Observer-based Adaptive Optimal Control for Unknown Singularly Perturbed Nonlinear Systems With Input Constraints

This paper introduces an observer-based adaptive optimal control method for unknown singularly perturbed nonlinear systems with input constraints. First, a multi-Time scales dynamic neural network MTSDNN observer with a novel updating law derived from a properly designed Lyapunov function is proposed to estimate the system states. Then, an adaptive learning rule driven by the critic NN weight error is presented for the critic NN, which is used to approximate the optimal cost function. Finally, the optimal control action is calculated by online solving the Hamilton-Jacobi-Bellman HJB equation associated with the MTSDNN observer and critic NN. The stability of the overall closed-loop system consisting of the MTSDNN observer, the critic NN and the optimal control action is proved. The proposed observer-based optimal control approach has an essential advantage that the system dynamics are not needed for implementation, and only the measured input U+002F output data is needed. Moreover, the proposed optimal control design takes the input constraints into consideration and thus can overcome the restriction of actuator saturation. Simulation results are presented to confirm the validity of the investigated approach. © 2014 Chinese Association of Automation.

Optimization of block-floating-point realizations for digital controllers with finite-word-length considerations

The closed-loop stability issue of finite-precision realizations was investigated for digital control-lers implemented in block-floating-point format. The controller coefficient perturbation was analyzed resultingfrom using finite word length (FWL) block-floating-point representation scheme. A block-floating-point FWL closed-loop stability measure was derived which considers both the dynamic range and precision. To facilitate the design of optimal finite-precision controller realizations, a computationally tractable block-floating-point FWL closed-loop stability measure was then introduced and the method of computing the value of this measure for a given controller realization was developed. The optimal controller realization is defined as the solution that maximizes the corresponding measure, and a numerical optimization approach was adopted to solve the resulting optimal realization problem. A numerical example was used to illustrate the design procedure and to compare the optimal controller realization with the initial realization.

A Video Game Based on Optimal Control and Elementary Statistics

The video game presented in this paper is a prey-predator game where two preys (human players) must avoid three predators (automated players) and must reach a location in the game field (the computer screen) called preys’ home. The game is a sequence of matches and the human players (preys) must cooperate in order to achieve the best perform- ance against their opponents (predators). The goal of the predators is to capture the preys, which are the predators try to have a “rendez vous” with the preys, using a small amount of the “resources” available to them. The score of the game is assigned following a set of rules to the prey team, not to the individual prey. In some situations the rules imply that to achieve the best score it is convenient for the prey team to sacrifice one of his components. The video game pursues two main purposes. The first one is to show how the closed loop solution of an optimal control problem and elementary sta- tistics can be used to generate (game) actors whose movements satisfy the laws of classical mechanics and whose be- haviour simulates a simple form of intelligence. The second one is “educational”, in fact the human players in order to be successful in the game must understand the restrictions to their movements posed by the laws of classical mechanics and must cooperate between themselves. The video game has been developed having in mind as players for children aged between five and thirteen years. These children playing the video game acquire an intuitive understanding of the basic laws of classical mechanics (Newton’s dynamical principle) and enjoy cooperating with their teammate. The video game has been experimented on a sample of a few dozen children. The children aged between five and eight years find the game amusing and after playing a few matches develop an intuitive understanding of the laws of classical me- chanics. They are able to cooperate in making fruitful decisions based on the positions of the preys (themselves), of the predators (their opponents) and on the physical limitations to the movements of the game actors. The interest in the game decreases when the age of the players increases. The game is too simple to interest a teenager. The game engine consists in the solution of an assignment problem, in the closed loop solution of an optimal control problem and in the adaptive choice of some parameters. At the beginning of each match, and when necessary during a match, an assign- ment problem is solved, that is the game engine chooses how to assign to the predators the preys to chase. The resulting assignment implies some cooperation among the predators and defines the optimal control problem used to compute the strategies of the predators during the match that follows. These strategies are determined as the closed loop solution of the optimal control problem considered and can be thought as a (first) form of artificial intelligence (AI) of the preda- tors. In the optimal control problem the preys and the predators are represented as point masses moving according to Newton’s dynamical principle under the action of friction forces and of active forces. The equations of motion of these point masses are the constraints of the control problem and are expressed through differential equations. The formula- tion of the decision process through optimal control and Newton’s dynamical principle allows us to develop a game where the effectiveness and the goals of the automated players can be changed during the game in an intuitive way sim- ply modifying the values of some parameters (i.e. mass, friction coefficient, ...). In a sequence of game matches the predators (automated players) have “personalities” that try to simulate human behaviour. The predator personalities are determined making an elementary statistical analysis of the points scored by the preys in the game matches played and consist in the adaptive choice of the value of a parameter (the mass) that appears in the differential equations that define the movements of the predators. The values taken by this parameter determine the behaviour of the predators and their effectiveness in chasing the preys. The predators personalities are a (second) form of AI based on elementary statistics that goes beyond the intelligence used to chase the preys in a match. In a sequence of matches the predators using this second form of AI adapt their behaviour to the preys’ behaviour. The video game can be downloaded from the website: http://www.ceri.uniroma1.it/ceri/zirilli/w10/.

