Gradient control and synchronization of Julia sets

This paper firstly introduces the control methods to fractals and give the definition of synchronization of Julia sets between two different systems. Especially, the gradient control method is taken on the classic Julia sets of complex quadratic polynomial Zn＋1 = zn^2＋ c, which realizes its Julia sets control and synchronization. The simulations illustrate the effectiveness of the method.

Pressure Gradient Control Strategies Based on Disturbance Rejection for Typical Pharmaceutical Cleanrooms

Keeping pressure gradient is an excellent approach to prevent the reveal of airflow direction and cross infection in manufacturing circumstances of pharmaceutical cleanrooms, thus how to keep cleanroom’s pressure is critical. In the paper, we study a positive pressure pharmaceutical cleanroom system which is composed by a cleanroom and an airlock. We divide the system’s disturbances into step disturbance, ramp disturbance and sine wave disturbance. We design its pressure gradient control strategies, including CAV control, PID control and active-disturbance-rejection-control. We build the system’s model and make simulations based on Matlab/Simulink software platform. Results show that active-disturbance-rejection-control algorithm has good capabilities for shorter responding time and lower overshot of the pressure gradient. The results reveal that active-disturbance-rejection-control method has good control performances in responding time, accuracy and disturbance rejection.

Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ～ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ～(O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits.

CONTROLLABILITY FOR A PARABOLIC EQUATION WITH A NONLINEAR TERM INVOLVING THE STATE AND THE GRADIENT

In this article, we consider the controllability of a quasi-linear heat equation involving gradient terms with Dirichlet boundary conditions in a bounded domain of RN.The results are established by using the variational methods, the related duality theory and Kakutani Fixed-point Theorem.

Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise

In this paper,a reinforced gradient-type iterative learning control pro file is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by external Gaussian white noise.The robustness is analyzed and the range of the step is speci fied by means of statistical technique and matrix theory.Compared with the conventional one,the proposed algorithm is more ef ficient to resist external noise.Numerical simulations of an injection molding process illustrate that the proposed scheme is feasible and effective.

Investigation on gradient material fabrication with electron beam melting based on scanning track control

A new electron beam control system was developed in a general vacuum electron beam machine by assembling with industrial control computer, programmable logic control （PLC）, deflection coil, data acquisition card, power amplifier, etc. In this control system, scanning track and energy distribution of electron beam could be edited off-line, real-time adjusted and controlled on-line. Ti-Mo gradient material （GM） with high temperature resistant was fabricated using the technology of electron beam melting. The melting processes include three steps, such as preheating, melting, and homogenizing. The results show that the GM prepared by melting technology has fine appearance, and it has good integrated interface with the Ti alloy. Mo and Ti elements are gradually distributed in the inter.face of the gradient material. The microstructure close to the Ti alloy base metal is α ＋ β basket-waver grain, and the microstructure close to the GM is a single phase of β solid solution.

Application of Conjugate Gradient Approach for Nonlinear Optimal Control Problem with Model-Reality Differences

In this paper, an efficient computational algorithm is proposed to solve the nonlinear optimal control problem. In our approach, the linear quadratic optimal control model, which is adding the adjusted parameters into the model used, is employed. The aim of applying this model is to take into account the differences between the real plant and the model used during the calculation procedure. In doing so, an expanded optimal control problem is introduced such that system optimization and parameter estimation are mutually interactive. Accordingly, the optimality conditions are derived after the Hamiltonian function is defined. Specifically, the modified model-based optimal control problem is resulted. Here, the conjugate gradient approach is used to solve the modified model-based optimal control problem, where the optimal solution of the model used is calculated repeatedly, in turn, to update the adjusted parameters on each iteration step. When the convergence is achieved, the iterative solution approaches to the correct solution of the original optimal control problem, in spite of model-reality differences. For illustration, an economic growth problem is solved by using the algorithm proposed. The results obtained demonstrate the efficiency of the algorithm proposed. In conclusion, the applicability of the algorithm proposed is highly recommended.

