Kalman Filter Algorithm For Estimation Research Articles

Robust extended state estimation for nonlinear complex networks with colored noise

In this article, a robust extended state Kalman filter estimation algorithm for nonlinear complex networks is designed, fully considering the state coupling and colored noise among system nodes. The innovative measurement information subtraction method at adjacent moments deals with colored noise, making it more in line to meet engineering requirements. The states information of system nodes is redundant, and the collected data are processed by the quantizer, which greatly reduces the impact of network bandwidth constraints. Based on the bounds of the dynamic change rate of the nonlinear function rather than the bounds of the procedure itself, the upper bound of the error covariance is obtained, and real-time gain optimization is realized. Finally, the rationality and accuracy of the estimator are verified by simulation. According to the control variable method, the effects of the quantization density and quantization level of the quantizer on the stability of system are briefly demonstrated.

Optimisation of delay tolerance in wireless sensor networks based on unscented Kalman filter estimation

Delay tolerance in wireless sensor network can be applied to complex and harsh communication environment. In order to improve the communication performance of delay tolerance in wireless sensor network, the optimisation of delay tolerance in wireless sensor network based on Unscented Kalman filter estimation is studied. Firstly, the model of delay tolerance in wireless sensor network is established according to the theory of dynamics and position constraints, and then an unscented Kalman filter estimation algorithm is used. Through analysing the basic state and measurement equation of delay tolerance in wireless sensor network model, the measurement equation of round-trip delay model is obtained. The pre handover time estimated by unscented Kalman filter is set, to effectively reduce the pre handover time and energy consumption, and optimise the delay wireless sensor network. The experimental results show that the delay tolerance of wireless sensor network optimised by the proposed method is within 30 ms under different observation noises, and the packet loss rate and energy consumption are low.

Combined estimation of the state of charge of a lithium battery based on a back-propagation– adaptive Kalman filter algorithm

The precise estimation of the battery’s state of charge is one of the most significant and difficult techniques for battery management systems. In order to improve the accuracy of estimation of the state of charge, the forgetting-factor recursive least-squares method is used to achieve online identification of the model parameters based on the first-order RC battery model, and a back-propagation neural-network-assisted adaptive Kalman filter algorithm is proposed. A back-propagation neural network is established by using the MATLAB neural network toolbox and is trained offline on the basis of the battery test data; then the trained back-propagation neural network is used to realize the online optimized results of an adaptive Kalman filter algorithm for estimation of the state of charge. The proposed methodology for estimation of the state of charge is demonstrated using experimental lithium-ion battery module data in dynamic stress tests. The results indicate that, in comparison with the common adaptive Kalman filter algorithm, the back-propagation–adaptive Kalman filter algorithm significantly improved precise estimation of the state of charge.

Estimation of the vehicle speed in the driving mode for a hybrid electric car based on an unscented Kalman filter

Preconditions for the effective implementation of a control strategy in an automobile’s active safety system rely on timely and accurate information on the vehicle’s running status, especially the vehicle speed. However, the vehicle speed cannot be measured directly without using advanced onboard sensors or specialized test equipment owing to their high mass production cost. In this study, a model-based method for estimating the vehicle speed in different driving modes is conducted in real time, which makes full use of information on the driving wheel’s torque for a four-wheel-drive hybrid car. First, a simulation platform that integrates the models of the powertrain system, the non-linear seven-degree-of-freedom vehicle dynamics system and the dynamic UniTire model is established. Next, an unscented Kalman filter algorithm is adopted to estimate the vehicle speed, and the estimated results and the simulated results are compared under different driving modes. Finally, real-vehicle tests at medium and low speeds are performed using a prototype car. The simulations and the test results confirm that the proposed unscented Kalman filter estimation algorithm without a linearizing truncation process can estimate the vehicle speed with high precision and strong adaptability.

A modified NARMAX model-based self-tuner with fault tolerance for unknown nonlinear stochastic hybrid systems with an input–output direct feed-through term

A modified nonlinear autoregressive moving average with exogenous inputs (NARMAX) model-based state-space self-tuner with fault tolerance is proposed in this paper for the unknown nonlinear stochastic hybrid system with a direct transmission matrix from input to output. Through the off-line observer/Kalman filter identification method, one has a good initial guess of modified NARMAX model to reduce the on-line system identification process time. Then, based on the modified NARMAX-based system identification, a corresponding adaptive digital control scheme is presented for the unknown continuous-time nonlinear system, with an input–output direct transmission term, which also has measurement and system noises and inaccessible system states. Besides, an effective state space self-turner with fault tolerance scheme is presented for the unknown multivariable stochastic system. A quantitative criterion is suggested by comparing the innovation process error estimated by the Kalman filter estimation algorithm, so that a weighting matrix resetting technique by adjusting and resetting the covariance matrices of parameter estimate obtained by the Kalman filter estimation algorithm is utilized to achieve the parameter estimation for faulty system recovery. Consequently, the proposed method can effectively cope with partially abrupt and/or gradual system faults and input failures by the fault detection.

3차원 재구성과 추정된 옵티컬 플로우 기반 가려진 객체 움직임 추적방법

A mirror neuron is a neuron fires both when an animal acts and when the animal observes the same action performed by another. We propose a method of 3D reconstruction for occluded object motion tracking like Mirror Neuron System to fire in hidden condition. For modeling system that intention recognition through fire effect like Mirror Neuron System, we calculate depth information using stereo image from a stereo camera and reconstruct three dimension data. Movement direction of object is estimated by optical flow with three-dimensional image data created by three dimension reconstruction. For three dimension reconstruction that enables tracing occluded part, first, picture data was get by stereo camera. Result of optical flow is made be robust to noise by the kalman filter estimation algorithm. Image data is saved as history from reconstructed three dimension image through motion tracking of object. When whole or some part of object is disappeared form stereo camera by other objects, it is restored to bring image date form history of saved past image and track motion of object.

An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control

An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

Active fault tolerant control using state-space self-tuning control approach

This paper presents a new fault tolerant control scheme for unknown multivariable stochastic systems by modifying the conventional state-space self-tuning control approach. For the detection of faults, a quantitative criterion is developed by comparing the innovation process errors occurring in the Kalman filter estimation algorithm, which, for faulty system recovery, a weighting matrix resetting technique is developed by adjusting and resetting the covariance matrices of the parameter estimate obtained in the Kalman filter estimation algorithm to improve the parameter estimation of the faulty systems. The proposed method can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection. The modified state-space self-tuning control scheme can be applied to the multivariable stochastic faulty system without requiring prior knowledge of system parameters and noise properties.