基于PNDSC 的欠驱动AUV编队控制器设计

针对含有模型不确定与未知海洋环境扰动下的欠驱动自主水下航行器(AUV)的编队控制问题, 提出一种基于预估器的神经网络动态面(PNDSC) 控制算法. 将动态面法引入控制器的设计中, 采用神经网络逼近AUV模型中的不确定项与海洋环境的扰动, 并结合预估器设计了神经网络权值的离散迭代更新率. Lyapunov 稳定性分析表明, 闭环系统所有信号是一致最终有界的. 仿真结果验证了所提出方法的有效性.

     

Pseudo‐predictor feedback control of discrete‐time linear systems with a single input delay

This paper studies the problems of stabilization of discrete‐time linear systems with a single input delay. By developing the methodology of pseudo‐predictor feedback, which uses the (artificial) closed‐loop system dynamics to predict the future state, memoryless state feedback control laws are constructed to solve the problem. Necessary and sufficient conditions are obtained to guarantee the stability of the closed‐loop system in terms of the stability of a class‐difference equations. It is also shown that the proposed controller achieves semi‐global stabilization of the system if its actuator is subject to either magnitude saturation or energy constraints under the condition that the open‐loop system is only polynomially unstable. Numerical examples have been worked out to illustrate the effectiveness of the proposed approaches. Copyright © 2015 John Wiley & Sons, Ltd.     

On input‐to‐state stability‐based design for leader/follower formation control with measurement delays

This paper addresses the design of low‐level controllers for leader–follower formations of nonholonomic vehicles in the presence of bounded measurement delays. The concept of input‐to‐state stability is extended to encompass the effect of bounded delays and restrictions on the input. A method is proposed to integrate a Smith predictor in a backstepping design on the basis of nested saturations and nonlinear small‐gain assignment, which allows for time delays in the feedback loop. Robustness analysis under uncertain bounded time delays is provided, and design tradeoffs resulting from the use of bounded controls are discussed. Illustrative simulations are shown to validate the design and robustness analysis in the context of a simple leader–follower trailing control problem. Copyright © 2012 John Wiley & Sons, Ltd.     

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.     

基于模型参考自适应Smith预估器的反馈式AGC厚度控制系统

针对具有纯时间延迟特性的反馈式AGC(automatic gauge contro1)系统,利用Smith预估器对纯时间延迟具有补偿作用的原理,建立了模型参考白适应Smith预估器反馈式AGC控制系统模型,给出了控制系统结构.推导了自适应调节律和设计算法,结合预估器模型,计算得到轧件出口厚度偏差值的表达式.仿真结果表明,模型参考自适应Smith预估器反馈式AGC系统对铝薄板带材轧制出口厚度偏差控制具有很好的控制效果,解决了反馈式AGC厚度控制由于纯时间延迟特性对控制性能产生很大影响的问题,消除了出口厚度波动现象,响应速度加快,可以满足高精度铝薄板带材轧制生产的控制要求.     

基于预测的自适应边缘检测新方法

图像的边缘是图像最基本的特征之一,边缘检测是提取图像特征的重要手段.首先利用梯度调节预测器(Gradient Adjusted Predictor:GAP)对图像进行预测,然后针对预测得到的误差图像,提出了一种基于梯度均值直方图的自适应阈值选取方法,利用得到的阈值来分类边缘与非边缘;为了获得单像素边缘,利用细化算法对边缘图像进一步细化,得到最后的边缘图像.仿真结果表明,与其它方法相比,本文方法检测到的边缘边界特征细腻、连续,定位精度较高,得到的实验结果比较理想.     

基于SMITH预估的神经网络再热汽温控制

电厂锅炉的再热汽温是机组安全、经济运行的重要参数之一,必须控制在一定范围内.而电厂再热汽温被控对象是具有大惯性、大滞后,并且常规PID控制难以取得良好的控制效果.针对这一特点,提出了一种RBF--Smith预估控制算法,该算法利用了基于RBF整定的PID控制提高对被控对象参数变化的自适应能力和Smith预估控制能够克服被控对象的大迟延特性,并对RBF--Smith预估控制用Matlab在不同工况下进行仿真试验,仿真结果表明所设计的控制系统的性能较常规PID控制有较大的提高,证明了控制方案的有效性.     

预估型氨法脱硫控制系统研究

燃煤发电系统中氨法脱硫控制系统具有时变、大延时、大惯性的特点。针对典型的大延迟系统,文中提出基于Smith预估器的脱硫控制系统,通过抵消系统被控对象中的纯滞后环节来提高系统的实时性。Smith预估器依赖准确的PID模型,系统中PID参数整定仍存在不确定性。利用SOA算法对PID参数进行整定,将绝对误差积分ITAE作为适应度函数,通过对适应度值的不断寻优,找到最佳的比例积分微分系数组合。仿真结果表明,相较于Smith预估器控制下的脱硫系统,基于SOA的Smith预估PID控制系统跟随性好且反应迅速,调节时间缩小为原来的9%,峰值时间缩小为原来的5%,具有较小的超调量,系统的实时性和稳定性得到保证。上述结果证明该算法对工程控制系统有效,并具有良好的应用前景。     

船舶综合管理平台网络下减摇鳍预估控制

为满足船舶网络化、信息化和智能化的要求,研究了综合管理平台网络下减摇鳍的控制问题.在传统减摇鳍系统模式的基础上,提出了综合管理平台下减摇鳍网络控制系统的模型.分析了由于平台网络引入的诱导时延对系统性能的影响.以均值预测法为基础,引入预测控制算法中“误差预测”思想,设计了一类新的时延辨识器.在此基础上,对于海浪干扰和网络...     

一类具有数据包丢失的长时延网络控制系统的分析与设计

针对一类网络控制系统,其网络诱导时延大于采样周期且存在数据丢包,研究了其稳定性和控制器的设计.在控制器端设计了一个状态预测器,其预测状态根据传感器的数据进行更新,然后用来确定控制作用.分析了预测误差的收敛性,证明了分离原理是成立的,并依据分离原理分别设计了状态预测器和控制器,给出了参数化设计方法.仿真例子验证了方法的有效性.