Air-to-air target tracking is a challenging task for practical military and civilian applications. In this study, an unbiased converted measurement Kalman filter in Earth-centred Earth-fixed (ECEF) ...coordinates (E-UCMKF) is proposed for air-to-air target tracking, which takes account for noisy measurements from the airborne radar and the inertial navigation system/global navigation satellite system. The unbiased measurement conversion approach from spherical coordinates to ECEF coordinates is presented, and the Kalman filter is implemented for target state estimation with the unbiased converted measurements and the corresponding measurement error covariances. Besides, when range-rate measurements are available to the processor, a sequential unbiased converted measurement non-linear filter in ECEF coordinates with range-rate measurements (E-SUCMNFwR) is developed, which constructs a pseudo measurement to decorrelate the range-rate measurement noise from the unbiased converted measurement noise, and estimates the state sequentially by the E-UCMKF and a modified cubature Kalman filter. Target tracking scenarios with different sensor accuracies are presented to demonstrate the validity of the proposed algorithms. Simulation results validate the consistency of the proposed unbiased measurement conversion. The results also show that the proposed E-UCMKF and E-SUCMNFwR outperform the conventional algorithms in terms of the position and velocity root-mean-square error, demonstrating their effectiveness and practicality.
This article focuses on the adaptive Kalman filtering problem for linear systems with unknown covariances of both dynamic multiplicative noise (multiplicative measurement noise) and additive noises ...(additive process and measurement noises). A recursive-noise adaptive Kalman filter is proposed to estimate both states and covariances of noises by using the variational Bayesian (VB) inference and an indirect method. First, we characterize inverse Wishart priors for both measurement noise covariance and process noise covariance and employ the Student's t -distribution to represent the likelihood function, which is non-Gaussian and affected by mixing multiplicative noise and additive measurement noise. Then, an adaptive Kalman filtering for recursive both noise covariance matrices and dynamic state is proposed following VB inference. Performance analysis for VB procedures and the proposed filter is provided to ensure the convergence and stability. A target tracking example is provided to validate the effectiveness of the proposed filtering algorithm.
This work is concerned with stochastic consensus conditions of multi-agent systems with both time-delays and measurement noises. For the case of additive noises, we develop some necessary conditions ...and sufficient conditions for stochastic weak consensus by estimating the differential resolvent function for delay equations. By the martingale convergence theorem, we obtain necessary conditions and sufficient conditions for stochastic strong consensus. For the case of multiplicative noises, we consider two kinds of time-delays, appeared in the measurement term and the noise term, respectively. We first show that stochastic weak consensus with the exponential convergence rate implies stochastic strong consensus. Then by constructing degenerate Lyapunov functional, we find the sufficient consensus conditions and show that stochastic consensus can be achieved by carefully choosing the control gain according to the noise intensities and the time-delay in the measurement term.
This paper proposes an improved general zeroing neural network (ZNN) model to suppress noise and to enhance the real-time performance of solving time-varying quadratic programming (TVQP) problems. ...The proposed model allows nonconvex activation functions (AFs) and has noise suppression characteristics, i.e., the nonconvex constrained noise suppressed ZNN (NCNSZNN) model. Theoretical analyses show that the developed NCNSZNN model converges globally to an accurate solution to the TVQP problem and is robust in the case of measurement noise (MN). Illustrative examples and comparisons are supplied to verify the validity and superiority of the proposed model for online solving TVQP constrained by equalities and inequalities (EAI) with MN.
Studies on moving horizon estimation (MHE) for applications featuring process uncertainties and measurement noises that follow time‐dependent non‐Gaussian distributions are absent from the ...literature. An extended version of MHE (EMHE) is proposed here to improve the estimation for a general class of non‐Gaussian process uncertainties and measurement noises at no significant additional computational costs. Gaussian mixture models are introduced to the proposed EMHE to approximate offline the non‐Gaussian densities of these random variables. Moreover, the proposed EMHE‐based estimation scheme can be updated online by re‐approximating the corresponding Gaussian mixture models when the distributions of noises/uncertainties change due to sudden or seasonal changes in the operating conditions. These updates are not expected to increase the central processing unit times considerably. Illustrative case studies featuring open‐loop operation and closed‐loop control using nonlinear model predictive control have shown that the practical features offered by EMHE resulted in significant improvements in state estimation and online control.
In the permanent magnet synchronous motor speed regulation system, the dynamic performance of the conventional active disturbance rejection control (ADRC) system will be deteriorated by using a low ...bandwidth speed filter. To solve this problem, two ADRC controllers considering the speed measurement noise are proposed in this article. One proposed ADRC system is based on the extended state observer (ESO), the other proposed ADRC system is based on phase-locking loop observer (PLLO). In the proposed two ADRC systems, an integrator is employed as the speed filter so that the measured position can be directly used for observing the speed and disturbance without speed calculation. Meanwhile, by using an integrator as the speed filter, the dynamic performance of the proposed ESO-based ADRC system is not affected since it is independent of the speed filter, and the proposed PLLO-based ADRC system has a better rejection ability for the low-frequency disturbance. Experimental results validate the proposed methods.
