Open-plus-closed-loop control for chaotic motion of 3D rigid pendulum

An open-plus-closed-loop （OPCL） control problem for the chaotic motion of a 3D rigid pendulum subjected to a constant gravitationM force is studied. The 3D rigid pendulum is assumed to be consist of a rigid body supported by a fixed and frictionless pivot with three rotational degrees. In order to avoid the singular phenomenon of Euler＇s angular velocity equation, the quaternion kinematic equation is used to describe the motion of the 3D rigid pendulum. An OPCL controller for chaotic motion of a 3D rigid pendulum at equilibrium position is designed. This OPCL controller contains two parts： the open-loop part to construct an ideal trajectory and the closed-loop part to stabilize the 3D rigid pendulum. Simulation results show that the controller is effective and efficient.

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/.

Signal averaging for noise reduction in anesthesia monitoring and control with communication channels

This paper investigates impact of noise and signal averaging on patient control in anesthesia applications, especially in networked control system settings such as wireless connected systems, sensor networks, local area networks, or tele-medicine over a wide area network. Such systems involve communication channels which introduce noises due to quantization, channel noises, and have limited communication bandwidth resources. Usually signal averaging can be used effectively in reducing noise effects when remote monitoring and diagnosis are involved. However, when feedback is intended, we show that signal averaging will lose its utility substantially. To explain this phenomenon, we analyze stability margins under signal averaging and derive some optimal strategies for selecting window sizes. A typical case of anesthe-sia depth control problems is used in this development.

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.

Energy-Efficient Optimal Guaranteed Cost Intermittent-Switch Control of a Direct Expansion Air Conditioning System

To improve the energy efficiency of a direct expansion air conditioning(DX A/C) system while guaranteeing occupancy comfort, a hierarchical controller for a DX A/C system with uncertain parameters is proposed. The control strategy consists of an open loop optimization controller and a closed-loop guaranteed cost periodically intermittent-switch controller(GCPISC). The error dynamics system of the closed-loop control is modelled based on the GCPISC principle. The difference,compared to the previous DX A/C system control methods, is that the controller designed in this paper performs control at discrete times. For the ease of designing the controller, a series of matrix inequalities are derived to be the sufficient conditions of the lower-layer closed-loop GCPISC controller. In this way, the DX A/C system output is derived to follow the optimal references obtained through the upper-layer open loop controller in exponential time, and the energy efficiency of the system is improved. Moreover, a static optimization problem is addressed for obtaining an optimal GCPISC law to ensure a minimum upper bound on the DX A/C system performance considering energy efficiency and output tracking error. The advantages of the designed hierarchical controller for a DX A/C system with uncertain parameters are demonstrated through some simulation results.

Controlling Chaotic Cscillations with Input-Output Linearization

In this paper, the nonlinear control of chaotic oscillations was investigated by using the input output linearization control method. The chaotic attitude of a kind of spacecraft was discussed. It is demonstrated that the input output linearization control law is the nonlinear version of the parametric open plus closed loop control law.

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。

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CarSim的电动汽车转向灵活性仿真与探究

传统的电动助力转向系统由于是由一套齿轮齿条转向器拉动车轮实现转向的,这就限制了车轮转角使其无法实现真正的阿克曼转向,也限制了车轮的最大转向角度。在CarSim自带模型的基础上,利用Simulink修改其转向子系统,从而建立了一种可实现前轮独立转向的汽车模型。通过双移线仿真试验和方向盘角阶跃响应试验,验证了模型的正确性。通过开环控制和闭环控制试验,证明了模型相对于传统汽车模型有着更好的转向灵活性。研究可为后期的线控转向汽车的灵活性和操稳性研究做出铺垫,对于延长轮胎寿命,提升驾驶感受也具有重要的意义。

考虑干扰的交通子区模糊迭代学习边界控制

针对固定增益迭代学习控制器跟踪交通子区内期望累计车辆数n^(*)的速度较慢,迭代次数过多的问题,并考虑到实际交通子区内存在的干扰对其运行状态的影响,建立了含有干扰项的交通流模型并对其进行离散化。采用开闭环比例微分(proportion differentiation,PD)型模糊迭代学习控制器,根据研究子区实际路网内的累计车辆数与期望累计车辆数n^(*)的误差及其变化率对开闭环PD型迭代学习控制器的迭代学习增益和反馈增益进行调整,使交通子区内的实际累计车辆数可以较快速地跟踪期望累计车辆数n^(*),并对开闭环PD型模糊迭代学习控制器的收敛性进行严格证明。通过对轻度和中度干扰影响下的交通子区进行仿真,结果显示,对比固定增益迭代学习控制器,开闭环PD型模糊迭代学习控制器的误差收敛速度分别提高24%和31%,平均排队长度分别减少24.52%和16.55%,平均延误分别减少21.63%和19.23%,平均停车次数分别减少23.68%和10.77%。

开放教育质量管理的闭环控制研究

开放教育质量管理的水平和效果,关系到质量保证体系的构建和体系办学质量的提升。研究针对教学检查、听评课教学督导、网上教学督查、满意度调查、质量年报等常态化管理工作中存在的闭环不足问题,将闭环控制理念和PDCA循环理论应用于开放教育质量管理中,形成相应的闭环管理工作流程,并通过优化质量标准、加强质量管理队伍建设、完善闭环管理工作机制、打造质量监控平台等路径,最终形成按照计划-实施-检查-反馈-改进这一循环方式运行的质量闭环管理模式,从而实现体系办学质量螺旋式上升的目标。

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。

船用柴油机SCR系统控制策略试验研究

为降低船用柴油机NO_(x)排放,分析柴油机选择性催化还原(selective catalytic reduction,SCR)后处理系统的开环、闭环控制策略,对不同策略及工况下NO_(x)转化率及NH3泄露进行试验对比。结果表明:采用开环策略,柴油机功率与额定功率的比为75%以上时,NO_(x)转化效率可达到83%,但不同工况下NO_(x)转化率波动超过20%;采用闭环控制策略,不同工况下NO_(x)转化率稳定在85%左右,尾气中NH_(3)的体积分数低于20×10^(-6);闭环控制策略下NO_(x)转化率具有自调整、高稳定性、响应速度快等优势,可降低NO_(x)排放且尾气中NH3的体积分数在规定范围内。

海上换流站的控制方法研究

柔性直流输电工程所含元器件较多,控制方式比较复杂,在运行中易发生高频震荡等问题。为了防止高频震荡导致风电解列和系统崩溃,本文分析讨论了海上柔直工程采用的开、闭环转换策略和开、闭环转换的时机。实践表明,采用阻尼振荡高频谐振抑制策略使得系统在开、闭环转换过程中不会发生高频谐振,增强了系统的稳定性。

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。