Rapid Relocation Method for Mobile Robot Based on Improved ORB-SLAM2 Algorithm

Hayat

2020

Summary The combined iterative parameter and state estimation problem is considered for bilinear state‐space systems with moving average noise in this paper. There are the product terms of state variables and control variables in bilinear systems, which makes it difficult for the parameter and state estimation. By designing a bilinear state estimator based on the Kalman filtering, the states are estimated using the input‐output data. Furthermore, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. A state estimator‐based MDW gradient‐based iterative (MDW‐GI) algorithm is proposed to estimate the unknown states and parameters jointly. Moreover, given the extended gradient‐based iterative (EGI) algorithm as a comparison, the MDW‐GI algorithm can reduce the impact of noise to parameter estimation and improve the parameter estimation accuracy. The numerical simulation examples validate the effectiveness of the proposed algorithm.

Section: Discussionmentioning

confidence: 99%

Moving data window gradient‐based iterative algorithm of combined parameter and state estimation for bilinear systems

Liu

Hayat

2020

“…Equations (58)-(70) form the state filter-based hierarchical least squares algorithm for the linear state-space model with multistate delays. The methods proposed in this paper can combine other identification approaches [40][41][42][43] to study parameter estimation problems of different systems with colored noises [44][45][46][47][48] and can be applied to other literatures [49][50][51][52] such as signal modeling and communication networked systems. [53][54][55][56][57][58][59] Remark 9.…”

Section: The Sf-hls Algorithmmentioning

confidence: 99%

Adaptive parameter estimation for a general dynamical system with unknown states

Xiao

2020

123

Summary This paper is concerned with the design of a state filter for a time‐delay state‐space system with unknown parameters from noisy observation information. The key is to investigate new identification algorithms for interactive state and parameter estimation of the considered system. Firstly, an observability canonical state‐space model is derived from the original model by linear transformation for the purpose of simplifying the model structure. Secondly, a direct state filter is formulated by minimizing the state estimation error covariance matrix on the basis of the Kalman filtering principle. Thirdly, once the unknown states are estimated, a state filter–based recursive least squares algorithm is proposed for parameter estimation using the least squares principle. Then, a state filter–based hierarchical least squares algorithm is derived by decomposing the original system into several subsystems for improving the computational efficiency. Finally, the numerical examples illustrate the effectiveness and robustness of the proposed algorithms.

“…the forgetting factor BSO-HMISG algorithm (BSO-FF-HMISG) algorithm is formulated to improve the convergence speed and the parameter estimation accuracy of the BSO-HMISG algorithm. The proposed state and parameter estimation algorithms for bilinear systems can combine other estimation algorithms 45,46 and the mathematical tools [47][48][49][50][51] and strategies [52][53][54][55][56] to explore new identification methods of other linear, bilinear, and nonlinear systems with colored noises [57][58][59][60][61] and can be applied to other fields such as information processing [62][63][64][65][66] and communication. [67][68][69][70][71] Remark 6.…”

Section: The Hmisg Algorithmmentioning

confidence: 99%

Recursive parameter estimation methods and convergence analysis for a special class of nonlinear systems

Xiao

2019

113

Summary This paper is concerned with the joint estimation of states and parameters of a special class of nonlinear systems, ie, bilinear systems. The key is to investigate new estimation methods for interactive state and parameter estimation of the considered system based on the interactive estimation theory. Because the system states are unknown, a bilinear state observer is established based on the Kalman filtering principle. Then, the unavailable states are updated by the state observer outputs recursively. Once the state estimates are obtained, the bilinear state observer–based hierarchical stochastic gradient algorithm is developed by using the gradient search. For the purpose of improving the convergence rate and the parameter estimation accuracy, a bilinear state observer–based hierarchical multi‐innovation stochastic gradient algorithm is proposed by expanding a scalar innovation to an innovation vector. The convergence analysis indicates that the parameter estimates can converge to their true values. The numerical example illustrates the effectiveness of the proposed algorithms.