基于自抗扰控制的交联电缆悬垂控制系统

交联悬垂控制系统是一个非线性、时变、强耦合多干扰的复杂控制系统,为提高悬垂控制系统的抗干扰能力和鲁棒性。提出一种基于自抗扰的悬垂控制策略。文中分别针对下牵引交流异步电机的速度环、电流环以及磁链还进行自抗扰控制器的设计,通过所设计的自抗扰控制器有效地提高了系统的悬垂控制精度。考虑到悬垂控制系统是一个非线性时延系统,时延的存在一定程度上降低了系统的控制性能。本文将史密斯预估技术引入到自抗扰控制器的设计中,设计了一种基于史密斯预估器的输出预估自抗扰控制器,有效地降低了时延的影响,提高了系统的鲁棒性和抗干扰性能。文中最后给出了仿真分析,仿真结果验证了所设计的复合控制器的有效性。     

Smith预估控制系统抗扰性能的改善

针对常规Smith预估控制系统扰动回调慢的弱点,从控制结构上进行改进,介绍接受扰动量输入的Smith预估控制A系统和加入微分反馈的Smith预估控制B系统。其中。后者符合二自由度控制思想。对二者从理论和工程实现两方面进行分析测试,与常规Smith预估控制系统进行曲线数据对比,给出二者动态性能的具体差别：A系统在扰动和控制通道模型一致的情况下,与常规Smith预估控制系统相比扰动回调时间和动态误差分别减小近70％和85％,B系统分别减小约50％和20％。     

A modified Smith predictive scheme based back-propagation neural network approach for FOPDT processes control

In this paper, a novel control scheme to deal with process uncertainties in the form of disturbance loads and modelling errors, as well as time-varying process parameters is proposed by applying the back-propagation neural network (BPNN) approach. A BPNN predictive controller that replaces the entire Smith predictor structure is initially trained offline. Lyapunov direct method is used to prove that the convergence of this BPNN is guaranteed by selecting a suitable learning rate during the learning process. However, the Smith predictor based BPNN control is an off-line training based algorithm, which is a time consuming method and requires a known process plant input from the controller. A desired control input to the process is difficult to obtain for the training of the network. As a result a group of proper training data (target control inputs and outputs) can hardly be provided. In order to overcome this problem, a BPNN with an on-line training algorithm is introduced for the control of a First Order plus Dead Time (FOPDT) process. The stability analysis is carried out using the Lyapunov criterion to demonstrate the network convergence ability. Simulation results show that this proposed online trained neural Smith predictor based controller provides excellent robustness to process modelling errors and disturbance loads, and high adaptability to time varying processes parameters.     

基于单目视觉的车道线分离警告算法研究

车道线检测技术是自动驾驶领域的研究热点。通过对车道线图像基本特征的分析,研究了一种基于车道线识别的稳健方法。在初始检测部分,图像预处理中采用大津法来适应不同的光照条件,并根据车道线特征进行车道线初始检测。然后,用Hough变换检测车道线。在车道线跟踪部分,用Kalman滤波器预测参数,动态建立感兴趣区域,在此区域搜索车道线。并根据一定的失效判决模块,验证跟踪结果。最后,根据交通工具与车道线之间的距离来判决偏离情况。实验证明,该算法具有较高的实时性和稳健性。     

基于预测误差格型滤波器的航迹关联算法研究

在K近邻域(K-NN)航迹关联算法的基础上,提出一种基于预测误差格型滤波器的关联算法,当融合中心仅获得状态估计值,而没有状态估计协方差矩阵时,将状态估计值通过格型滤波器,利用得到的预测误差代替协方差,为判决门限的选择提供依据,仿真结果表明,这种算法是有效的.     

基于最小二乘支持向量机预测器的传感器故障检测与数据恢复

本文介绍了最小二乘支持向量机（LS-SVM）回归的基本原理，提出了一种基于LS-SVM回归的时间序列预测器，并将其用于传感器的故障检测和数据恢复。论述了LS-SVM预测器的实现方法和步骤，并且将其应用于压力传感器的故障检测和数据恢复，同线性神经网络预测器、RBF神经网络预测器和BP神经网络预测器的比较结果表明，LS-SVM预测器具有更高的预测精度，更好的外推能力，计算效率最高，因此，LS-SVM预测器是传感器故障检测和短期数据恢复的一种有效方法。     

一种变结构单神经元--Smith预估控制在再热汽温中的仿真研究

本文针对300MW机组喷燃器倾角-再热汽温被控对象,具有大迟延和参数时变,而难于控制的特点,提出了一种基于变结构的单神经元-Smith预估控制算法,该控制算法采用Smith预估控制克服被控对象的大迟延特性,采用变结构的单神经元PID控制提高对被控对象参数变化的自适应能力,提高控制系统的响应速度,并抑制被控参数严重超调.仿真试验表明该控制算法具有良好的控制品质,抗干扰能力和鲁棒性,综合性能明显优于常规的PID控制.     

Prediction of the impact of processing critical conditions for Listeria monocytogenes growth in artisanal dry-fermented sausages (salami) through a growth/no growth model applicable to time-dependent conditions

L. monocytogenes data from artificially contaminated salami batter samples and from published data were used to evaluate a newly-developed, time-dependent probability parameter (P_t), which was then incorporated in the existing and already validated simplified modelling approach (SMA), taking into account time in assessing the pathogen growth probability. The SMA was further expanded to quantify the impact of each preservative factor (f) to growth probability. The modified SMA (mSMA) was used to evaluate those environmental conditions that could decrease the safety status of artisanal salami. The dependence of the probability of growth, and of the growth of L. monocytogenes on temperature, pH, a_w, lactic acid and indigenous lactic acid bacteria of salami was predicted and the impact of each factor was quantified under dynamically changing conditions. Characteristics were measured in 5 salami batches and for two of them the predicted final population size was higher than the European Commission (EC) tolerated level for L. monocytogenes (100 CFU g⁻¹). Applying the mSMA to potential scenarios that included the contribution of starter cultures, the onset of Listeria inhibition was predicted earlier, preventing the pathogen to exceed its critical limit. Given its prediction capability, the mSMA is expected to assist small-sized enterprise users in the effective management of preventive and corrective measures in salami processing.