linear in the kernel coefficients and always stable. Although the kernel representation suffers from the curse of dimensionality, the demand for storage requirements can be relieved via a tensor network (TN) technique. This allows one to approximate complicated coupled nonlinear dynamics with high degree and even multi-input multi-output (MIMO) Volterra models. The iterative feature of existing TN-based algorithms poses challenges for the algorithms to converge to an appropriate solution with both good prediction accuracy and minor overfitting problems. In this article, noniterative TN-based algorithms with two tuning factors to solve either a linear or ridge regression are proposed for MIMO Volterra system identification. The proposed algorithms do not suffer convergence problems due to its noniterative feature and perform active low-rank approximations to reduce overfitting problems. Simulations compare different algorithms and investigate the effects for different excitation signals.

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Adaptive optimization algorithm for nonlinear Markov jump systems with partial unknown dynamics

Cited by 92 publications

References 42 publications

Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation

Iterative parameter and order identification for fractional‐order nonlinear finite impulse response systems using the key term separation

Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces Aided UAV Swarm

Noniterative tensor network‐based algorithm for Volterra system identification

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