On Development of Fuzzy Controller: The Case of Gaussian and Triangular Membership Functions

In recent years, the use of Fuzzy set theory has been popularised for handling overlap domains in control engineering but this has mostly been within the context of triangular membership functions. In actual practice however, such domains are hardly triangular and in fact for most engineering applications the membership functions are usually Gaussian and sometimes cosine. In an earlier paper, we derived explicit Fourier series expressions for systematic and dynamic computation of grade of membership in the overlap and non-overlap regions of triangular Fuzzy sets. In another paper, we extended the methodology to cover cases of cosine, exponential and Gaussian Fuzzy sets by presenting explicit Fourier series representation for encoding fuzziness in the overlap and non-overlap domains of Fuzzy sets. This current paper presents the development of a “Fuzzy Controller” device, which incorporates the formal mathematical representation for computing grade of membership of Gaussian and triangular Fuzzy sets. It is shown that triangular approximation of Gaussian membership function in Fuzzy control can lead to wrong linguistic classification which may have adverse effects on operational and control decisions. The development of the Fuzzy controller demonstrates that the proposed technique can indeed be incorporated in engineering systems for dynamic and systematic computation of grade of membership in the overlap and non-overlap regions of Fuzzy sets;and thus provides a basis for the design of embedded Fuzzy controller for mission critical applications.

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.

Statistic PID Tracking Control for Non-Gaussian Stochastic Systems Based on T-S Fuzzy Model

A new robust proportional-integral-derivative （PID） tracking control framework is considered for stochastic systems with non-Gaussian variable based on B-spline neural network approximation and T-S fuzzy model identification. The tracked object is the statistical information of a given target probability density function （PDF）, rather than a deterministic signal. Following B-spline approximation to the integrated performance function, the concerned problem is transferred into the tracking of given weights. Different from the previous related works, the time delay T-S fuzzy models with the exogenous disturbances are applied to identify the nonlinear weighting dynamics. Meanwhile, the generalized PID controller structure and the improved convex linear matrix inequalities （LMI） algorithms are proposed to fulfil the tracking problem. Furthermore, in order to enhance the robust performance, the peak-to-peak measure index is applied to optimize the tracking performance. Simulations are given to demonstrate the efficiency of the proposed approach.

基于最优控制理论的国产光抽运小铯钟频率控制算法

原子钟频率控制是时间保持工作中的关键技术.当前守时工作中的频率控制主要针对国外微波钟采用开环控制算法,但由于国产光抽运小铯钟(下称国产钟)的工作原理和性能不同于国外同类型原子钟,因此该算法不能很好适应国产钟.为了提升我国标准时间的自主性和安全性,本文基于国产钟的噪声特性,在最优控制理论的框架下研究了线性二次高斯控制算法,该算法属于闭环控制算法,从同步时间、频率控制准确度和频率控制稳定度方面研究国产钟性能,最后分析了不同控制间隔对国产钟性能的影响.结果表明随着二次损失函数中约束矩阵W_(R)的增大,同步时间延长,控制准确度降低,控制短期稳定度提高.W_(R)相同情况下,随着控制间隔的增大,同步时间延长,控制准确度降低,控制短期稳定度提高,对于W_(R)=1时,控制间隔为1 h的同步时间为5小时,控制准确度为1.83 ns,1 h的Allan偏差为1.81×10^(-13);控制间隔为8 h的同步时间为28 h,控制准确度为4.48 ns,1 h的Allan偏差为1.48×10^(-13).控制国产光抽运小铯钟的中长期稳定度都得到提高.

A machine learning approach to quality-control Argo temperature data

本文提出了一种基于机器学习的Argo浮标温度异常值检测方法.该方法采用机器学习无监督算法高斯混合模型对Argo浮标数据进行聚类分析,并构建包围所有数据点的最小多边形的凸包.基于射线投影算法实现点在多边形内分析,通过自动识别数据点位于凸包内外来判断该数据点数据质量的好坏.本文采用南海区域Argo浮标数据对该方法进行测试,结果表明该方法可以识别70%以上的包含异常值的温度剖面,同时自动标记出各异常值点.

