Parameter identification and position control for helical hydraulic rotary actuators based on particle swarm optimization

It is difficult to obtain an accurate mathematical model in electro-hydraulic servo control system, due to the nonlinear factors such as dead zone, saturation, flow coefficient, and friction. Hence, a parameter identification algorithm, combining recursive least squares (RLS) with modified nonlinear particle swarm optimization (NPSO) algorithm, is proposed. On this basis, another improved NPSO algorithm is also put forward, aiming at searching for the optimal proportional–integral (PI) controller gain of the nonlinear hydraulic system while giving comprehensive consideration to the system performance indexes. The system identification experiments and position tracking control are conducted, respectively. As indicated by the comparison with the least squares (LS), RLS, PSO, and RLS–LPSO results, the proposed method shows higher identification and control accuracy.     

基于积分宽度法测量发光二极管结温方法的研究

本文提出了一种基于光谱积分宽度法来测量发光二极管(light emitting diode, LED)结温的新方法,并进行了理论分析和实验研究。本方法主要分为光谱数据采集、定标函数的测定和结温测量三个过程。首先,为了测量成本的降低和精度的提高而采用在正常工作电流下采集LED光谱数据,并通过采用不同温度下的光谱积分宽度与选定的某一基准状态下的值逐差可得到线性度达0.99以上的定标函数,并通过此定标函数可实时测定任意状态下的结温。其次,为了比较本方法测量结温的精确性,分别对单色和白光LED采用本方法和业界主流的正向电压法,通过自行设计的基于积分宽度法结温测量系统和美国Mentor Graphics公司的T3Ster型仪器的测量结果进行比较,两种方法测出的结温最大偏差为2.1℃,在可接受的误差范围内。实验结果表明积分宽度法测结温具有高效便捷且低成本的的特点,具有一定的应用前景。     

Fixed-time distributed optimization for multi-agent systems with external disturbances over directed networks

This article considers the fixed-time distributed optimization problem of multi-agent systems with external disturbances, in which the global optimization objective is a convex combination of local objective functions. To solve this issue, a directed communication network is carefully designed, and an integral sliding mode control protocol is proposed based on the gradient of global objective function first. Moreover, two distributed optimal protocols are designed by using the gradient and the Hessian matrix of local objective function, respectively. By employing Lyapunov stability theory, graph theory, convex analysis, and inequality techniques, we prove that all proposed protocols can make agents achieve consensus and converge accurately to the optimal solution of the considered problem in some fixed-time intervals. Finally, some numerical simulations are given to verify the feasibility of the theoretical results.     

Event-triggered-based integral reinforcement learning output feedback optimal control for partially unknown constrained-input nonlinear systems

In this paper, an adaptive output feedback event-triggered optimal control algorithm is proposed for partially unknown constrained-input continuous-time nonlinear systems. First, a neural network observer is constructed to estimate unmeasurable state. Next, an event-triggered condition is established, and only when the event-triggered condition is violated will the event be triggered and the state be sampled. Then, an event-triggered-based synchronous integral reinforcement learning (ET-SIRL) control algorithm with critic-actor neural networks (NNs) architecture is proposed to solve the event-triggered Hamilton–Jacobi–Bellman equation under the established event-triggered condition. The critic and actor NNs are used to approximate cost function and optimal event-triggered optimal control law, respectively. Meanwhile, the event-triggered-based closed-loop system state and all the neural network weight estimation errors are uniformly ultimately bounded proved by Lyapunov stability theory, and there is no Zeno behavior. Finally, two numerical examples are presented to show the effectiveness of the proposed ET-SIRL control algorithm.     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.     

