On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine

Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine （SVM） for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

Observer-Based Nonlinear Control of A Torque Motor with Perturbation Estimation

This paper presents an observer-based nonlinear control method that was developed and implemented to provide accurate tracking control of a limited angle torque motor following a 50Hz reference waveform. The method is based on a robust nonlinear observer, which is used to estimate system states and perturbations and then employ input-output feedbazk linearization to compensate for the system nonlinearities and uncertainties. The estimation of system states and perturbations allows input-output linearization of the nonlinear system without an accurate mathematical model of nominal plant. The simulation results show that the observer-based nonlinear control method is superior in comparison with the conventional model-based state feedback linearizing controller.

On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter

On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization(SECPSO) ,is presented for the parameter identification in the mechanistic model,and the support vector machines(SVM) method is adopted to establish the nonlinear measurement model.The unscented Kalman filter(UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.

On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine

Biomass is a key factor in fermentation process, directly influencing the performance of the fermenta- tion system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine (SVM) for an on-line biomass estimation was ana- lyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

Torque estimation for robotic joint with harmonic drive transmission based on system dynamic characteristics

In the applications of joint control and robot movement,the joint torque estimation has been treated as an effective technique and widely used.Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output.Through analyzing the structures of the harmonic drive and experiment apparatus,a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter(UKF)is designed and built.Based on research and scheme,torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique.Finally,a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed,and simulation results compared with the measurements of a commercial torque sensor,have verified the effectiveness of the proposed method.

Sensor-less Terrain Estimation and Torque Control of a Wheeled Mobile Robot

As unmanned electric wheeled mobile robots have been increasingly applied to high-speed operations in unknown environments,the wheel slip becomes a problem when the robot is either accelerating,decelerating,or turning at high speed.Ignoring the effect of wheel slip may cause the mobile robot to deviate from the desired path.In this paper a recently proposed method is implemented to estimate the surface conditions encountered by an unmanned wheeled mobile robot,without using extra sensors.The method is simple,economical and needs less processing power than for other methods.A reaction torque observer is used to obtain the rolling resistance torque and it is applied to a wheeled mobile robot to obtain the surface condition in real-time for each wheel.The slip information is observed by comparing the reaction torque of each wheel.The obtained slip information is then used to control the torque of both wheels using a torque controller.Wheel slip is minimized by controlling the torque of each wheel.Minimizing the slip improves the ability of the unmanned electric wheeled mobile robot to navigate in the desired path in an unknown environment,regardless of the nature of the surface.

On-Line Structural Breaks Estimation for Non-stationary Time Series Models

Non-stationary time series could be divided into piecewise stationary stochastic signal. However, the number and locations of breakpoints, as well as the approximation function of the respective segment signal are unknown. To solve this problem, a novel on-line structural breaks estimation algorithm based on piecewise autoregressive processes is proposed. In order to find the "best" combination of the number, lengths, and orders of the piecewise autoregressive (AR) processes, the Akaikes Information Criterion (AIC) and Yule-Walker equations are applied to estimate an AR model fit to the data. Numerical results demonstrate that the proposed estimation algorithm is suitable for different data series. Furthermore, the algorithm is used in a clinical study of electroencephalogram (EEG) with satisfactory results, and the ability to deal with real-time data is the most outstanding characteristic of on-line structural breaks estimation algorithm proposed.

Model Predictive Direct Torque Control of Permanent Magnet Synchronous Motor (PMSM) with Online Parameter Estimation Based on Extended Kalman Filter

Aiming at the torque and flux ripples in the direct torque control and the time-varying parameters for permanent magnet synchronous motor (PMSM), a model predictive direct torque control with online parameter estimation based on the extended Kalman filter for PMSM is designed. By predicting the errors of torque and flux based on the model and the current states of the system, the optimal voltage vector is selected to minimize the error of torque and flux. The stator resistance and inductance are estimated online via EKF to reduce the effect of model error and the current estimation can reduce the error caused by measurement noise. The stability of the EKF is proved in theory. The simulation experiment results show the method can estimate the motor parameters, reduce the torque, and flux ripples and improve the performance of direct torque control for permanent magnet synchronous motor (PMSM).

Electromagnetic Torque Estimation of a Three-Phase Induction Motor for Setting the Feedrate of a Milling Machine

In this paper, it presents a project of a fuzzy controller and a neural estimator to control a coordinate table powered by three-phase induction motor, aiming to implement an intelligent milling system. The position/speed control is performed using vector techniques of three-phase induction machines. The estimation of the motor electromagnetic torque is used for setting the feedrate of the table. The speed control is developed using TS （Takagi-Sugeno） fuzzy logic model and electromagnetic torque estimation using neural network type LMS （least mean square） algorithm. The induction motor is powered by a frequency inverter driven by a DSP （digital signal processor）. Control strategies are implemented in DSP. Simulation results are presented for evaluating the performance of the system.