闭式泵控液压-机械直线执行器的离散PID控制

液压驱动因其功率密度高、直线输出力大,广泛应用于装备制造领域。但非对称液压缸受两腔面积差的制约,在阀控系统中需要用非对称阀控制,在闭式泵控系统中需要增加额外的补油回路。而电动缸具有对称的输出力和较高的能效,解决了非对称液压缸面积差的影响,但功率密度低使电动缸难以满足低转速大扭矩的高负载工况。针对上述问题,提出了液压回转与机械直线转换相结合的新型驱动方式,采用闭式泵控马达驱动滚珠丝杠带动活塞杆动作。由于在闭环控制中单一的PID难以满足全负载工况,系统采用局部最小化优化方法自动寻找最优PID参数,生成离散PID图表并根据负载自动选择对应的PID参数控制以减小负载干扰。结果表明:离散PID自动调定的系统能避免负载变化产生较大干扰,在变负载和目标位移情况下,离散PID参数自动调整的响应曲线阶跃下降和上升的稳态误差最大分别为4.5 mm、3.0 mm。

基于时域Neal-Smith准则的人机控制系统设计多目标优化研究

为了获得驾驶员和飞机良好的匹配特性,使用基于人机闭环参考模型的方法将飞行品质的要求引入优化算法,进行飞行控制系统设计。首先使用CAP准则建立飞机等效系统模型。然后,基于时域Neal-Smith准则,通过多目标优化算法一次性得到在不同任务条件下的最优驾驶员模型。根据需要选择相应的驾驶员模型,并与飞机等效系统模型组成人机闭环参考模型,进行飞行控制设计多目标优化。仿真结果显示,文中的算法不仅可以整定控制器参数,根据任务需求选取最优驾驶员模型,还可以利用优化结果进行PIO易感性的分析。

MP-MMC驱动六相永磁同步风力发电机分数阶PID控制研究

针对多相模块化多电平变流器(MP-MMC)驱动多相发电机,在Y移30度六相永磁同步发电机(PMSG)旋转坐标系下的数学模型基础上,引入了分数阶PID控制器。分数阶PID控制器相比整数阶PID控制器增加了两个控制自由度,即主要影响控制系统稳态性能的积分阶次λ和主要影响控制系统动态性能的微分阶次μ,使得分数阶PID控制器的设计更加灵活。在MMC内部能量均分部分采用分数阶PID控制器可使得子模块电容电压峰峰值及其谐波畸变率降低,进而输出电压波形得到改善;也使得MMC的桥臂环流峰峰值降低,环流波形得到改善。在发电机转速侧,发电机定子的d、q轴电流闭环控制侧部分采用分数阶PID控制器可使发电机转速动态性能与稳态性能进一步改善。在z1和z2子空间的谐波分量控制部分采用分数阶PID控制器可使得系统谐波影响更小,控制效果更佳。最后,仿真和实验结果验证了分数阶PID控制器具有良好的控制性能,可进一步提升了六相PMSG-MMC系统的运行能力。

基于hp-RPM的滑翔弹道优化及制导仿真

基于hp自适应Raudu伪谱法研究了滑翔弹的带落角约束的弹道优化以及在线制导问题。hp-RPM方法将连续时间内的轨迹优化问题离散成非线性规划问题求解得到最短滑翔时间的带落角约束的方案弹道。针对滑翔弹在实际飞行过程中由于模型误差和外界干扰而引起偏差,提出了一种基于hp-RPM方法的闭环在线实时制导方法,进行了仿真实验,结果表明,该方法可以有效降低滑翔弹再入飞行过程中的随机误差。

Research on closed-loop simulation system for digital AC servo system

An intelligent and efficient closed-loop simulation system without any hardware is proposed for AC servo system.According to the characteristics of AC servo system and its control processor,the code executing platform combined with the reverse Polish notation algorithm is developed.To gain the accurate simulation results,the discrete motor model and IPM model are built to deal with the dead-time.Based on the simulation system,parameter identification can be implemented and control parameters can be optimized by using the exhaust algorithm.The parameters obtained by the simulation system were successfully applied to an experimental system,and the favorable control performance was achieved.

