Fault tolerant system based on non-integers order observers: Application in a heat exchanger

This work presents a fault-tolerant (FT) scheme based on the application of non-integer order observers also called fractional observers, the case of study is a double pipe countercurrent heat exchanger (HE). The aim of the FT is to detect sensors faults as soon as possible, and to provide a healthy signal in order to replace the faulty sensor signal by the fractional observer estimation. To develop the FT scheme a bank of high gain fractional order observers (HGFOO) is proposed. The Riemann-Liouville (RL) fractional derivative definition is used to solve each fractional observer. Experimental measures from a HE were used to test the performance of the fractional observers and the control scheme. The results show the robustness of the proposed observers.     

A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries

State of charge(SOC) estimation for lithium ion batteries plays a critical role in battery management systems for electric vehicles. Battery fractional order models(FOMs) which come from frequency-domain modelling have provided a distinct insight into SOC estimation. In this article, we compare five state-of-the-art FOMs in terms of SOC estimation. To this end, firstly, characterisation tests on lithium ion batteries are conducted, and the experimental results are used to identify FOM parameters. Parameter identification results show that increasing the complexity of FOMs cannot always improve accuracy. The model R(RQ)W shows superior identification accuracy than the other four FOMs. Secondly, the SOC estimation based on a fractional order unscented Kalman filter is conducted to compare model accuracy and computational burden under di erent profiles, memory lengths, ambient temperatures, cells and voltage/current drifts. The evaluation results reveal that the SOC estimation accuracy does not necessarily positively correlate to the complexity of FOMs. Although more complex models can have better robustness against temperature variation, R(RQ), the simplest FOM, can overall provide satisfactory accuracy. Validation results on di erent cells demonstrate the generalisation ability of FOMs, and R(RQ) outperforms other models. Moreover, R(RQ) shows better robustness against truncation error and can maintain high accuracy even under the occurrence of current or voltage sensor drift.     

Intelligent fault monitoring and diagnosis in electrical machines

The aim of this paper is to develop an intelligent diagnosis method for fault detection and isolation in induction motors. We consider failures in three components of induction motor: bearing, stator winding and rotor winding. Firstly, a model-based nonlinear observer in the proposed method is designed based on available information. The fault detection decision is carried out by comparing the model-based observer speed with their signatures. Secondly, multiple state observers are constructed based on possible fault function set. The fault isolation decision is made by checking each residual generated by observer state estimation. Finally, simulation tests are given to verify the effectiveness of the proposed fault diagnosis scheme.     

New hybrid sensorless speed of a non-salient pole PMSG coupled to wind turbine using a modified switching algorithm

The paper focuses on the design of position and speed observers for the rotor of a non-salient pole permanent magnet synchronous generator (NSPPMSG) coupled to a wind turbine. With the random nature of wind speed this observer is required to provide a position and speed estimates over a wide speed range. The proposed hybrid structure combines two observers and a switching algorithm to select the appropriate observer based on a modified weighting coefficients method. The first observer is a higher-order sliding mode observer (HOSMO) based on modified super twisting algorithm (STA) with correction term and operates in the medium and nominal wind speed ranges. The second observer is used in the low speed range and is based on the rotor flux estimation and the control by injecting a direct reference current different to zero. The stability of each observer has been successfully assessed using an appropriate Lyapunov function. The simulation results obtained show the effectiveness and performance of the proposed observer and control scheme.     

A flexible current tracking control of sensorless induction motors via adaptive observer

In this article, an interconnected adaptive observer-based backstepping control method is proposed to achieve sensorless current control without persistent excitation condition. Based on the stability theories of mathematics, an effective observer utilized to estimate the fluxes and external load is given. It has a good foundation for breaking the limitation caused by the coupling between the control process and the parameter estimation course. The characteristic of the uncoupling can make the control range for currents more wider. Due to the effective online tracking and the good adaptability of the designed observers, the control effect shows good robustness to the parameters. Finally, numerical simulations are provided to intuitively illustrate the effectiveness and the superiority of the theoretical results.     