Position Sensorless Torque Estimation of Synchronous Reluctance Motors Based on Extended-Flux Model without Core-Loss Measurement

This paper proposes an extended-flux model with core-loss resistance of SynRMs （synchronous reluctance motors） and precise torque estimation without core-loss measurement and position encoder. The proposed torque estimation is useful for precise MTPA （maximum torque per ampere） control of position sensorless controlled SynRMs, which is achieved with the assistance of active and reactive powers.

An Improved Proportional Integral Estimator of the Stator Resistance for a Direct Torque Controlled Induction Motor

In this paper, an improved PI （proportional integral） stator resistance estimation for a DTC （direct torque controlled） induction motor is proposed. This estimation method is based on an on-line stator resistance correction regarding the variations of the stator current estimation error. In fact, the input variable of the P1 estimator is the stator current estimation error. The main idea is to tune accurately the stator resistance value relatively to the evolution of the stator current estimation error gradient to avoid the drive instability and ensure the tracking of the actual value of the stator resistance. But there is an unavoidable steady state error between the filtered stator current modulus and its estimated value from the dq model of the machine which is due to pseudo random commutations of the inverter switches. This may deteriorate the performance of the proposed fuzzy stator resistance estimator. An offset has been introduced in order to overcome this problem, for different speed command values and load torques. Simulation results show that the proposed estimator was able to successfully track the actual value of the stator resistance lbr different operating conditions.

四轮独立电驱动越野车驱动力优化控制

为了充分利用四轮独立电驱动越野车各轮转矩独立可控的优势,提高越野车的牵引力和越野能力,提出了一种驱动转矩协调控制策略.根据越野车前后轴载荷预分配驱动电动机转矩,利用基于车轮滑转率-路面附着系数的极值寻求算法实现路面最优滑转率估计.采用比例-积分-微分(proportional-integral-differential, PID)控制-滑模控制(sliding mode control, SMC)和基于状态的驱动力再分配方法实现各轮驱动转矩的协调分配,抑制越野车在恶劣路面条件下的车轮打滑.通过CarSim/MATLAB联合仿真以及硬件在环(hardware-in-the-loop, HIL)测试,进行了爬陡坡、单一附着路面、对开路面及连续起伏路面工况的试验验证.结果表明:提出的控制策略能根据实际工况分配驱动轮的转矩,降低驱动轮滑转率,实现整车驱动力的优化.

基于离合器滑摩扭矩估计的并联式气电混合动力船舶模式切换控制

在并联式气电混合动力船舶由纯电到混动的模式切换过程中,需要通过离合器的滑摩作用来完成动力的耦合,然而离合器滑摩扭矩具有很大的不确定性,导致其名义值偏离实际值,使得基于名义值的模式切换控制失效,造成传动轴系冲击。针对上述问题,提出了一种基于离合器滑摩扭矩估计的模式切换控制方法。采用扰动观测器与未知输入观测器结合的方式实现了离合器滑摩扭矩的估计,并基于估计值设计了驱动电机以及天然气发动机扭矩控制律。仿真与台架试验结果表明,所提的离合器滑摩扭矩估计方法有较好的估计效果,基于估计值的模式切换控制策略能够有效减小轴系冲击。

基于浸入与不变观测器的永磁同步电机无传感器控制

针对永磁同步电机,基于浸入与不变(immersion and invariance,I&I)理论构建反电动势和负载转矩观测器,从而提出无传感控制方案。首先,用低通滤波器对电机的电压与电流信号进行滤波处理;然后,通过分析滤波前后的信号,应用I&I理论建立不变流形,根据不变流形的稳定性与可控性,提出系统自适应律来构建反电动势观测器,并根据得到的反电动势的估计值,采用反正切法求解转子位置和速度的估计值;其次,基于I&I理论利用转速估计值构建负载转矩观测器以得到负载转矩估计值;最后,采用反步法基于估计的转速与负载转矩设计了转速环控制器并证明了闭环稳定性,实现了永磁同步电机的无传感器控制。仿真研究结果表明,在带有未知负载扰动的情况下,与传统PI控制器下的滑模观测器法相比,基于I&I观测器法具有更好动态响应能力和更强的鲁棒性。

Data-driven Robust State Estimation Through Off-line Learning and On-line Matching

To overcome the shortcomings of model-driven state estimation methods, this paper proposes a data-driven robust state estimation (DDSE) method through off-line learning and on-line matching. At the off-line learning stage, a linear regression equation is presented by clustering historical data from supervisory control and data acquisition (SCADA), which provides a guarantee for solving the over-learning problem of the existing DDSE methods;then a novel robust state estimation method that can be transformed into quadratic programming (QP) models is proposed to obtain the mapping relationship between the measurements and the state variables (MRBMS). The proposed QP models can well solve the problem of collinearity in historical data. Furthermore, the off-line learning stage is greatly accelerated from three aspects including reducing historical categories, constructing tree retrieval structure for known topologies, and using sensitivity analysis when solving QP models. At the on-line matching stage, by quickly matching the current snapshot with the historical ones, the corresponding MRBMS can be obtained, and then the estimation values of the state variables can be obtained. Simulations demonstrate that the proposed DDSE method has obvious advantages in terms of suppressing over-learning problems, dealing with collinearity problems, robustness, and computation efficiency.

