Robust Student's t-based trajectory tracking for inspection wall-climbing robot

Lai, Xin; Guorui, Zhu; Chambers, Jonathon A.

doi:10.1109/icris52159.2020.00017

Cited by 1 publication

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where   k k p z |x is the measurement likelihood PDF, and its function is formulated as follows, (13) where ( )…”

Section: The Gaussian Approximate Filter (Gaf)mentioning

confidence: 99%

“…Although EKF exists dependent on first order linearization of system models to propagate state mean and covariance, linear processes and system accuracy decrease thus resulting in performance degradation and failure of running results [10]. However, researchers have made a lot of efforts to expand the scope of application of this technology [8][9][10][11][12][13]. Especially adaptive algorithm combined with EKF has been used to improve the accuracy of the system model and solve divergence problems caused by a single use of EKF.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Fuzzy Neural Network Based On Progressive Gaussian Approximate Filter with Variable Step Size

Zhu

2022

ITC

Self Cite

View full text Add to dashboard Cite

The nonlinear filtering problem is a hot spot in robot navigation research. As an advanced method that can effectively improve the robustness and accuracy of the system, the progressive Gaussian approximate filter with variable step size (PGAFVS) still has some shortcomings, how to resolve the nonlinear filtering problem in the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy, which becomes the purpose of this paper. This paper formulates the processing of trajectory tracking measurement noise problem as a Kalman filtering procedure and the measurement noise covariance matrix in controller, is jointly estimated based on the progressive Gaussian approximate filter (PGAF), after that, PGAFVS can be deduced. Then we proposed an adaptive fuzzy and backpropagation neural network controller based on PGAFVS (AFNPGA-VS) that can improve the application of tightly coupled integration under the premise of the prior uncertainty and further promote robustness high measurement accuracy. The simulation results show that the proposed algorithm outperforms the state-of-the-art methods.

show abstract

“…where   k k p z |x is the measurement likelihood PDF, and its function is formulated as follows, (13) where ( )…”

Section: The Gaussian Approximate Filter (Gaf)mentioning

confidence: 99%