A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

A passivity‐based simple adaptive synergetic control for a class of nonlinear systems

For a class of nonlinear systems with the representation {A(x), B(x), C} and where the system parameters and dynamics are unknown, a simple adaptive synergetic controller ensuring the asymptotic convergence of the system to a desired manifold is proposed based on the technique of simple adaptive control (SAC). It is well known that the design of the synergetic control (SC) law requires a thorough knowledge of the system parameters and dynamics. Such problem obstructs the synthesis of the SC law and the designer is prompted to pass through the estimation methods, which, in turn, poses a problem of increasing the computation time of the control algorithm. To cope with this problem, a solution is proposed by modifying the original SC law to develop an SAC‐like adaptive SC law without the need of prior knowledge of the system. The stability of the proposed adaptive controller is formally proven via the Lyapunov approach. Experimental application to a quadrotor system is given to validate the theoretical results.     

基于混合远景和近景区域暗通道算法的图像去雾研究

为了提高图像去雾效果,提出混合远景和近景区域暗通道算法。首先建立远景和近景区域暗通道,为近景区域最小值通道和远景区域最小值通道之和,不同区域设置不同的调节系数;接着估计大气光值,通过混合暗通道远景区域的前50个像素点颜色值的平均值获得,增设校正系数;然后导向滤波,采用自适应滑动窗口优化透射率;最后给出了算法流程。实验仿真显示所提算法去雾图像视觉效果较好,定量分析指标对比度、可见边对比度值较优。     

基于Smith预估补偿的聚丙烯反应器自适应控制系统研究

针对含有时滞的聚丙烯多区循环反应器温度串级控制系统为复杂的高阶系统，被控对象的滞后时间较大，且受到反应器内部放热、入口循环气温度、夹套水流量等干扰的影响，系统过程控制较为困难。通过以一阶惯性环节加纯滞后来逼近高阶系统，应用模型参考自适应控制算法，使自适应机构能够在线的整定反应器串级控制副回路的Smith预估器，使副回路Smith预估器的动态特性与副被控对象的动态特性接近一致；同时，根据期望的性能指标，在串级控制主回路设置一个参考模型，再次应用自适应算法，使得主回路被控对象的输出尽可能跟踪参考模型的输出。仿真结果表明，该控制系统对于具有时滞的聚丙烯反应器温度控制是有效的。     

步长自适应的测量矩阵迭代优化方法

在压缩感知中，降低传感矩阵的列相干性可以提高重构精度。因为稀疏字典一般是固定的，所以目前主要通过优化测量矩阵来间接降低传感矩阵列相干性。提出一种改进的测量矩阵优化算法，使用梯度下降法更新测量矩阵并结合Barzilai-Borwen方法以及Armijo准则，使步长能够在迭代中自适应调整并保证算法收敛性。仿真实验表明，所提出的方法具有更快的收敛速度并且能够得到更优的测量矩阵。     

基于自适应融合的手掌静脉增强方法

针对掌脉轮廓不清晰，图像对比度低、亮度低，进而导致识别性能降低的现象，提出一种自适应融合的手掌静脉增强方法。首先，基于暗原色先验（DCP）去雾算法，根据掌脉图像变异系数自适应选择去雾系数，得到DCP增强图像，并且基于部分子块重叠直方图均衡（POSHE）算法得到POSHE增强图像；然后，将图像分为16个子块，依据图像灰度均值与标准差确定各子块权重；最后，根据各子块权重对DCP和POSHE增强图像进行自适应融合，得到最终增强图像。该方法既保留了DCP算法在增强图像对比度和亮度的同时不引入明显噪声的优点，又保留了POSHE算法在增强图像对比度和亮度的同时不损失局部细节的特点；同时，两者的自适应融合既解决了DCP图像阴影部分掌脉缺失现象，又削弱了POSHE产生的块效应。在对两个公开库和自建库分别进行的实验中，三个数据库的等错误率分别为0.0004、0.0472、0.0579，识别率分别为99.98%、94.27%、92.05%。实验结果表明，与现有的图像增强方法相比，该方法降低了等错误率，提高了识别精度。     

基于改进关联规则算法的警情数据分析

针对传统Apriori算法挖掘警情数据的缺点，提出一种改进的Apriori算法。该算法首先在关联规则发现阶段引入权值参数，设计支持度阈值函数，以挖掘不常发生的重大案情发生规律；然后提出一种压缩矩阵优化算法，将数据压缩存储在只有0或1的矩阵中，并用2个数组来记录矩阵中每一行及每一列1的总数，可多次压缩矩阵，提升挖掘效率；最后将改进的算法用于实际的警情数据挖掘分析，给出关联规则挖掘结果。实验表明，改进算法不仅执行效率较传统算法有所提升，且针对警情数据的挖掘结果准确性也有所提高。     

Distributed model reference adaptive containment control of heterogeneous uncertain multi-agent systems

In this paper, a distributed model reference adaptive control (MRAC) design framework is proposed for containment control of heterogeneous uncertain multi-agent systems (MAS). Both groups of leaders and followers are considered to have general linear dynamics with the leaders subject to bounded external inputs and the followers subject to uncertain system dynamics. Two distributed adaptive control protocols are developed under this framework. The first protocol assumes measurable leaders’ input signals for a subset of the followers, and employs distributed observers with state-feedback adaptive controllers to achieve exact containment control performance. The second protocol incorporates robust adaptive control with nonlinear compensator techniques to handle a more challenging scenario of unmeasurable bounded leaders’ inputs. Convergence of the containment control errors to an arbitrarily adjustable neighborhood of the origin is guaranteed with the second protocol. The proposed MRAC framework provides a promising alternative solution over the prevailing cooperative output regulation framework for heterogeneous linear MAS containment control. It enables us to handle more general system settings under more stringent control environments with limited accessibility of leaders’ information and uncertain follower dynamics. Effectiveness and usefulness of the proposed approaches are demonstrated through extensive simulation studies, including an application to containment control of multiple nonholonomic mobile robots.     

Distributed adaptive neural networks leader-following formation control for quadrotors with directed switching topologies

The leader-following formation problem is discussed for a team of quadrotors under directed switching topologies. To obtain a more general dynamic model, we describe the quadrotor system in a non-affine pure-feedback form with mismatched unknown nonlinearities. By employing an adaptive neural networks state observer to approximate the unknown nonlinear functions and to reconstruct the immeasurable inner states, we propose a novel distributed output feedback formation control protocol with the backstepping method combining with the dynamic surface control technique. From the Lyapunov stability theorem, all signals in the closed-loop formation system are proven to be cooperatively semiglobally uniformly ultimately bounded for any given bounded initial conditions. Finally, we proved that we verify the performance of the proposed formation control approach by a simulation study.     

孤网模式下水轮机调节系统参数敏感性分析

鉴于参数敏感性分析是水轮机调节系统稳定性研究的重要内容,以XLD电站中某机组的孤网运行模式为例,基于轨迹灵敏度指标,在机组负载扰动和参数摄动条件下研究了系统主要变量对参数变化的敏感程度。结果表明,孤网模式下不同参数变化对不同变量的影响不同,系统变量对发电机参数的敏感性要大于对随动系统参数的敏感性。