接触器低速大吸力合闸优化控制策略

为了更好地抑制接触器合闸触头弹跳,提出一种控制策略以优化合闸过程。首先,利用线圈电压与电流作为输入构建参数观测器,实时观测接触器的动态吸力、弹簧反力、动铁心速度及位移。在此基础上,构造双闭环控制结构,外环采用模糊控制逐次调节超程前吸力裕量,内环将吸力裕量与实时弹簧反力叠加用作参考吸力,进行吸力闭环控制,实现外环动铁心合闸速度的逐次迭代优化。将观测位移与触头开距进行实时比较,一旦动铁心运动到超程阶段时,增大吸力参考,使电磁吸力快速增加,实现触头的低速大吸力合闸,以压制动铁心反弹,尽量抑制触头弹跳,并兼顾可靠合闸。通过仿真与实验验证了方案的可行性,该控制策略对依靠电磁力进行操动的电磁及永磁开关的合闸优化具有一定意义。

应用闭环算法车辆转向机构最优控制系统分析

四轮车辆转向时,前后车轮的转向中心为一点时,视为车辆的理论最优转向,也是最优控制的目标。针对常用的多连杆转向系统,根据结构特点和转角关系,对系统控制进行分析;基于闭环控制算法,依据目标路径与参数反馈获取的转向控制量,对转向系统的最优控制进行设计分析;同时,对系统的状态变量和预测参数进行分析;基于实际车辆和转向最优控制系统,分析车辆在空载工况,以30km/h转向,分析稳态回转工况和双移线工况车辆运行的各参数变化,并与理论最优值进行对比,以验证控制系统的有效性。结果可知:采用离散分析和半步长积分对控制系统的状态变量进行预测,获取的经过精度较高;稳态回转工况,随着车辆的提升,转向轨迹的半径逐渐增大,车辆表现出明显的不足转向特征;两种工况下,转向控制系统获得的特性曲线与理论值具有高度的一致性,二者的峰值误差控制在5%以内,表明最优控制算法的可靠性,为此类设计优化提供参考。

基于SABO算法的PMSM弱磁和MTPA控制方法

永磁同步电机因其结构紧凑、噪声较少、功耗较少、运行速度快、操作稳定,已被普遍采用。针对永磁同步电机弱磁控制过程中,转速环参数选取采用传统PI(proportional-integral)控制方法,依靠经验整定参数,外界抗干扰能力较差、难以保证在各运行区间具有优良性能等问题,提出了一种基于减法平均优化算法的永磁同步电机的弱磁和MTPA(maximum torque per ampere)控制的宽运行范围方法。将智能寻优算法、MTPA控制、弱磁控制三者相结合,利用减法平均优化算法优化PI控制器的参数,提高了系统的响应性能和抗干扰能力;工作电压未超过电压极限圆使用MTPA控制策略运行;工作电压超过电压极限圆利用电压闭环反馈,进行弱磁控制。使用MATLAB/Simulink构建的永磁同步电机弱磁控制仿真模拟,通过PI控制器和减法平均优化算法优化后的PI控制器性能对比,从仿真结果得到控制器方法的有效性。实验有效证明了该控制方法能够解决各种运行工况下控制器参数的优化整定问题,提高电机控制精度。

Research on production optimal control techniques for smart oilfield

Production optimal control technologies have become important tools for efficiently developing oil and gas reservoirs in recent years.This paper presents an overview of the research and application of these technologies in smart oilfield,including reservoir data matching and prediction,well production optimization,and automatic well monitoring and control technologies.With the support of the National Natural Science Foundation of China,we made years of effort and finally derived a novel data—driven reservoir data matching and prediction methods.Besides,the new automatic optimization technologies and flow monitoring and control devices were also presented.The proposed technologies helped improve the computational efficiency by hundreds of times compared to traditional technologies.The real-time optimization and control of the injection and production parameters was realized using the proposed technologies,which have been widely applied in actual reservoirs at home and abroad,achieving significant economic benefits.

基于三相电压源型PWM整流器的优化控制策略

对三相电压源型PWM整流器的数学模型进行分析,并提出一种基于实际电压误差值、使用PI控制器的无净差跟踪的电流解耦控制方案。该方案经仿真实验后,具备较好的性能,能够有效解决三相电压源型PWM整流器在实际工作中电感参数等问题的优化控制。

基于多模态异构算法组态工具的流程行业智能化解决方案

本文首先分析了流程行业应用场景从自动化向智能化转型升级所需要的条件,并从数据模态、模型特征、异构实现、工程化以及人员能力方面总结了工业智能应用面临的问题和挑战;其次,针对性地提出了多模态数据融合、机理模型数据驱动及专家异构整合、算法技能,符合用户习惯的工程化组态方式、不同人员协作方式等方案,并进一步给出了针对复杂工业场景的工具方案;最后,对智能优化控制、设备异常检测、多模态数据闭环控制等场景进行了分析,并总结展望了多模态异构算法组态工具在流程行业向智能化转型升级中的作用。

