A New Predefined Time Zeroing Neural Network With Drop Conservatism for Matrix Flows Inversion and Its Application

Xiao, Lin; Li, Linju; Huang, Wenqian; Li, Xiaopeng; Jia, Lei

doi:10.1109/tcyb.2022.3225155

Cited by 9 publications

(2 citation statements)

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“…where 1 − Λ2 r is referred to as the potential function on unit quaternions, and it is directly related to the feedback term e in ( 27) by e = −∇(1 − Λ2 r ) Λi [22]. Moreover, the type of Lyapunov function designed as (38) has been demonstrated to exhibit robustness to noise interference, as evidenced in [28].…”

Section: B Nrbo For Estimating Attitude and Gyro Biasmentioning

confidence: 99%

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Nonlinear Robust Bias Observer for MEMS Gyros Using Noninertial Attitude Sensor Measurement

Tang,

Chang,

Wang

et al. 2024

IEEE/ASME Trans. Mechatron.

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In robotic applications, the dynamics sensitivities of commercial-grade micro-electro-mechanical system (MEMS) gyros often exhibit uncertainties that cannot be accurately modeled by linear drift. To address the estimation of these uncertain biases, we propose a novel nonlinear robust bias observer (NRBO) in this article. Unlike existing nonlinear observers for attitude and gyro bias, our proposed method incorporates a dynamics-sensitive gyro bias estimation approach, achieved through the synthesis of the attitude-angular rate nonlinear dynamic coupling (AARNDC) term and the attitude-linear coupling (ALC) term. We highlight the potential advantages of our proposed method, including the asymptotic stability of the NRBO and its robustness against MEMS gyro bias instability, enabled by a rational design of the AARNDC and ALC terms. In addition to gyro bias estimation, we present the attitude estimation within the NRBO framework. Field experimental results, conducted with a cable-driven parallel robot, demonstrate the robustness of the proposed NRBO against bias instability measurement noise. Moreover, the results Manuscript

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Section: B Nrbo For Estimating Attitude and Gyro Biasmentioning

confidence: 99%

“…Therefore, it is crucial to strike a balance while choosing k a . As demonstrated in (28), k a e serves as a feedback term to compensate for gyro outputs ω b ib . In the presence of measurement noise, both k a ΔΨ and k a e appear together, as shown in (49).…”

Section: B Nrbo For Estimating Attitude and Gyro Biasmentioning

confidence: 99%