Adaptive neural network control for a class of continuous stirred tank reactor systems

In this paper, the control problem of continuous stirred tank reactors （CSTR） is studied. The considered CSTR are required to contain unknown functions and unknown dead zone input. An adaptive controller that uses the neural networks （NNs） is provided to solve the unknown terms. The proposed approach overcomes the effect of the dead zone input. The dead zone input in the systems is compensated for by introducing a new Lyapunov form and Young＇s inequality. The backstepping procedure is exploited to implement controller design with adaptation laws. The stability is analyzed using Lyapunov method. The performance is examined for CSTR to confirm the effectiveness of the proposed approach based on computer simulation.     

Variable partial-update NLMS algorithms with data-selective updating

We note that some existing algorithms are based on the normalized least-mean square （NLMS） algorithm and aim to reduce the computational complexity of NLMS all inherited from the solution of the same optimization problem, but with different constraints. A new constraint is analyzed to substitute an extra searching technique in the set-membership partial-update NLMS algorithm （SM-PU-NLMS） which aims to get a variable number of updating coefficients for a further reduction of computational complexity. We get a closed form expression of the new constraint without extra searching technique to generate a novel set-membership variable-partial-update NLMS （SM-VPU-NLMS） algorithm. Note that tile SM-VPU-NLMS algorithm obtains a faster convergence and a smaller mean-squared error （MSE） than the existing SM-PU-NLMS. It is pointed out that the closed form expression can also be applied to the conventional variable-step-size partial-update NLMS （VSS-PU-NLMS） algorithm. The novel variable-step-size variable-partial-update NLMS （VSS-VPU-NLMS） algorithm is also verified to get a further computational complexity reduction. Simulation results verify that our analysis is reasonable and effective.     

Observer‐based compensation control of servo systems with backlash

For compensating backlash phenomenon in servo systems, the authors propose an observer method in this paper to estimate both system states and vibration torque before controller design. First, a systematic scheme is given to obtain plant parameters, which is very important in observing system states. This is a parameter estimation principle that gives a crude estimation and computes the differences between the crude and true values. As a result, the precise value of the parameters is obtained by adding together the crude value and the difference. Then, based on the precise estimated parameters, an extended state observer (ESO) is designed to obtain feedback and feedforward signals. Consequently, robust compensation control is achieved by designing an output feedback controller, consisting of a feedback term and a feedforward term. Finally, in order to validate the proposed approach, extensive experiments are performed on a practical servo system with backlash nonlinearity.     

An H∞non‐fragile observer‐based adaptive sliding mode controller design for uncertain fractional‐order nonlinear systems with time delay and input nonlinearity

In this paper the problem of non‐fragile adaptive sliding mode observer design is addressed for a class of nonlinear fractional‐order time‐delay systems with uncertainties, external disturbance, exogenous noise, and input nonlinearity. An H_∞ observer‐based adaptive sliding mode control considering the non‐fragility of the observer is proposed for this system. The sufficient asymptotic stability conditions are derived in the form of linear matrix inequalities. It is proven that the sliding surface is reachable in finite time. An illustrative example is provided which corroborates the effectiveness of the theoretical results.     

一种提取抖动视频中前景目标的新方法

提取视频中的前景目标信息是视频处理领域非常重要的问题,考虑到现实生活中会出现监控摄像头不可避免地会出现晃动或偏移情况,造成监控视频短暂抖动,此时背景图像灰度和纹理信息都会受到较大的影响,从而给后期进一步分析前景信息带来了巨大的困难。为了兼顾纹理特征提取和噪声抑制两方面的要求,针对抖动视频的前景提取问题,提出了一种有效的融合小波变换和在线混合高斯模型的方案。首先运用仿射变换在线逐帧校准,接着利用小波变换对图像去噪,并建立自适应模型迭代上述过程,最后利用在线混合高斯模型提取前景。实验结果表明,与同类方法相比,该算法无论针对单目标还是多目标视频均可以有效去除抖动,得到较好的前景目标提取效果,具有较高的准确性和鲁棒性。     

Research on Variable Structure and Adaptive Control for Strap-on Rocket

固体捆绑运载火箭变结构自适应控制

陈曦,刘玉玺,汪轶俊

（上海宇航系统工程研究所）

创新点说明：

对于新型固体捆绑火箭而言,由于箭体安装固体助推器,存在推力偏差较大、推力同步性较大的问题,导致火箭的结构干扰增大。安装尾翼外干扰增大,如经过大风区时,风干扰对箭体产生较大的影响。同时考虑建立动力学模型的过程中存在的参数不确定性、参数摄动、三通道间耦合的问题,传统的控制方法难以满足新型固体捆绑火箭的要求,因此本文率先将基于模型参考的变结构自适应控制方法应用于固体捆绑运载火箭上。