主动四轮转向汽车最优控制及闭环操纵性仿真

鉴于汽车正常情况下都运行在侧向加速度较小的线性工作区域,对基于线控技术的主动四轮转向汽车进行了前、后轮转角最优跟随控制器的设计和算法推导,建立了"人-车-路"闭环操纵系统模型,并进行闭环系统仿真和安全性评价。结果表明:基于最优控制的主动四轮转向汽车同时实现了减小车身质心侧偏角与跟踪期望横摆角速度的控制目标,改善了车辆高速行驶下的转向响应特性;相对于传统前轮转向汽车与比例控制四轮转向汽车,基于最优控制的主动四轮转向汽车具有更好的路径跟随精度和主动安全性。

基于免疫遗传算法的模糊优化控制及其仿真

提出了一种记忆细胞精英抗体遗传策略,能强化免疫系统的记忆功能和抗体浓度调节机制,从而使免疫遗传算法更快速、稳定地收敛到全局最优点.在模糊规则和隶属度函数联合编码基础上,通过上述改进型免疫遗传算法实现模糊控制器参数的同步优化.另外,由于模糊规则库和隶属度函数通常都具有对称的特点,控制器优化时可将搜索空间减半,从而使稳定性、收敛性得到显著提高.将这种新型模糊优化控制方法应用于过热汽温控制系统进行仿真,结果表明可以获得非常满意的闭环控制效果.

基于灰狼优化算法的茶园拖拉机转角控制器

比例积分微分(proportional⁃integral⁃derivative,PID)控制算法被广泛用于茶园拖拉机的转角控制系统,但是PID控制器带来大量的参数整定以及滞后性,必然会降低控制精度和控制效率.为了解决这一问题,提出了基于灰狼优化算法(grey wolf optimization algorithm,GWOA)的茶园拖拉机转角控制器.首先,建立了简化的茶园拖拉机电动助力转向(electric power steering,EPS)系统的数学模型;其次,采用了基于PID的电动机电流-转向盘转角双闭环的控制策略;接着,设计了灰狼优化算法来对传统PID控制器的参数进行优化,构建了基于灰狼优化算法的PID转角控制器(GWOA⁃PID);最终,利用CARSIM和MATLAB实时模拟拖拉机运行情况,对提出的基于灰狼优化算法的茶园拖拉机转角控制器进行验证,结果表明:传统PID控制器的电流总谐波畸变(total har⁃monic distortion,THD)为11.46%,基于粒子群算法(particle swarm optimization,PSO)的PID控制器的THD为8.12%,所提出的GWOA⁃PID控制器的THD为6.28%,使得电流谐波在原来的基础上降低了45.2%.

四轮转向的控制方法的发展

从控制原理出发 ,系统地评述了车辆四轮转向系统控制方法的发展 ,以此为基础指出四轮转向系统的研究必须以闭环综合评价为出发点 ,并与其它主动安全技术相结合 。

基于PID控制和遗传算法的半导体激光器温控系统

为了实现半导体激光器温度的精确控制,设计了将PID控制与遗传算法相结合的高精度温度控制系统.该系统使用热敏电阻与光电二极管进行测量和反馈半导体激光器的温度值,并用热电制冷器作为温控执行器,构成了双闭环控制系统,提出了利用遗传算法在线优化PID参数的方法,根据实时测量偏差、控制器输出和上升时间构建函数作为待优化的性能指标,从而动态调整控制器的参数.结果表明,系统能够实现高精度的温度稳定控制,稳定度达到0.027%,温度偏差为0.002℃,控制效果优于传统的PID控制,具有较好的应用价值.

硅微谐振加速度计的温度特性

硅微谐振加速度计以其小体积、低成本和高精度的频率信号输出,成为硅微惯性传感器研制的热点之一。温度特性是影响硅微谐振加速度计精度水平的重要因素。在分析硅微谐振加速度计工作机理的基础上,从结构设计方法、工艺流程加工和闭环控制回路方面分析了温度对零位和标度因数的影响因素,同时给出了相应解决措施。研制的硅微谐振加速度计基频约为17 kHz,标度因数约220 Hz/g,在-40～+70℃范围内,谐振频率的温度系数为-71.5×10-6/℃,标度因数的温度系数为-610×10-6/℃,样机在常温下谐振频率的相对稳定性为0.313×10-6,1.5 h零偏稳定性达到42.5μg。