Evaluation of discrete-time well-conditioned state observers

Model-based monitoring systems based on state observer theory often have poor performance with respect to accuracy, bandwidth, reliability (false alarms), and robustness. Previous works have investigated quantitatively the above limitations from the machine monitoring viewpoint and have developed a design methodology for discrete-time well-conditioned state observers. In this paper, the estimation performance of well-conditioned observers is demonstrated on a DC spindle system designed and built for this purpose. The results show that the robustness of the estimate is similar to that obtained with the well-known Kalman filtering technique. Additional simulation-based examples show that the transient as well as steady-state error robustness to perturbations is better than or equal to Kalman filter performance depending on the nature of the modeling error. Because of this robustness, the well-conditioned observer for discrete-time systems is an important technique for the development of improved machine monitoring systems.     

带有迟滞非线性的气动运动模拟平台自抗扰轨迹跟踪控制

针对带有迟滞非线性的气动运动模拟平台的轨迹跟踪提出了带有切换扩张状态观测器的自抗扰控制方法。气动运动模拟平台的迟滞非线性特性主要指气动人工肌肉在正向充气反向放气时长度和拉力曲线的差异。针对此特性设计了切换扩张状态观测器来估计和补偿模型非线性,分别对于气动人工肌肉正反向充放气的不同模型采用不同的观测器增益,以提高状态估计效果,减小跟踪误差。进一步,设计了状态误差反馈控制器,得到了基于切换扩张状态观测器的二阶非线性动态系统全局有界稳定的充分条件。最后实验结果证实了所设计的切换扩张状态观测器的实际效果。     

基于安时积分和无迹卡尔曼滤波的锂离子电池SOC估算方法研究

基于Thevenin等效模型建立二阶RC等效电路模型，通过混合脉冲动力功率特性测试实验获取电池脉冲放电数据，进行电池等效电路参数辨识。为弥补锂离子电池荷电状态在90%~100%区间时电池模型参数辨识拟合误差而引起其估算误差的缺陷，综合采用安时积分与无迹卡尔曼滤波估算电池荷电状态。使用硬件在环仿真测试平台及环境模拟测试平台进行电池管理系统设计，在不同工况下对电池进行荷电状态估算，结果表明荷电状态估算误差范围为-1.5%~1.0%，该方法估算精度较高，效果理想。     

Estimation method of state-of-charge for lithium-ion battery used in hybrid electric vehicles based on variable structure extended kalman filter

Since the main power source of hybrid electric vehicle(HEV) is supplied by the power battery, the predicted performance of power battery, especially the state-of-charge(SOC) estimation has attracted great attention in the area of HEV. However, the value of SOC estimation could not be greatly precise so that the running performance of HEV is greatly affected. A variable structure extended kalman filter(VSEKF)-based estimation method, which could be used to analyze the SOC of lithium-ion battery in the fixed driving condition, is presented. First, the general lower-order battery equivalent circuit model(GLM), which includes column accumulation model, open circuit voltage model and the SOC output model, is established, and the off-line and online model parameters are calculated with hybrid pulse power characteristics(HPPC) test data. Next, a VSEKF estimation method of SOC, which integrates the ampere-hour(Ah) integration method and the extended Kalman filter(EKF) method, is executed with different adaptive weighting coefficients, which are determined according to the different values of open-circuit voltage obtained in the corresponding charging or discharging processes. According to the experimental analysis, the faster convergence speed and more accurate simulating results could be obtained using the VSEKF method in the running performance of HEV. The error rate of SOC estimation with the VSEKF method is focused in the range of 5%to 10%comparing with the range of 20%to 30%using the EKF method and the Ah integration method. In Summary, the accuracy of the SOC estimation in the lithium-ion battery cell and the pack of lithium-ion battery system, which is obtained utilizing the VSEKF method has been significantly improved comparing with the Ah integration method and the EKF method. The VSEKF method utilizing in the SOC estimation in the lithium-ion pack of HEV can be widely used in practical driving conditions.     

Output feedback tracking control of uncertain robot manipulators via higher-order sliding-mode observer and fuzzy compensator

This paper develops a new method to control uncertain robot manipulators by using only position measurements. The controller is designed based on a combination of a computed torque controller (CTC) with a higher-order sliding-mode observer and a fuzzy compensator. First, three higher-order sliding-mode (SM) observers (second-order SM, third-order SM and third-order SM linear (TOSML) observers) are designed and compared to verify whether the TOSML observer is the best for observing velocity and identifying uncertainty. A combined CTC-TOSML controller was then designed. Although this controller scheme can overcome the drawbacks of conventional CTCs, its tracking performance can still be improved. To enhance capability of the tracking performance, a CTC-TOSML controller plus fuzzy compensator called a CTC-TOSML-Fuzzy controller is proposed. The proposed controller increases the potential of the CTC for real robot applications. Finally, computer simulation results on a PUMA560 robot are discussed to verify the effectiveness of the proposed strategy.     