Direct Adaptive Control Based on Gradient Estimation

基于坡度，评价在这篇文章被建议的直接适应的控制的一个控制方法。动态系统在一个线性模型集合被嵌入。基于动态系统的嵌入的性质，一个适应最佳的控制算法被建议。建议控制算法的柔韧的集中被证明了，有建议方法的静态的控制错误也被分析。到工业聚丙烯进程的建议方法的应用程序结果验证了它的可行性和有效性。

Modified Fletcher-Reeves and Dai-Yuan Conjugate Gradient Methods for Solving Optimal Control Problem of Monodomain Model

In this paper, we present the numerical solution for the optimal control problem of monodomain modelwith Rogers-modified FitzHugh-Nagumo ion kinetic. The monodomain model, which is a well-known mathematical model for simulation of cardiac electrical activity, appears as the constraint in our problem. Our control objective is to dampen the excitation wavefront of the transmembrane potential in the observation domain using optimal applied current. Various conjugate gradient methods have been applied by researchers for solving this type of optimal control problem. For the present paper, we adopt the modified Fletcher-Reeves method and modified Dai-Yuan methodfor computing the optimal applied current. Numerical results show that the excitation wavefront is successfully dampened out by the optimal applied current when the modified Fletcher-Reeves method is used. However, this is not the case when the modified Dai-Yuan method is employed. Numerical results indicate that the modified Dai-Yuan method failed to converge to the optimal solution when the Armijo line search is used.

融合分类校正与样本扩增的小样本目标检测

现有小样本目标检测方法在扩增样本时往往存在数据分布偏移问题,同时分类任务性能容易受定位任务影响。针对上述问题,提出一种新的小样本目标检测算法。该算法在Faster R-CNN框架基础上引入分类校正模块(CCB)、样本扩增模块(SAB)和梯度限制层(GCL)改善性能。CCB使用离线的强分类网络对检测器最终结果进行校正;SAB在特征域利用基类样本信息修正新类样本分布,从而在修正的分布中进行采样完成新类样本扩增;在梯度反向传播中通过GCL限制主干网络接收的基类和新类信息。在PASCAL VOC和COCO数据集上的实验结果表明,相较于目前已知的最新算法结果,提出的小样本目标检测算法在样本数量很小的情况下提升了检测效果,在公共数据集PASCAL VOC上最高提升可以达到5.1%,更难的数据集COCO上最高提升可达到1.9%,同时拥有很好的鲁棒性和泛化能力。

基于TD3-PER的氢燃料电池混合动力汽车能量管理策略研究

为优化氢燃料电池混合动力汽车的燃料经济性及辅助动力电池性能,提出了一种基于优先经验采样的双延迟深度确定性策略梯度(TD3-PER)能量管理策略。采用双延迟深度确定性策略梯度(TD3)算法,在防止训练过优估计的同时实现了更精准的连续控制;同时结合优先经验采样(PER)算法,在获得更好优化性能的基础上加速了策略的训练。仿真结果表明:相较于深度确定性策略梯度(DDPG)算法,所提出的TD3-PER能量管理策略的百公里氢耗量降低了7.56%,平均功率波动降低了6.49%。

循环梯度加载下煤岩损伤破坏特性及能量演化规律研究

煤矿开采中岩体受开挖卸荷因素的影响常处于反复的循环加载过程,为研究循环梯度加载作用下煤岩的损伤破坏特性和能量演化规律,对煤样进行单轴循环梯度加载实验,对比分析煤岩在不同循环梯度加载下的变形破坏特性、输入能量密度、耗散能密度和损伤变量的规律。结果表明:随着循环梯度应力的递增,煤岩试样所承受的循环次数递减、峰值强度降低,分别为17.97、14.86、11.23、10.53 MPa;在循环梯度加载中,输入能量密度绝大部分以弹性应变能的形式储存于岩样内部,耗散能在单次循环输入总能量的0.2%~40%之间递增。选用累积耗散作为煤岩损伤破坏全过程的量化指标,并通过Logistic方程对煤岩损伤进行了拟合,得到了煤岩损伤的演化模型。

