基于自适应观测器的永磁同步电机无速度传感器矢量控制系统仿真研究

本文在模型参考自适应控制理论的基础上，介绍了一种基于波波夫超稳定性理论的模型参考自适应永磁同步电机转速观测器，通过MATLAB语言中的Sinmlink,Power System Block模块建立了控制系统的仿真模型，对选用的转速观测器的动态性能，稳定性等问题进行了仿真研究。     

MRAC在位置随动控制系统中的应用

针对位置随动控制系统具有不确定性的特点,设计了具有良好跟随性的自适应控制系统。利用机理法建立数控机床刀具进给定位控制系统的数学模型,将该系统等效为带有参变量的单输入单输出系统,在该模型的基础上设计了基于李雅普诺夫稳定性理论的模型参考自适应控制系统。利用Matlab进行仿真,其仿真结果表明,基于李雅普诺夫稳定性理论的模型参考自适应控制系统具有良好的跟随性、稳定性和控制精度。     

燃料电池车离散MRAC电机控制系统的DSP软件设计

自适应控制理论在燃料电池车电机控制系统中的应用,对于提高电动汽车的驱动性能具有较好的效果。在此给出采用离散模型参考自适应控制的燃料电池车电机控制系统,控制系统设计以数字信号处理器为核心,简单介绍了系统的硬件设计,并在此基础上重点探讨DSP控制系统的软件设计,分析了主程序、脉宽调制中断处理程序、电流PI调节程序和速度自适应调节程序的软件实现,给出了主要程序流程图。探讨在电机DSP控制系统中,离散模型参考自适应算法的实现对于各种先进的控制策略在电动汽车中的应用具有重要意义。     

基于MRAS的异步电机无速度传感器的矢量控制

由于电机定转子参数的变化,利用一般的转子磁链对转速进行估算,将导致不能得到准确的结果。这里采用积分型转子磁链的参考和可调模型构建出一个基于MRAS的异步电机无速度传感器的矢量控制模型。该模型提高了矢量控制系统的动态性能并利用MATLAB,sIMULINK进行了异步电机无速度传感器矢量控制系统的仿真,验证了文中所采用的模型参考自适应的速度估算方法的可行性以及对参数误差的鲁棒性。     

亚轨道飞行器控制系统快速原型设计

为了实现对飞行器控制系统的快速、廉价、高效等设计需求,提出了一种基于快速控制原型技术的亚轨道飞行器控制系统设计方案,并完成系统的软硬件设计与实时仿真。该系统由总体控制计算机、两台实时数值仿真机、VMIC、硬件接口以及物理设备等组成,建立了亚轨道飞行器再入阶段六自由度运动模型,设计控制分配并采用经典控制方法进行姿态控制,通过Rt Fly平台进行分布式快速原型仿真。仿真结果表明,控制参数调节方便,系统实时性良好,快速控制原型技术可以有效应用于飞行器控制系统设计。     

两轴柔性支承快速反射镜结构控制一体化设计

为了预测并确认结构设计阶段快速反射镜系统的频率特性和时域性能,对一种两轴柔性支承快速反射镜进行了结构控制一体化研究和测试。提出了一种新型柔性支承结构,根据激光系统光束传输要求的快速反射镜指标,设计了快速反射镜系统的主要结构参数;研究了系统的动态数学模型,确定了闭环系统的控制方式和控制参数;建立了系统摆动部分的刚柔耦合模型,获得了结构非线性模型,基于非线性模型对控制系统和运动系统进行了联合仿真测试。联合仿真结果显示,系统在运动方向的谐振频率为54 Hz,与有限元和理论计算结果的误差均为3.8%,系统位置闭环带宽为203 Hz,符合设计要求。时域输出结果显示,系统的超调量为3.5%,调节时间为10 ms,与理论计算结果的偏差分别为3%和5 ms。     

认知雷达转向控制系统优化设计研究

神经网络是一种模仿动物神经网络行为特征，进行分布式并行信息处理的算法数学模型。为了能更好地实现对认知雷达转向系统的精确控制，文中将神经网络的概念引入到认知雷达转向控制系统中，提出一种基于神经网络的认知雷达转向控制系统。首先介绍了神经网络的模型算法，然后基于此，对雷达转向系统进行跟踪仿真，进而得到预计的转向控制系统的输入量。最后，结合模型预测控制算法，对转向系统的角度和速度进行精确控制。为了验证理论分析结果，基于MATLAB搭建了认知雷达转向控制系统的仿真模型并进行仿真验证，结果表明，所提出的方法能够对转向系统的角度和速度进行精确控制，转矩脉动减小了56%,超调量减小了61%。     