The state-of-charge (SOC) estimation method currently ignores the measurement error caused by the Battery Management System (BMS). In this paper, the characteristic of LiFePO4 battery is deeply ...studied to explore the relationship between open-circuit-voltage (OCV) and SOC. By the analysis of the characteristic of the curve, the results show that the curve does not change with the battery aging by the capacity correction. Meanwhile, the feature of the charging voltage curve is also analyzed. It is pointed out that the ohmic internal resistance and capacity can be obtained by the transformation of the charging voltage curve, which reduces the workload of the dual extended kalman filter (DEKF) algorithm. Based on the DEKF algorithm, the SOC under constant current and dynamic discharge conditions are estimated. The results show that the estimation error is within 3%. The influence of battery voltage and current measurement noise on the estimation accuracy of the SOC is then analyzed. It is found that the measurement noise increases the SOC estimation deviation. Finally, the open circuit voltage in measurement equation is replaced by the charging voltage. And a new method of combining DEKF algorithm and charging voltage curve for SOC estimation is proposed. The results of the experiments under constant current and dynamic discharge conditions show that the proposed method can eliminate the measurement noise and ensure the accuracy of SOC estimation.
•We derive a new robust KF algorithm, called dynamic noise-aware KF(DAKF).•The steady-state performance of the proposed DAKF is theoretically analyzed.•Simulation results show that DAKF performs well ...in suppressing non-Gaussian noise.
In this work, we focus on the online state estimation problem for linear systems in non-Gaussian measurement noise. Specifically, the measurement noise is modeled as a Gaussian mixture model (GMM). Then, Kalman innovation is used to approximate the current measurement noise, and dynamically perceive its responsiveness to each sub-model of the GMM. The responsiveness from different Gaussian scales is mapped to a new cost function, while the corresponding Kalman filter algorithm is derived. The theoretical steady-state error and computational complexity analysis of the algorithm are also given. The simulation and real experimental results agree with the theoretical predictions and demonstrate the superior performance of the proposed algorithm.
•An iterative method is developed for the joint with many interface DOFs.•The iterative method is also extended for nonlinear joint identification.•The identification of the end-toothed connection ...was conducted.•Simulation and experiment were in good agreement.
Accurate identification of mechanical connections plays a significant role in predicting the dynamic behaviors of assembled structures. Traditional identification methods based on frequency response function (FRF) decoupling may not be suitable for the joints with many interface degrees of freedom (DOFs), especially in the case of insufficient measured FRF data and large experimental noise. In this paper, a new iterative method is proposed to solve this problem. First, the incremental basic formulas on the measured DOFs and pseudo-measured DOFs are derived, respectively. The former avoids the measurements of rotational or interface DOFs whereas the latter is suitable for the identification of the joint with many interface DOFs. Then, the joint properties expressed in terms of mass, stiffness and damping matrices are updated from the solution of the linear equation system combined with the iterative basic identification equations at different frequencies. Thus, the original problem is transformed into estimating the parameters iteratively by minimizing the differences between predicted responses and measured ones, which effectively improves the identification accuracy and numerical stability. Moreover, the iterative method is extended to the identification of nonlinear joints based on describing function theory. The validity and superiority of the proposed method are verified by a simple lumped parameter system. Then, a numerical example of a structure connected with bolted joints, which has a large number of interface DOFs, is simulated to verify the convergence and the robustness of the method to noise. Finally, the joint dynamic properties of an end-toothed connection under different contact states are identified, and the assembly responses are also accurately predicted. The effectiveness and the applicability of the proposed iterative identification method to the real structures are well validated.
•Structure of PID controller with serial compensator (namely PIDC) is utilized.•Novel tuning rules for PIDC-type automatic voltage regulators in terms of two user-supplied parameters are ...derived.•Efficient disturbance rejection is achieved.•The designed controller efficiently handles model uncertainties and the measurement noise.•Experimental verification of the proposed method is provided in a laboratory setting.
This paper presents a novel analytical method for designing the Proportional-Integral-Derivative controller with serial Compensator (PIDC) for automatic voltage regulation (AVR). The primary focus of this method is to address constraints related to robustness and sensitivity to measurement noise. The key concept behind this proposed design method lies in selecting a specific complementary sensitivity function, which is determined by two adjustable parameters, λ affecting performance and N primarily affecting robustness to measurement noise. The ultimate goal of the PIDC controller design is to achieve enhanced rejection of load disturbances and ensure satisfactory reference tracking. The method proposed in this paper offers a combination of flexibility and simplicity, providing new analytical tuning formulas for PIDC controller based on a pair of adjustable parameters affecting the trade-off between performance and robustness. The paper thoroughly analyzes the dependencies of performance and robustness indices on the design parameters, elucidating the associated trade-offs in detail. A comparative analysis with respect to two recently proposed optimal design methods has been performed in a simulation setting. Subsequently, the effectiveness of the proposed analytical method has been experimentally validated using a small-scale synchronous generator in a laboratory setting. The application of the proposed approach, resulting in enhanced suppression of disturbances is improved, as evidenced by improvements in the Integral of Absolute Error (IAE). Additionally, the proposed design method demonstrated robustness against uncertainties in the plant model and the measurement noise.
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