磁传感器偏航角诊断滤波方法研究

在磁干扰环境中,磁传感器解算的飞行器偏航角会出现偏差。针对磁干扰导致的估计误差问题,面向模型不确定性飞行器系统,提出了基于非线性扩张状态观测器的磁传感器偏航角诊断滤波方法。该方法首先设计了一种双参数型非线性扩张状态观测器,降低了参数复杂性;然后基于该观测器设计改进型观测器残差法,对磁干扰数据进行诊断过滤;最后,设计了基于误差预测的改进型互补滤波器,通过融合滤波进一步抑制磁干扰。静态仿真实验结果表明,本文所设计的诊断过滤方法数据匹配率超过94%,融合滤波结果相位超前且更为平滑;动态飞行实验结果表明,该诊断滤波方法有效抑制了磁干扰对四旋翼飞行器偏航角估计的影响,增强了飞行器的稳定性和抗磁扰能力。     

基于组合模型的锂电池参数辨识和电池荷电状态在线联合估计

针对电池荷电状态(SOC)估算过程中开路电压与SOC之间的迟滞效应以及充放电电流和端电压中噪声的影响，提出了基于组合模型的Frisch 方案双滤波（FSDF）法。先通过一阶RC等效电路模型结合Preisach离散模型建立新的模型，随后采用Frisch 方案对模型的输入输出进行噪声方差估计，滤除部分输入输出噪声，最后使用扩展卡尔曼滤波结合无迹卡尔曼滤波进行参数实时更新和电池单体SOC估算。实验证明，FSDF方法对锂电池SOC估算结果与Frisch方案递推最小二乘无迹卡尔曼滤波法等其他方法相比，具有精度高、鲁棒性好等特点。     

Reset observers alleviating the peaking and the robustness tradeoffs: A case study on force estimation in teleoperation

The peaking phenomenon is an undesirable effect appearing in observers and destroying controller performance. Several solutions have been proposed to mitigate peaking in state estimation. The literature shows that reset or impulsive observers are superior to linear (Luenberger) observers. However, the comparisons are based on particular choices of linear observers. This paper investigates this issue. First, comparative frameworks are proposed based on two traded-off performance indices: ensemble maximum-peak versus ensemble settling time for nominal conditions, and ensemble settling time versus size of the error asymptotic invariant set for quadratically bounded uncertain plants. Next, performance limitations of linear observers are represented by Pareto-optimal boundaries. In this way, not previously considered in the literature as far as known, the superiority of the chosen reset observer is more rigorously assessed. The framework is finally applied to force estimation in haptic teleoperation.     

Detection of broken rotor bars in induction motors using nonlinear Kalman filters

This paper presents a model-based fault detection approach for induction motors. A new filtering technique using Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF) is utilized as a state estimation tool for on-line detection of broken bars in induction motors based on rotor parameter value estimation from stator current and voltage processing. The hypothesis on which the detection is based is that the failure events are detected by jumps in the estimated parameter values of the model. Both UKF and EKF are used to estimate the value of rotor resistance. Upon breaking a bar the estimated rotor resistance is increased instantly, thus providing two values of resistance after and before bar breakage. In order to compare the estimation performance of the EKF and UKF, both observers are designed for the same motor model and run with the same covariance matrices under the same conditions. Computer simulations are carried out for a squirrel cage induction motor. The results show the superiority of UKF over EKF in nonlinear system (such as induction motors) as it provides better estimates for rotor fault detection.     

Fractional observer to estimate periodical forces

In the present work we propose a fractional state observer with constant gain to estimate the periodical force exerted on a mechanical system by measuring only its displacement. The state observer is designed from both the Fourier series that approximates the periodical force and the equations of the damped harmonic oscillator that represents the behavior of the system. Specifically, the reconstruction of the force is carried out from the estimates of the series coefficients, which in fact are part of the dynamical system that composes the observer. Adams-Bashforth-Moulton method is used to compute the fractional derivatives of the observer in the Liouville-Caputo sense. Experiments based on real data are presented to show the advantages of using a fractional observer in the reconstruction of forces.     