自适应动态寻优控制系统仿真研究

对采用自适应动态寻优方法的极值调节控制系统进行仿真研究,其目的是为解决在工业生产过程中若采用传统的动态寻优方法就必须准确地辨识极值调节控制对象线性部分的参数这一无法回避的难题.对参数飘移的极值调节控制系统进行仿真研究,仿真结果表明,采用自适应动态寻优方法对极值调节控制对象线性部分模型的先验知识要求很少,只需要知道极值调节控制对象的线性部分的阶数就足够了,在动态寻优的过程中不仅能够自动辨识控制对象的参数,而且还能够自动适应参数的飘移.从而可得出如下结论:在实际工业生过程中,若采用自适应动态寻优方法,根本不需要辨识极值调节控制对象线性部分的参数,而且还能够自动适应参数的飘移.从而能有效地保证控制系统运行的连续性与稳定性.因此采用自适应动态寻优方法的极值调节控制系统将会在实际工业生产过程中发挥其强大的控制功能.     

基于模糊控制的自适应幅相补偿控制系统

针对数字阵列在使用过程中存在的性能退化问题,利用模糊控制相关理论建立了自适应幅相补偿控制器模型,并设计了新型数字阵列自适应幅相补偿控制系统。该控制系统能够智能、实时地对数字阵列进行监测,并根据阵面状态对各通道幅相进行动态调整,实现数字阵列性能的优化。该系统在未来数字相控阵雷达设计中具有很大的工程意义。     

基于神经网络的模型参考自适应控制

将神经网络应用于模型参考自适应控制系统中,组成模型参考神经网络自适应控制系统.利用神经网络自身的优点来克服传统模型参考自适应控制算法的不足,使系统具有更强的鲁棒性和容错性.仿真结果表明,该系统与传统的模型参考自适应控制系统比较,不仅能实现原系统的功能,而且该系统的稳定性和抗干扰性优于原系统.     

Integrated design of speed-sensorless and adaptive speed controller for a brushless DC motor

The study develops a design of an integrated new speed-sensorless approach that involves a torque observer and an adaptive speed controller for a brushless dc motor (BLDCM). The system is based on the vector control drive strategy. The speed-sensorless approach first employs a load observer to estimate the disturbed load torque, and then the estimated load torque is substituted into the mechanical dynamic equation to determine the rotor speed, and thus develop a speed-sensorless algorithm. Additionally, the mechanical rotor inertia constant and the friction coefficient, which are the inputs of the load observer, are estimated using the recursive least-square rule. Therefore, the proposed speed-sensorless approach is unaffected by the time-variant motor parameters nor is affected by the integrator drift problem. It also has a simpler computing algorithm than the extended Kalman filter for estimating the speed. The modified model reference adaptive system algorithm, an adaptive control algorithm, is adopted as a speed controller of the BLDCM to improve the performance of the speed-sensorless approach. Simulation and experimental results confirm that the performance of the design of a new integrated speed-sensorless approach and the adaptive speed controller is good.     

Model reference adaptive predictive control for a variable-frequency oil-cooling machine

This paper develops methodologies and techniques for the design, analysis, and implementation of a model reference adaptive predictive temperature controller for a variable-frequency oil-cooling machine, suited for cooling high-speed machine tools. The oil-cooling process is modeled experimentally as a first-order system model with a time delay and its system parameters are identified using the recursive least-square method. Based on this model, a model reference adaptive predictive controller is proposed for achieving set-point tracking and robustness. A real-time model reference adaptive predictive control algorithm is then presented and implemented utilizing a stand-alone digital signal processor TMS320F243 from Texas Instruments Incorporated. The experimental results show that the proposed control method is proven capable of giving satisfactory performance under set-point changes, fixed loads, and load changes.     

变流量节能系统动态监测及实时调节系统设计分析

为提高变流量节能系统自适应性能和实时调节能力,提出基于双启发式动态规划的变流量节能系统动态监测及实时调节系统设计方法,构建变流量节能动态监测及实时调节控制对象模型,以供水流量及一次侧的供水温度、供水流量为初始约束参量设计系统的控制规则。在此基础上,结合双启发式动态规划模式并采用模糊优化分配控制策略,对系统进行调节和控制,通过线性规划学习方法,实现变流量节能动态监测及实时调节。仿真结果表明,采用该方法进行变流量节能系统动态监测及实时调节的自适应性能较好,各时段流量预测误差曲线波动介于-1.5至1.5之间,且热负荷值与预测热负荷之间相对误差最大值为2.5%,误差较低。     

基于跟踪微分器的模型参考自适应控制

为了处理二阶系统模型参数的大范围不确定性,提出了基于跟踪微分器的模型参考自适应控制,利用两个非线性跟踪微分器分别得到系统输出的微分信号和误差的微分信号,同时抑制了高频噪声放大效应。根据被控对象的数学模型,自适应调节律能自动实时调节控制律中的参数。实验结果表明,当雷达伺服系统被控对象模型的参数在较大范围内变化时,该新型控制器有效补偿了二阶系统参数的不确定性,提高了伺服系统稳态和动态跟踪精度,保证了系统的全局渐近稳定。     