基于遗传-模式搜索算法的微尺度管控区域大气污染物PM2.5溯源

针对微尺度管控区域可能发生的大气污染提出有效的靶向诊断方法-结合高斯烟羽模型和遗传-模式搜索算法的大气污染物分布式溯源方法.将污染源反算模型得到的污染物理论质量浓度与传感器网络观测值的数据对应关系作为目标函数,使用模式搜索算法嵌入遗传算法加快反算模型的搜索过程,反算得到污染源强度和位置.依托杭州市亚运板球场馆大气感知器网络进行实验验证,监测2021年10月PM2.5质量浓度、气象数据,对所提出的混合式大气污染溯源方法进行实验验证.实验结果表明:改进遗传-模式搜索算法对于多维变量的搜索效果较好,能快速精准地反算污染源的位置和强度,可以为微尺度管控区域突发性气体污染防治提供应急决策参考.

基于高斯过程回归的永磁同步电机非线性磁链辨识

在新能源汽车发展领域,以碳化硅为代表的新一代半导体功率器件正在逐步取代硅基IGBT,崭新的技术生态对电机控制性能也有了更高的要求。从传统的PI控制、直接转矩控制到模型预测控制、神经网络控制等新算法,电机参数的精准度逐渐成为电控系统进一步提升性能的关键因素。针对永磁同步电机经典线性模型受交叉饱和等非线性因素影响不能适用于复杂多变工况的问题,提出基于高斯过程回归的非线性磁链辨识方法,使用二阶广义积分器获取动态工况中的磁链数据完成系统辨识,通过仿真与实验验证了该方案的有效性及参数辨识的准确性。

基于高斯展开法的碟形声学黑洞宽频调谐减振机理研究

近年来,嵌入式声学黑洞(acoustic black holes,ABH)以其优异的性能,在结构减振降噪、声波调控、能量回收等领域展示了广阔的应用前景,但其局部结构强度弱化会影响其工程实用性。提出一种碟形声学黑洞(dish-shaped acoustic black hole,DABH)结构,将其附加在主体结构上,以实现对主体结构的宽频减振。在Rayleigh-Ritz法框架下,选择高斯函数作为基函数,根据声学黑洞板的形状确定基函数的分布,避免质量和刚度矩阵的奇异化,建立了其耦合系统半解析模型。通过与有限元模态分析结果的对比,验证了半解析建模方法的正确性。研究了碟形声学黑洞结构参数以及连接位置对主体结构振动响应特性的影响规律,分析了碟形声学黑洞的ABH效应以及与主体结构的耦合效应,揭示了其宽频调谐减振的机理,为拓展声学黑洞在宽频结构振动控制上的应用提供了新的思路。

基于模型预测控制算法的精确着舰控制方法

针对舰载机着舰过程中的航母运动、舰尾流扰动等影响,提出一种基于模型预测控制-线性二次型高斯(MPC-LQG)算法的精确着舰控制方法,开展飞机纵向通道的精确着舰控制研究。对飞机纵向动力学、舰尾流、航母运动、轨迹跟踪等,进行建模和分析。对MPC-LQG算法进行融合,以实现轨迹跟踪控制,核心思想是:模型预测控制进行航母运动补偿;设计全维状态观测,实现全状态反馈,以实现最优着舰控制。对不同的着舰情况和初始条件进行算法仿真,并与其他算法进行仿真比较。仿真结果表明,所提方法的轨迹跟踪效果很好,高度偏差0.1~0.2 m;相比传统着舰控制,所提方法动态响应快、着舰精度高。