研究目的：

本文针对固体捆绑运载火箭控制系统的特点,应用变结构自适应控制理论,设计适用于固体捆绑运载火箭的控制系统,保障固体捆绑运载火箭的正常飞行。

研究方法：

（1）建立固体捆绑运载火箭动力学模型,考虑模型中参数不确定性、未建模动态及通道间耦合;

（2）分析固体捆绑运载火箭存在的干扰,例如：四个固体助推带来的推力偏差、推力不同步性、推力线偏斜,质心横移、芯级、发动机及尾翼安装误差、芯级发动机液体燃料晃动、箭体弹性振动等各类结构误差及风干扰等外干扰;

（3）针对固体捆绑运载火箭设计变结构自适应控制律的设计并证明其稳定性;

（4）Simulink仿真分析验证。

结果：

本文使用变结构自适应控制理论设计的控制系统可全局渐进稳定,三通道姿态角跟踪误差较小,姿态角收敛速度较快,系统性能良好,可有效处理各类结构干扰、外干扰、气动参数不确定性及未建模动态等问题,该控制系统的动态特性及控制品质良好,具有工程应用的前景。

结论：

本文将基于模型参考的变结构自适应控制方法应用于新型固体捆绑运载火箭的控制系统的设计中。给出了仅有俯仰方向有大姿态变动的条件下的火箭动力学偏量模型,并在此基础上设计变结构自适应控制律,同时证明了该控制系统的稳定性。本文通过理论及仿真分析,证明了变结构自适应控制理论在新型固体捆绑运载火箭上的应用可行性,具有一定的工程应用前景。

关键词：运载火箭;模型参考;变结构控制;自适应控制

     

智能电网中电动汽车充电的自适应电价控制方法

针对电动汽车的充电提出一种电价控制策略。聚合管理者集中管理电动汽车的电池,并且考虑用电高峰时电网的电能供给有限,通过电价控制调整充电的需求量。采用自适应动态规划,通过在线网络训练,得到最优的电价策略。仿真结果表明,该自适应电价控制方法能够通过学习电动汽车的移动性和充电过程,从而调整实际充电需求量至期望水平,保证智能电网的稳定运行。     

Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations

Due to the complexity of the machine tool structure and the cutting process, the dynamics of machining processes are still not completely understood. This is especially true due to the demand of high-speed machining to increase productivity. In order to model and control these complex processes, new approaches, which can represent complex phenomenon combined with learning ability, are needed. The combined neural–fuzzy approach appears to be ideally suited for this purpose. In this paper, the recently developed fuzzy adaptive network (FAN) is used to model surface roughness in turning operations. The FAN network has both the learning ability of neural network and linguistic representation of complex, not well-understood, vague phenomenon. Furthermore, it can continuously improve the initially obtained rough model based on the daily operating data. To illustrate this approach, a model representing the influences of machining parameters on surface roughness is established and then the model is verified by the use of the results of pilot experiments. Finally, a comparison with the results based on statistical regression is provided.     

未知控制方向的迟滞非线性系统预设自适应控制

针对一类含有迟滞特性的未知控制方向严反馈非线性系统,设计了基于误差变换的反步自适应控制器.首先提出动态迟滞算子来扩展输入空间建立神经网络迟滞模型.然后利用径向基函数(RBF)神经网络逼近未知函数,并引入Nussbaum型函数来解决系统未知控制方向问题.最后采用误差变换将误差限定在预设的范围内,并利用反步法设计自适应控制器.该控制方案不仅能够保证跟踪精度,还可以提高系统暂态和稳态性能.仿真结果表明了控制方案的可行性.     

AFDX总线10M接口电气参数极限适应性测试方法

AFDX(航空电子全双工交换式以太网)作为新型机载数据总线应用于大中型飞机,其通信接口电气参数的极限适应性测试是一项重要测试内容.提出一种10M标准AFDX总线信号发生器实现方案,采用4路非归零(NRZ)编码模拟双极性差分曼彻斯特编码,产生符合ARINC664规约要求的总线数据信号,并实现数据信号的传输速率、差分信号幅度、上升/下降时间等电气特性参数连续可调,满足AFDX总线10M接口电气参数极限适应性测试需求.