深度学习屈光检测方法研究

屈光不正是一种非常常见且对视功能发育有严重危害的眼科问题。准确与方便的屈光检测技术,对于及时发现屈光不正问题以及采取相应措施进行干预具有非常重要的意义。目前的屈光筛查设备虽然能较快进行屈光检测,但主要存在两个问题：检测准确度较低,对被测者配合度要求较高。因此,提出一种新的屈光检测方法,此方法使用基于偏心摄影验光原理的光学系统获取人脸面部近红外图像,使用图像处理技术对面部近红外图像进行处理,得到左右瞳孔图像和瞳孔位置信息,使用提出的结合了深度可分离卷积和SE模块的混合数据多输入神经网络模型进行训练与屈光度的计算。与传统偏心摄影验光原理的屈光检测方法相比,此方法有望随着数据集的扩增而达到更高的准确度,并且此方法将瞳孔位置信息作为模型的输入,可以解决传统算法对被测者配合度要求较高的问题。该研究是对屈光检测新方法的一种有益探索,使用此方法有利于屈光筛查更便利地进行,为实现非接触自助式的屈光筛查提供基础。     

非零和微分博弈系统的事件触发最优跟踪控制（英文）

近年来,对于具有未知动态的非零和微分博弈系统的跟踪问题,已经得到了讨论,然而这些方法是时间触发的,在传输带宽和计算资源有限的环境下并不适用.针对具有未知动态的连续时间非线性非零和微分博弈系统,本文提出了一种基于积分强化学习的事件触发自适应动态规划方法.该策略受梯度下降法和经验重放技术的启发,利用历史和当前数据更新神经网络权值.该方法提高了神经网络权值的收敛速度,消除了一般文献设计中常用的初始容许控制假设.同时,该算法提出了一种易于在线检查的持续激励条件(通常称为PE),避免了传统的不容易检查的持续激励条件.基于李亚普诺夫理论,证明了跟踪误差和评价神经网络估计误差的一致最终有界性.最后,通过一个数值仿真实例验证了该方法的可行性.     

考虑电能质量优化的MMC-SST输入级控制策略

针对配电网中固态变压器（SST）网侧电压三相不平衡以及谐波污染时,电流负序分量激增影响网侧电流波形质量进而危及系统正常运行等问题,在SST输入级提出了一种可有效抑制电流负序分量的新型双序控制策略。文中首先介绍了模块化多电平变换器型固态变压器（MMC-SST）的运行原理及输入级数学模型,采用改进型二阶广义积分—正交信号发生器（SOGI-QSG）对网侧电压、电流等电气量进行正、负序分量的分离与提取,通过引入正交中间变量建立了比例复数积分（PCI）控制器的数学模型,并且在两相静止坐标系中的正、负序模型下提出了一种基于PCI的新型双序控制策略,对网侧电流的正、负序分量进行分序控制。最后,在MATLAB/SIMULINK中搭建了MMC-SST的仿真系统,并与传统的PI控制、改进的PI双序控制进行对比分析,结果验证了所提双序控制策略的可行性与优越性。     

基于对抗深度学习的无人机航拍违建场地识别

对违建场地的检测方法主要是通过人工对无人机航拍视频进行检查,存在检测精度低、识别性能差、工作效率低的问题。提出一种结合空间变换网络与Fast RCNN的生成对抗网络ASTN-Fast RCNN,通过深度学习与无人机航拍视频相结合自动识别检测处在建设初期的违建场地。将空间变换网络作为生成器,生成Fast RCNN目标检测器难以识别的旋转形变样本,并通过目标检测器与生成器的对抗式训练,提高检测器的鲁棒性。实验结果表明,该方法能够有效提高对无人机航拍违建场地的识别性能。     

Path integral solution for nonlinear systems under parametric Poissonian white noise input

In this paper the problem of the response evaluation in terms of probability density function of nonlinear systems under parametric Poisson white noise is addressed. Specifically, extension of the Path Integral method to this kind of systems is introduced. Such systems exhibit a jump at each impulse occurrence, whose value is obtained in closed form considering two general classes of nonlinear multiplicative functions. Relying on the obtained closed form relation liking the impulses amplitude distribution and the corresponding jump response of the system, the Path Integral method is extended to deal with systems driven by parametric Poissonian white noise. Several numerical applications are performed to show the accuracy of the results and comparison with pertinent Monte Carlo simulation data assesses the reliability of the proposed procedure.