基于空变数据的相控贝叶斯-序贯高斯模拟方法

传统的地质统计学估计和模拟方法在整合测井和地震数据时,笼统地采用静态空间的数据,而未考虑样点与待模拟点空间沉积的变化以及岩相变化对样点取值影响的问题,尤其是储层空间非均质性强、井分布不均匀时。基于贝叶斯序贯高斯算法,针对碎屑岩储层,研究了一种基于空间变化数据的相控模拟方法。首先,定义一个随待模拟点位置变化而动态变化的空间三维球体模型,将空间数据的选择从静态的一维柱体拓展到该球体。然后,在考虑物源方向的情况下,采用变差函数工具对三维球体模型内的数据进行筛选。同时,利用沉积相进行约束,进一步提高样点取值的精确性。最后,采用反距离加权算法确定样点的权重。川西坳陷东坡实际工区资料的应用表明,基于空变数据的相控贝叶斯序贯高斯模拟方法,平面上河道模拟展布形态与地震属性一致性较好,剖面上河道清晰连续,分辨率也得到提升,盲井模拟结果与测井数据吻合率达到92%,验证了方法的准确性。对比不同硬件配置的应用结果表明,该方法适应于不同性能的硬件条件,验证了方法的实用性。

基于个体记忆和高斯扰动的自适应灰狼算法

针对传统灰狼算法在求解复杂优化问题时易陷入局部最优和收敛速度慢等问题,提出一种基于个体记忆和高斯扰动的自适应灰狼算法(NGWO)。首先,引入一个非线性控制参数,以平衡算法中全局探索与局部开发能力。其次,为了加快算法的收敛速度和提高收敛精度,提出了一种拥有个体记忆的自适应位置更新公式。最后,通过结合贪心算法与自适应高斯扰动方法,增强了本算法跳出局部最优的能力。通过基准函数的测试,并与其他优化算法和改进算法进行对比,结果表明NGWO具有更精确的解和更高的收敛速度。

车辆自适应巡航下的MPC方法的研究

提出了一种新颖的车辆自适应巡航控制(ACC)系统,该系统可以在保障跟车距离的同时,提升车辆的燃油经济性。基于模型预测控制(MPC)进行ACC的上层控制器搭建,并在此基础上采用高斯过程回归(GPR),网格搜索(GS)和自适应方法进行三轨参数调整,将控制域和预测域调整到最优状态。此外,为了减少计算量并提高稳定性,系统采用了粒子群优化算法(PSO)对系统进行升级改进。仿真结果表明,基于MPC控制的车辆自适应巡航控制系统可以在保证良好跟踪性能的同时降低燃油消耗率。

高速公路平面坐标系建立问题的探讨

【目的】解决高速公路平面坐标系的建立问题,提高平面坐标系的测设精度,有效确保工程建设质量。【方法】根据高斯正形投影误差计算公式和误差限差理论,分析高斯正形投影误差的组成及各部分的大小,利用平移量计算高速公路平面坐标系的中央子午线和投影高,将标准坐标系下控制点坐标转化为新坐标系下坐标。【结果】对新坐标进行精度验算,两点间通过坐标反算获得的平距与实测平距的差值处于允许范围之内,建立了满足高速公路工程精度需要的新坐标系。【结论】实践表明:高速公路平面坐标系可根据计算所得的中央子午线和投影高来建立具有抵偿高程面的任意带高斯正形投影平面直角坐标系,且新建平面坐标系下各控制点的变形量都能满足规范精度与设计要求。

基于深度强化学习的含智能软开关配电网电压控制方法

大量分布式新能源接入给配电网运行带来了电压越限和网损增加等一系列问题。提出了一种基于多智能体强化学习的无模型电压控制策略,通过协调光伏逆变器、分布式储能和智能软开关以降低网损、消除电压越限。针对传统电压控制策略对配电网精确的模型参数依赖性强的问题,提出了基于高斯过程回归的潮流替代模型,通过多智能体与潮流替代模型交互实现无模型的离线训练和在线应用。同时提出了一种基于随机加权三重Q学习的多智能体深度强化学习算法,能够进一步降低柔性演员-评论家算法的高低估误差,提升算法探索能力和收敛结果。最后在IEEE33节点系统上的仿真结果,验证了所提方法在解决配电网分布式电压优化控制问题上的有效性。