计及采样扰动抑制的电压源逆变器三矢量无模型预测电流控制方法

针对传统电压源逆变器无模型预测电流控制(model-free predictive current control,MFPCC)方法存在电流纹波大、电流梯度更新停滞以及预测性能易受采样扰动影响的问题。该文提出一种计及采样扰动的三矢量MFPCC方法。在一个控制周期应用3个基本矢量,并根据价值函数计算矢量作用时间,降低了输出电流纹波;其次,通过建立不同矢量作用下的电流梯度方程组,实现电流梯度数据的实时更新,消除了停滞现象;再次,分析采样扰动对MFPCC的影响,采用扩张状态观测器估计采样扰动以补偿预测电流控制,抑制其对输出电流的影响。最后,通过仿真和实验,对所提方法的有效性进行了验证。

基于模态分解及GRU-XGBoost短期电力负荷预测

精确的短期电力负荷预测能有效提高电力系统运营水平。针对电力负荷数据受多种因素影响,波动性和随机性强等问题,提出了一种基于模态分解及混合模型的负荷预测方法。首先,采用主成分分析法(principal component analysis,PCA)对负荷特征向量进行处理,去掉冗余信息,再用完全自适应噪声集合经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)将历史负荷分解为简化的几个子序列;其次,选择引入样本熵(sample entropy,SE)来计算子序列熵值,将相近的子序列重构得到随机、细节、低频和趋势分量后选用不同结构门控循环单元(gate recurrent unit,GRU)对不同分量类型进行预测,再使用极致梯度提升模型(extreme gradient boosting,XGBoost)对各分量残差进行拟合,各重组序列的预测值为GRU预测值与XBGoost拟合值之和,重组各序列得到最终预测值。选取3年时电力负荷数据进行实验,结果表明,所提模型的均方根误差(root mean square error,RMSE)、平均绝对百分比误差(mean absolutepercentage error,MAPE)和平均绝对误差(mean absolute error,MAE)分别为370.676 MW、99.07%和246.89 MW,与单一模型和混合模型相比,实现了评价指标的明显减少。

考虑建模误差的某型火箭末级制导方法研究

运载火箭末级发动机点火后,在制导系统的导引下,火箭最终抵达入轨位置、取得入轨速度,卫星进入运行轨道。火箭末级飞行动力学模型为非线性微分方程形式,难以利用解析方法得到火箭姿态变化特性,进而控制火箭飞行过程中的状态变量。将火箭入轨约束条件转化为最优控制的性能指标函数,利用庞德里亚金极小值原理与牛顿梯度法相结合的方式来解决动力学方程求解问题,得到末级飞行标准轨道;火箭动力学建模过程中,由于参数难以精确计量等的影响,存在建模误差,导致火箭实际飞行轨道会偏离标准轨道。对模型进行线性化处理,引入状态反馈,可以使实际飞行轨道接近于标准轨道,提高卫星荷载入轨要求精度。仿真结果表明:该制导算法可较好地减小火箭载荷入轨误差。

考虑智能网联车辆影响的八车道高速公路施工区可变限速控制方法

为提升车联网环境下高速公路施工区交通运行效率及安全水平,提出了一种基于强化学习的可变限速控制方法.选取智能驾驶模型和真车试验模型,分别对传统人工车辆和智能网联车辆的跟驰行为进行建模,构建了以瓶颈下游路段交通流量为效率指标、瓶颈路段速度标准差为安全指标的复合奖励值,利用深度确定性策略梯度算法,分车道动态求解最佳限速值.仿真结果表明,所提可变限速控制方法在不同智能网联车辆渗漏率条件下均能有效提升交通流运行效率和安全水平,且在智能网联车辆渗漏率较低时,提升效果更加显著.当智能网联车辆渗漏率为1.0时,瓶颈下游路段交通流量提升10.1%,瓶颈路段速度标准差均值下降68.9%;当智能网联车辆渗漏率为0时,瓶颈下游路段交通流量提升20.7%,瓶颈路段速度标准差均值下降78.1%.智能网联车辆的引入能够提升至多52.0%的瓶颈下游路段交通流量.