New robust adaptive control algorithm for high-performance ACdrives

This paper presents a new robust structure for a model reference adaptive control (MRAC) controller for field-oriented-controlled (FOC) drives which requires no prior knowledge of the drive parameters and is guaranteed to provide global asymptotic stability of the closed-loop system. This structure simplifies the design and implementation of the adaptive controller requiring less effort to synthesis than a standard MRAC system. Discussion on theoretical aspects, such as selection of a reference model, stability analysis proof, gain adaptive process, steady-state error elimination, and robustness to unmodeled dynamics are included. The paper describes many practical aspects of the implementation, such as adaptive gain analysis, adaptive rate selection, the gain variation limits, gain windup prevention measure, and initial values. The new robust adaptive controller has been successfully implemented on an FOC drive and experiment results for dynamic tracking, sudden loading and unloading, and gains adaptation under different operation conditions are presented to support the robustness of the proposed controller     

Precise angular speed control of permanent magnet DC motors in presence of high modeling uncertainties via sliding mode observer-based model reference adaptive algorithm

The major concentration of this study is on developing a control scheme with parameter- and load-insensitive features capable of precise angular speed regulation of a permanent magnet (PM) DC motor in the presence of modeling uncertainties. Towards this objective, first, an appropriate nonlinear dynamic model of friction, the modified LuGre model, is opted and incorporated into the mathematical model of a PM DC motor. Then a sliding mode observer (SMO) is designed to estimate the state variable of the friction model. Next, a model reference adaptive control system into which estimated values of the friction state and parameters are fed is designed to track the desired speed trajectory while alleviating the adverse effects of model uncertainties and friction. Stability of the proposed SMO-based MRAC system is discussed via the Lyapunov stability theorem, and its asymptotic stability is verified. In addition to simulation studies, the algorithm is implemented on a new variable structure test-bed which gives us the ability to simulate desired parameter variations and external disturbance changes in experiment. The main contribution of the proposed scheme is the bounded estimation of the system’s friction parameter. While similar control solutions do estimate these parameters, there is no guarantee that they will estimate the correct value of friction parameters. However, in the proposed method, by properly choosing the design parameters, if certain criteria is satisfied, the estimated friction parameters will be in the bounded vicinity of their actual values. The obtained results show the effectiveness of the proposed tracking algorithm and its robustness against load and system parameters’ variations.     

A robust adaptive controller for PM motor drives in roboticapplications

The paper deals with theoretical development and practical implementation of an adaptive speed and position regulator suitable for robotic applications. The proposed adaptive control scheme is characterized by a reduced amount of computation and is based on the model reference adaptive control approach to compensate the variations of the system parameters, such as inertia and torque constant. A disturbance torque observer is employed to balance the required load torque and reduce the complexity of the adaptive algorithm. Simulation tests of a robotic drive, including an interior type permanent magnet synchronous (IPMS) motor, are reported in order to compare the proposed control scheme with standard speed and position regulators. Experimental results, obtained from a prototype based on a commercial PC board, are also reported in order to practically evaluate the feasibility and the features of the proposed adaptive control scheme     

Adaptive gain scheduling with feedforward control system based on system model for PMSM

A feedforward controller for permanent magnet synchronous motor (PMSM) has been proposed in this study, and proportional and integral gain could be self-adaptive under different operating conditions. The control structure used in the feedforward system is the same as in the feedback control system. This control structure could guarantee independence of the speed command input to output with the disturbance input to output, which makes the system have better reference trajectory tracking and disturbances rejection. In order to obtain optimal control performance when the parameters are uncertain, a gain scheduling adaptive controller is used in the feedforward system. The proposed controller has been verified by the experimental and simulation results with less steady-state error and better dynamic response than the controllers without it under the condition of external load torque disturbance and PMSM parameter uncertainties.     

校园计算网格作业自适应调度的研究和实现

由于网格资源具有分布、异构、动态、自治等特性。使得网格作业调度成为网格计算中具有挑战性的任务之一。针对校园高性能计算资源负载不均衡、管理模式不统一的问题，提出了校园计算网格自适应调度系统的解决方案。详细研究了网格作业的白适应调度技术，设计了含有负载均衡能力的资源动态信息收集模型，并给出自适应调度算法。实现了校园范围内高性能计算资源的负载均衡和统一的资源管理。     

Adaptive backstepping control and friction compensation for AC servo with inertia and load uncertainties

An adaptive backstepping control with friction compensation scheme is presented. A third-order linear dynamic model is used for the AC motor control system design while the LuGre dynamic friction model with nonuniform friction force variations characterizes the friction force. Nonlinear adaptive control laws are designed to compensate the unknown system parameters and disturbances. System robustness and asymptotic position tracking performance are shown through simulation and experimental results.