声学黑洞约束阻尼板半解析耦合建模与振动特性研究

声学黑洞(ABH)以其优异的性能在结构减振降噪、声波调控、能量回收等领域展示了极其广阔的应用前景。但声学黑洞边缘截断会导致非零反射系数的存在,从而弱化声学黑洞效应。为此,本文在声学黑洞结构中引入约束阻尼材料,在Rayleigh-Ritz法框架下,选择高斯函数作为基函数,根据声学黑洞板的形状确定高斯基函数的分布,避免质量矩阵和刚度矩阵的奇异化,建立了声学黑洞约束阻尼板的半解析分析模型。通过与有限元分析结果对比,验证了半解析建模方法的正确性。研究了约束阻尼结构参数对声学黑洞板弯曲振动特性的影响规律,揭示了约束阻尼的减振机理和能量耗散作用。实验进一步验证了声学黑洞约束阻尼板的减振效果。

A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

In this article,an approach for economic performance assessment of model predictive control(MPC) system is presented.The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG) benchmark other than conventional minimum variance control(MVC) to estimate the potential of reduction in variance.The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance,and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction.Combining the LQG benchmark directly with benefit potential of MPC control system,both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization problem.The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.

Stiffness-damping matching method of an ECAS system based on LQG control

A novel method of matching stiffness and continuous variable damping of an ECAS(electronically controlled air suspension) based on LQG(linear quadratic Gaussian) control was proposed to simultaneously improve the road-friendliness and ride comfort of a two-axle school bus.Taking account of the suspension nonlinearities and target-height-dependent variation in suspension characteristics,a stiffness model of the ECAS mounted on the drive axle of the bus was developed based on thermodynamics and the key parameters were obtained through field tests.By determining the proper range of the target height for the ECAS of the fully-loaded bus based on the design requirements of vehicle body bounce frequency,the control algorithm of the target suspension height(i.e.,stiffness) was derived according to driving speed and road roughness.Taking account of the nonlinearities of a continuous variable semi-active damper,the damping force was obtained through the subtraction of the air spring force from the optimum integrated suspension force,which was calculated based on LQG control.Finally,a GA(genetic algorithm)-based matching method between stepped variable damping and stiffness was employed as a benchmark to evaluate the effectiveness of the LQG-based matching method.Simulation results indicate that compared with the GA-based matching method,both dynamic tire force and vehicle body vertical acceleration responses are markedly reduced around the vehicle body bounce frequency employing the LQG-based matching method,with peak values of the dynamic tire force PSD(power spectral density) decreased by 73.6%,60.8% and 71.9% in the three cases,and corresponding reduction are 71.3%,59.4% and 68.2% for the vehicle body vertical acceleration.A strong robustness to variation of driving speed and road roughness is also observed for the LQG-based matching method.

基于改进相对熵的控制系统性能评价方法

为提高控制系统随机性能评价的准确性,克服传统相对熵指标非对称、无界的问题,提出一种基于改进相对熵指标(IREI)的控制系统性能评价方法。该方法首先将原始相对熵指标改进为对称有界的测度指标,即IREI;然后采用核密度估计法计算过程数据的概率密度函数,通过引入蒙特卡罗模拟以逼近非高斯分布情况下的指标值。最后,利用MATLAB/Simulink建立包含非高斯噪声扰动及调节阀劣化的仿真模型,设计一致性检验与随机扰动检验实验,将IREI与确定性指标及最小方差指标进行对比。对比结果表明,该方法可以灵活有效地评价控制器性能。

Noise-Induced Transition in a Voltage-Controlled Oscillator Neuron Model

In the presence of Gaussian white noise,we study the properties of voltage-controlled oscillator neuronmodel and discuss the effects of the additive and multiplicative noise.It is found that the additive noise can accelerate andcounterwork the firing of neuron,which depends on the value of central frequency of neuron itself,while multiplicativenoise can induce the continuous change or mutation of membrane potential.

Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge.In this paper we proposed a method based on improved bacterial foraging optimization（IBFO）,which simulates the foraging behavior of “E.coli” bacterium,to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system（C-ITSKFS） rule base.To remove the defect of the low rate of convergence and prematurity,three modifications were produced to the standard bacterial foraging optimization（BFO）.As for the low accuracy of finding out all optimal solutions with multi-method functions,the IBFO was performed.In order to demonstrate the performance of the proposed IBFO,multiple comparisons were made among the BFO,particle swarm optimization（PSO）,and IBFO by MATLAB simulation.The simulation results show that the IBFO has a superior performance.