In vehicle GNSS/INS integrated navigation, robust and adaptivealgorithms have become one of the key technologies for achieving a comprehensive PNT due to their ability to control the gross errors of the observation m...
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In vehicle GNSS/INS integrated navigation, robust and adaptivealgorithms have become one of the key technologies for achieving a comprehensive PNT due to their ability to control the gross errors of the observation model and dynamic model. The Sage-Husa algorithm is widely used in optimizing the Kalman filter due to its ability to estimate the observation or state covariance without prior information. However, the quality of observations in complex environments is prone to large fluctuations, so the averaging method is not suitable for dynamic navigation. To solve this problem, this article designs a double window structure and introduces a time-dependent fading weighted factor. At the same time, a logarithmic form factor constructor is proposed in order to avoid anomalies in the robust and adaptive factor. The traditional innovation adaptive filter is improved and turned into a multi-factor adaptive filter. In this paper, an improved fault detection algorithm is used to combine a robustalgorithm with an adaptivealgorithm to adapt to different gross errors in different scenarios. The experimental results of complex scenarios show that the position RMSE of the improved algorithm in the east, north, and height directions is 0.68 m, 0.71 m, and 1.05 m, respectively, which are reduced by 39.3%, 39.3%, and 70.3% compared to the EKF.
Due to the practical restrictions such as uncertain dynamics, external disturbances and time-varying delays and lack of enough information about the remote environment, the performance of teleoperation systems cannot ...
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Due to the practical restrictions such as uncertain dynamics, external disturbances and time-varying delays and lack of enough information about the remote environment, the performance of teleoperation systems cannot be guaranteed in most cases. However, some practical tasks such as tele-surgery conducted by the nonlinear teleoperation system require high performance. Recent research on teleoperation systems motivates us to propose a new robustadaptive control law for nonlinear bilateral teleoperation systems with time-varying delays, external disturbances and uncertain dynamics in a unified framework. To this end, a novel adaptive torque observer is developed to relax the system from force sensors. The main advantage of this paper is to design properly a mechanism, which is free from joint accelerations due to the acceleration measurement difficulty in robotic systems. Moreover, the key point of the proposed algorithm is to enhance the robust behavior of the system in the presence of various uncertainties by adding an auxiliary term in the control loop. Furthermore, the stability and the convergence of synchronization error to zero are also proven with the aid of both Lyapunov-Krasovskii function and linear matrix inequality. Simulation results emphasize on the effectiveness of the proposed Observed-based control approach in comparison with the related research.
In this study, the authors propose a robust adaptive algorithm for frequency estimation in three-phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the ...
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In this study, the authors propose a robust adaptive algorithm for frequency estimation in three-phase power systems when the voltage readings are corrupted by random noise sources. The proposed algorithm employs the Clarke's transformed three-phase voltage (a complex signal) and augmented complex statistics to deal with both of balanced and unbalanced system conditions. To derive the algorithm, a widely linear predictive model is assumed for the Clarke's transformed signal where the frequency of system is related to the parameters of this model. To estimate the model parameters with noisy voltage reading, they utilise the notions of maximum correntropy criterion and gradient-ascent optimisation. The proposed algorithm has the computational complexity of the popular complex least-mean-squares (CLMS) algorithm, along with the robustness that is obtained by using higher-order moments beyond just second-order moments. They compare the performance of the proposed algorithm with a recently introduced augmented CLMS (ACLMS) algorithm in different conditions, including the voltage sags and presence of impulsive noises and and higher-order harmonics. Their simulation results demonstrate that the proposed algorithm provides improved frequency estimation performance compared with ACLMS especially when the measured voltages are corrupted by impulsive noise.
Proposed is an affine projection sign algorithm with a nonlinear error scalar matrix to improve the robustness and the tracking performance against non-Gaussian impulsive interferences. The error scalar matrix scalars...
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ISBN:
(纸本)9781510806481
Proposed is an affine projection sign algorithm with a nonlinear error scalar matrix to improve the robustness and the tracking performance against non-Gaussian impulsive interferences. The error scalar matrix scalars down the errors of some projection directions in the presence of impulsive noise. The major contribution of the letter is that variable error nonlinearity methods used in normalized least mean square(NLMS) can be applied to the scalar matrix with a little modification. An ideal scalar matrix is presented in the simulation environment of the two component Gaussian mixture noise model. Although a closed-form solution of the ideal matrix cannot be obtained in practice, it provides us a heuristic consideration about how to design the scalar matrix and theoretically best learning curves that the proposed method can achieve. We also discuss a practical method to approximate the optimal learning curve. Improved performance of the proposed algorithm is demonstrated in a system identification scenario.
This paper presents a novel robustadaptive filtering scheme based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibili...
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This paper presents a novel robustadaptive filtering scheme based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The statistical noise information is quantitatively formulated as stochastic property closed convex sets by the simple design formulae developed in this paper. A simple set-theoretic inspection also leads to an important statistical reason for the sensitivity to noise of the affine projection algorithm (APA). The proposed adaptivealgorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces that are highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. The numerical examples show that the proposed adaptive filtering scheme realizes dramatically fast and stable convergence for highly colored excited speech like input signals in severe noise situations.
In this paper, we propose a robust adaptive algorithm for impulsive noise suppression. The perturbation of the input signal as well as the perturbation of the estimation error are restricted by M-estimation. The thres...
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In this paper, we propose a robust adaptive algorithm for impulsive noise suppression. The perturbation of the input signal as well as the perturbation of the estimation error are restricted by M-estimation. The threshold used in M-estimation is obtained from the proposed adaptive variance estimation. Simulations show that the proposed algorithm is less vulnerable to the impulsive noise than the conventional algorithm.
A multi-channel adaptive active noise control system is studied in this paper, which involves a multiple number of primary noise sources, noise reference microphones, secondary control sources and cancelling error det...
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A multi-channel adaptive active noise control system is studied in this paper, which involves a multiple number of primary noise sources, noise reference microphones, secondary control sources and cancelling error detection microphones. First, a general structure of the multi-channel active noise control system is clarified and a perfect cancellation condition by a feedforward controller is presented. Secondly, a new LMS type of robust adaptive algorithm for updating the feedforward controllers is proposed, which can assure a stability of the adaptation and keep the each cancelling error within a tight bound by efficiently using prior information on each upper bound of the uncertainty terms. In experimental studies using an air duct, the proposed algorithm is compared with an ordinary filtered-x algorithm which does not always possess the property of stable convergence.
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