On-line topical importance estimation:an effective focused crawling algorithm combining link and content analysis

Focused crawling is an important technique for topical resource discovery on the Web.The key issue in focused crawling is to prioritize uncrawled uniform resource locators(URLs) in the frontier to focus the crawling on relevant pages.Traditional focused crawlers mainly rely on content analysis.Link-based techniques are not effectively exploited despite their usefulness.In this paper,we propose a new frontier prioritizing algorithm,namely the on-line topical importance estimation(OTIE) algorithm.OTIE combines link-and content-based analysis to evaluate the priority of an uncrawled URL in the frontier.We performed real crawling experiments over 30 topics selected from the Open Directory Project(ODP) and compared harvest rate and target recall of the four crawling algorithms:breadth-first,link-context-prediction,on-line page importance computation(OPIC) and our OTIE.Experimental results showed that OTIE significantly outperforms the other three algorithms on the average target recall while maintaining an acceptable harvest rate.Moreover,OTIE is much faster than the traditional focused crawling algorithm.

Lateral stability region conservativeness estimation and torque distribution for FWIA electric vehicle steering

Estimation of the lateral stability region and torque distribution on steering is very important to improve stability in lateral handling for all wheel drive electric vehicles.Based on the built-nonlinear vehicle dynamic model,the lateral stability region of the vehicle related to steering is estimated using Lyapunov function.We obtained stable equilibrium points of non-straight driving according to the estimated lateral stability region and also reconstructed the Lyapunov function matrix,which proved that the closed-loop system composed of yaw rate and lateral velocity is satisfied with negative definite property.In addition,the designed controller dynamically allocates the drive torque in terms of the vertical load and slip rate of the four wheels.The simulation results show that the estimated lateral stability region and the designed controller are satisfactory in handling stability performance against different roads and vehicle parameters.

Kalman Filter-Based Fusion Estimation Method of Steering Feedback Torque for Steer-by-Wire Systems

Universal challenge lies in torque feedback accuracy for steer-by-wire systems,especially on uneven and low-friction road.Therefore,this paper proposes a fusion method based on Kalman filter that combines a dynamics-reconstruction method and disturbance observer-based method.The dynamics-reconstruction method is designed according to the vehicle dynamics and used as the prediction model of the Kalman filter.While the disturbance observer-based method is performed as an observer model of the Kalman filter.The performance of all three methods is comprehensively evaluated in a hardware-in-the-loop system.Experimental results show that the proposed fusion method outperforms dynamics reconstruction method and disturbance observer-based method.Specifically,compared with the dynamics-reconstruction method,the root mean square error is reduced by 36.58%at the maximum on the flat road.Compared with the disturbance observer-based method,the root mean square error is reduced by 39.11%at the maximum on different-friction and uneven road.

并联式混合动力发动机神经网络法转矩预测与闭环控制

利用实际发动机的标定数据搭建了GT-Suite及Matlab/Simulink联合仿真模型,建立了基于进气和发动机状态参数的预测转矩反馈协同控制模块。对比了ANN法和现有发动机的标定脉谱插值预测(MAP)法2种方法下发动机稳态及瞬态转矩变化、升降挡等工况预测的结果误差,结果表明:稳态工况下MAP法较为可靠,低、中、高3种发动机转速下转矩预测波动小,误差比ANN法低1.31%、1.09%和1.52%;实际瞬态转矩跃变及阶跃工况下,ANN法较MAP法误差低5.62%和1.32%,升降挡工况下低1.93%和0.84%。

基于三阶超螺旋滑模观测器算法的机器人力矩估计方法

本文研究了六自由度(6-DOF)机器人关节力矩估计问题.为了提高机器人关节力矩估计精度,本文设计了一种三阶超螺旋滑模关节力矩观测器.在对观测器系统进行稳定性分析时,本文设计了一种新的Lyapunov函数证明了观测器闭环系统的稳定性.与已有的机器人力矩观测器结果不同,该力矩观测器仅以机器人关节角度作为观测器输入信号,减少了对机器人系统信息的依赖.最后,以6-DOF工业机器人为对象,开展实验验证.实验结果表明,与传统线性力矩观测器相比,本文提出的滑模力矩观测器估计误差小,验证理论的正确性.