基于双深度输入凸神经网络多模型的中间点过热度预测控制

新能源大量并网,超临界火电机组参与调峰容易造成中间点过热度较大波动,从而导致过热蒸汽超温等问题。为较好控制中间点过热度达到稳定,提出了一种基于双深度输入凸神经网络多模型(muti-DDICNN model)的中间点过热度预测方法,分别训练了不同预测步长下子模型,构建了中间点过热度状态预测网络(SPNN)和误差预测网络(EPNN)。利用此预测网络凸性质,设计了一种基于双深度输入凸神经网络多模型预测控制器(DDICNN-MPC),将控制问题转化为凸优化问题,求取控制矩阵对目标函数的雅可比矩阵,采用梯度下降法计算控制矩阵最优解。仿真结果表明,DDICNN-MPC能快速平稳地跟踪中间点过热度设定值,且稳态误差较小,具有较好的调节能力。

电力系统两阶段紧急切负荷控制智能预决策

电力系统仿真分析是安全稳定控制领域重要技术,可以用于制定与校验紧急控制措施。传统的人工分析仿真数据以决策紧急控制措施的工作模式严重依赖专家经验,在应用于复杂大电网时显得耗时耗力。该文提出一种两阶段紧急切负荷控制智能预决策方法,第一阶段决策切负荷点,第二阶段决策切负荷量。首先基于仿真数据,区分3种电压失稳模式:纯电压失稳、耦合电压失稳和混合电压失稳,分别采用不同的负荷筛选方法;然后基于轻量级梯度提升机算法,根据仿真数据直接预估系统恢复稳定所需的切负荷总量,按负荷排序进行分配。结合暂稳仿真校验控制措施的有效性,调整决策量。以我国东北电网为例进行仿真研究,验证了在大电网紧急控制措施制定时,所提两阶段智能决策方法相比完全迭代试凑方法在有效性、快速性和准确性方面的优势。

基于GBDT的望奎县农田土壤有机碳主控因子研究

利用多源公开数据,针对于2012年搜集到的农田土壤有机碳(SOC)数据,通过梯度增强决策树(GBDT)探究SOC的主控因子,揭示SOC空间分异机理.选择国家商品粮食生产基地县和全国产量大县-望奎县作为研究区域,结果表明,GBDT模型相较于一元线性回归模型在所有的自变量上均具有更好的预测效果,总体的R^(2)为0.958,表明该模型能够很好地解释目标变量的变异;碱解氮、有效磷、气温、土壤微生物多样性、速效钾、土壤pH和降水量依次是影响有机碳含量最为显著的几个因素,其中碱解氮以33.85%的影响权重位居最高;7个环境协变量均观察到对SOC的阈值效应,且均具有两个阈值.研究发现任意两个变量都不是独立的,均存在相互作用的负值效应.表明环境变量对SOC含量的影响是一个复杂的交互作用,而不是简单的叠加.侧面证明了过度施肥并不会达到增加土壤肥力的作用,而会造成资源浪费和农田生态污染问题.

基于DDPG改进PID算法的堆肥翻堆作业反馈控制

在农业废弃物堆肥发酵过程中物料的含水率会发生变化,导致翻堆作业负荷的变化。而现有的翻堆作业主要通过人工操作,导致机器作业过程中作业效率低,容易产生故障。针对人工操作翻堆机作业时出现调控不精准的问题,通过试验构建翻堆机作业负荷与翻堆物料含水率、翻堆机行走速度关系模型,并结合翻堆机变频调速控制模型,利用DDPG(Deep Deterministic Policy Gradient)改进PID算法对翻堆作业调速系统进行优化控制。经过Simulink仿真结果表明,DDPG改进PID算法相比传统PID算法在超调量上减少6.7%,调节时间减少2.5 s,并且抗扰动与跟随性能均更优。翻堆作业现场测试结果表明:DDPG改进PID算法的控制方式相比传统PID算法超调量要降低4%、调节时间减少2 s,相比人工控制其调节时间减少6 s。