Coordinated control strategy of DC microgrid with hybrid energy storage system to smooth power output fluctuation

Intl J Robust & Nonlinear

Lin

et al. 2020

This article is concerned with the parameter identification of output-error bilinear-parameter models with colored noises from measurement data. An auxiliary model least squares-based iterative method is developed through the overparameterization model. It examines the difficulty of estimating the overparameterized vector, which usually presents a heavy computational burden in the identification process. To overcome this drawback, a parameter separation technique is introduced and the nonlinear model is reformulated as a refined identification model through eliminating the crossmultiplying terms. In this regard, a parameter separation least squares-based iterative (PS-LSI) algorithm is derived by avoiding estimating the redundant parameters. On the basis of the PS-LSI algorithm, we derive a maximum likelihood least squares-based iterative method to further improve the numerical accuracy. The identification is dependent on the formulation of a pseudolinear regression relationship, which contains two linear prefilters constructed from the system and noise models. The performance of this proposed method is confirmed by the numerical simulations as well as direct comparisons with other existing algorithms.

Section: The Ml-lsi Algorithmmentioning

confidence: 99%

Maximum likelihood least squares‐based iterative methods for output‐error bilinear‐parameter models with colored noises

Chen

Intl J Robust & Nonlinear

Lin

et al. 2020

“…Obviously, it is larger than that of the 3S-LSI algorithm. The proposed iterative algorithm for a class of observability canonical bilinear systems in this paper can combine some mathematical tools [39][40][41][42][43] to study the parameter estimation problems of different stochastic systems with colored noises [44][45][46][47][48][49][50][51] and can be applied to other fields [52][53][54][55][56][57] such as signal processing and communication networked systems [58][59][60][61] and transportation communication systems, [62][63][64][65] and engineering systems and so on.…”

Section: The Computational Efficiencymentioning

confidence: 99%

Three‐stage least squares‐based iterative estimation algorithms for bilinear state‐space systems based on the bilinear state estimator

Liu

Zhang

Adaptive Control & Signal

et al. 2020

Because of the product item of the control input and the state vector, the identification of bilinear systems is difficult. This paper considers the combined parameter and state estimation problems of bilinear state-space systems. On the basis of the observability canonical form and the model transformation, an identification model with a linear combination of the system parameters is obtained. Using the hierarchical principle, the identification model is decomposed into three submodels with fewer variables, and a three-stage least squares-based iterative (3S-LSI) algorithm is presented to estimate the system parameters. Furthermore, we derive a state estimator (SE) for estimating the unknown states, and present an SE-3S-LSI algorithm for estimating the unknown parameters and states simultaneously. After that, the least squares-based iterative algorithm is presented as a comparison. By analyzing the estimation results and the calculation amount, these two algorithms can identify the bilinear system effectively but the 3S-LSI algorithm can improve the computational efficiency. The simulation results indicate the effectiveness of the proposed algorithms.

“…Moreover, the recursion computation of 2S-LS-MIESG algorithm involves more innovations and data. The proposed 2S-LS-MIESG algorithm in this article can joint some mathematical strategies 59,60 to explore new identification methods of linear and nonlinear stochastic systems [61][62][63][64] and can applied to other fields. [65][66][67][68] The pseudo-code of implementing the 2S-LS-MIESG algorithm is shown in Algorithm 2.…”

Section: E(p T) = E(t) Y(p T)mentioning

confidence: 99%

“…That is, {v(t)} is a white noise sequence with zero mean and variance 2 . (64), if Assumptions (A1) and (A2) hold, and there exist two constants a, b and an integer t 0 such that when t ⩾ t 0 , the following persistent excitation condition holds, The proofs of Theorem given in this article can be done in a similar way to those in Reference 69.…”

mentioning

confidence: 99%

Two‐stage recursive identification algorithms for a class of nonlinear time series models with colored noise

Intl J Robust & Nonlinear

Gan

et al. 2020

This article concentrates on the recursive identification algorithms for the exponential autoregressive model with moving average noise. Using the decomposition technique, we transform the original identification model into a linear and nonlinear subidentification model and derive a two-stage least squares (LS) extended stochastic gradient (ESG) algorithm. In order to improve the parameter estimation accuracy, we employ the multi-innovation identification theory and develop a two-stage LS multi-innovation ESG algorithm. A simulation example is provided to test the effectiveness of the proposed algorithms.