[动力电池]基于双时间尺度扩展卡尔曼粒子滤波算法的电池组单体荷电状态估计

为实现对电池组单体荷电状态(SOC)的精确估算,首先对锂电池组单体建立增强自校正(ESC)模型,然后根据锂电池ESC模型建立电池组平均模型和各单体SOC差异模型,再对其用双时间尺度的扩展卡尔曼粒子滤波(EKPF)算法来估算电池组平均SOC值和各单体差异SOC值,从而得到电池组中各单体SOC值。对12节锂电池串联电池组进行SOC估算实验,结果表明,基于双时间尺度EKPF算法的电池组单体SOC估计方法可实现对单体SOC的精确估计,且该方法比双时间尺度扩展卡尔曼滤波算法和扩展卡尔曼滤波(EKF)算法具有更高的估算精度。     

一种退役锂电池健康状态估计方法

针对退役锂电池健康状态估计效率较低的现状,提出一种快速、有效的估计方法。首先采用3阶RC等效电路模型描述电池特性得出状态方程,确保电池模型精确性,同时引入电池荷电状态SOC(State of charge)和欧姆内阻(R₀)作为状态方程参数。其次利用区域概念,计算出特定的区域容量与区域电压,减少电池参数估计所需要的数据、时间。然后通过扩展卡尔曼滤波(Extended kalman filtering)算法估计电池参数SOC和R₀,进而对电池健康状态(State of health, SOH)进行估计。最后,利用电池测试设备(Arbin-BT2000)对18650电池进行充放电实验,验证该方法的可行性。实验结果证明SOH估计所需参数明显减少,使得电池数据测量所需时间明显缩短,并且估计误差不超过4%,误差较小,说明所提出方法能快速、有效地估算出电池SOH。     

Fault Detection of continuous time linear switched systems using combination of Bond Graph method and switching observer

In this paper, a new Fault Detection (FD) scheme based on combination of switching observer and Bond Graph (BG) method for linear continuous time switched systems with Average Dwell Time (ADT) approach is proposed. The proposed scheme is a BG-based two-stage FD system in which the compact state space representation and Global Analytical Redundancy Relations (GARRs) are derived based on the BG model. In the first stage, a switched observer is designed considering disturbance attenuation level, fault sensitivity and rapid fault detection criteria by solving a weighted Linear Matrix Inequality (LMI) optimization problem. Next, a new form of GARRs which is based on output estimation error of the observer and is called Error-based Global Analytical Redundancy Relations (EGARRs) is developed to combine the observer and BG method. The output estimation errors from the observer, which are adequately sensitive to faults and simultaneously the effects of disturbances are attenuated therein, are given to the EGARRs to generate the residuals of the FD system. The proposed method may be used for fault detection of switched linear systems with ADT based on the BG model of the system. Finally, two case studies including a two-tank system and a buck converter-driven DC motor are considered to show the efficiency and real-time implementation of the proposed method.     

无刷直流电机参数自适应负载观测器设计

在分析无刷直流电机负载观测器存在的电机参数变化时不能对负载进行可靠观测问题的基础上,设计了基于Lyapunov稳定性的参数自适应负载观测器,讨论了该自适应观测器只能对电机单一参数辨识的算法缺陷。提出综合采用电机电阻在线测量和自适应负载观测器技术,获得了对无刷电机多参数变化的辨识和负载的精确观测效果。对采用该负载观测器补偿的电机驱动液压泵转速控制系统进行了仿真分析。仿真数据表明:笔者设计的电阻在线测量无刷直流电机自适应负载观测器算法能快速跟踪参数变化,实现对电机负载转矩的精确观测。     

An adaptive algorithm applied to a design of robust observer

Primary goal of adaptive observers would be to estimate the true states of a plant. Identification of unknown parameters is of secondary interest and is achieved frequently with the persistent excitation condition of some regressors. Nevertheless, two problems are linked to each other in the classical approaches to adaptive observers; as a result, we get a good state estimate once after a good parameter estimate is obtained. This paper focuses on the state estimation without parameter identification so that the state is estimated regardless of persistent excitation. In this direction of research, Besancon (2000) recently summarized that most of adaptive observers in the literature share one common canonical form, in which unknown parameters do not affect the unmeasured states. We enlarge the class of linear systems from the canonical form of (Besancon. 2000) by proposing an adaptive observer (with additional dynamics) that allows unknown parameters to affect those unmeasured states. A recursive algorithm is presented to design the proposed dynamic observer systematically. An example confirms the design procedure with a simulation result.