基于扩展滑膜控制器的PMSM无速度传感器研究

本文设计了一种基于永磁同步电机（PMSM）直接转矩控制（DTC）的扩展滑膜观测器,通过测量定子电压与电流,获得电机的转速和转子位置。MATLAB仿真结果表明,扩展的滑膜观测器在空间矢量电压直接转矩控制（SVPWM-DTC）中,所得转速更接近实际转速。     

基于扰动观测器的机载云台扰动复合补偿方法

针对多旋翼无人机(mUAV)机载云台的扰动补 偿需求,提出一种基于改进的扰动观测器(IDO,improved disturbance observer)的伺服控 制器与减振装 置相结合的复合补偿方法。深入分析了IDO的扰动补偿能力和鲁棒性,构建 了基于IDO的云 台伺服控制结构;依据mUAV机载云台的振动特性,设计出减振装置的结构参数 及安装位置。飞行实验表明, 引入复合补偿方法后,视轴(LOS)指向误差减小到了0.03°,振动隔 离度提高了约15dB。复合补偿方法有效地补偿了机载云 台的扰动,完全满足了mUAV机载云台扰动补偿的需求。     

Sliding Mode Observer Driver IC Integrated Gate Driver for Sensorless Speed Control of Wide Power Range of PMSMs

This work proposes a highly efficient sensorless motor driver chip for various permanent‐magnet synchronous motors (PMSMs) in a wide power range. The motor driver chip is composed of two important parts. The digital part is a sensorless controller consisting mainly of an angle estimation block and a speed control block. The analog part consists of a gate driver,which is able to sense the phase current of a motor. The sensorless algorithms adapted in this paper include a sliding mode observer (SMO) method that has high robust characteristics regarding parameter variations of PMSMs. Fabricated SMO chips detect back electromotive force signals. Furthermore, motor current–sensing blocks are included with a 10‐bit successive approximation analog‐to‐digital converter and various gain current amplifiers for proper sensorless operations. Through a fabricated SMO chip, we were able to demonstrate rated powers of 32 W, 200 W, and 1,500 W.     

振动与力加载耦合电液系统的力加载跟踪控制

针对振动与力加载耦合电液系统因强耦合产生的干扰力问题,分析耦合系统的组成及工作原理,建立振动与力加载耦合电液系统的动力学模型;在此基础上,分析耦合电液系统干扰力的产生机制,提出带干扰观测器的前馈速度补偿控制策略;搭建振动与力加载耦合电液系统试验台,对提出的控制策略进行试验验证。试验结果证明提出的带干扰观测器的前馈速度补偿控制策略能够有效抑制系统干扰力,提高力加载跟踪精度。     

基于直接升力的空中加油对接飞行控制

为解决传统空中加油对接过程中受油机纵向轨迹跟踪控制存在时间滞后的问题,提出一种基于直接升力的空中加油对接飞行控制方法.采用非线性L1制导的方法生成横纵向加速度指令,设计2种直接升力方案对受油机纵向轨迹控制加以改进,采用动态逆方法对姿态回路设计,通过扩张状态观测器对动态逆内回路进行补偿.仿真结果表明:采用的2种方案均能消除纵向轨迹跟踪的时间滞后,实现受油机纵向轨迹的快速响应,完成空中加油的成功对接.     

基于凸组合算法优化的前向神经网络观测器设计

本文利用凸组合算法对单隐藏层前向神经网络进行了优化,通过迭代来更新权值以调整隐藏层的信息.同时引入了一个新的误差函数来评价误差性能,该函数通过对权值进行解耦来求解优化参数,提高了参数的计算速度.在此基础上,提出了一种非线性系统的自适应神经网络状态观测器设计方法.最后通过仿真验证了该神经网络观测器能准确并快速地观测出系统的状态值.     

Output feedback model predictive control of hydraulic systems with disturbances compensation

Enhancing the robustness of output feedback control has always been an important issue in hydraulic servo systems. In this paper, an output feedback model predictive controller (MPC) with the integration of an extended state observer (ESO) is proposed for hydraulic systems. The ESO was designed to estimate not only the unmeasured system states but also the disturbances, which will be synthesized into the design of the output prediction equation. Based on the mechanism of receding horizon and repeating optimization of MPC, the output prediction equation will be updated in real time and the future behavior of the system will be accurately predicted since the disturbances are compensated effectively. Hence, the ability of the traditional MPC to suppress disturbances will be improved evidently. The experiment results show that the proposed controller has high-performance nature and strong robustness against various model uncertainties, which verifies the effectiveness of the proposed control strategy.     

基于滑模的二阶多智能体快速抗扰一致性

针对带有外部未知扰动的二阶多智能体系统的领导—跟随有限时间一致性问题，本文设计出一种带有时变增益的有限时间干扰观测器，用以实现对每个跟随智能体中未知扰动的快速估计，在此基础上，本文结合超螺旋积分滑模控制方法并利用邻居智能体的位置和速度信息设计一种快速抗扰一致性协议，该协议能够保证存在非线性动态的多智能体系统有限时间一致性控制并能抑制抖振现象.同时，利用李亚普诺夫函数进行了稳定性的证明.最后，通过Matlab数值仿真进一步验证了所提出协议的可行性.     

Neural network–based direct adaptive robust control of unknown MIMO nonlinear systems using state observer

This paper focuses on the problem of adaptive robust tracking control for a class of uncertain multiple-input and multiple-output (MIMO) nonlinear system. Unlike most previous research studies, model dynamics, disturbances, and state variables are unknown in this paper. A novel observer-based direct adaptive neuro-sliding mode control approach is proposed of which the only required knowledge is the system output. By incorporating the Adaptive Linear Neuron (ADALINE) neural network (NN) into the conventional sliding mode observer, the proposed observer has favorable performance. In the controller, a radial basis function (RBF) NN is constructed to approximate the unknown equivalent control laws and the estimation of the sliding surface is applied as the input. A gain-adaptation sliding mode term is designed to enhance the robustness of the control system. Besides, the free parameters of the ADALINE NN and the RBFNN are updated online by adaptive laws to obtain optimal approximation performance. Finally, the comparative simulations are given to show the effectiveness and merits of proposed scheme.     

A globally exponentially stable speed observer for a class of mechanical systems: experimental and simulation comparison with high-gain and sliding mode designs

It is shown in the paper that the problem of speed observation for mechanical systems that are partially linearisable via coordinate changes admits a very simple and robust (exponentially stable) solution with a Luenberger-like observer. This result should be contrasted with the very complicated observers based on immersion and invariance reported in the literature. A second contribution of the paper is to compare, via realistic simulations and highly detailed experiments, the performance of the proposed observer with well-known high-gain and sliding mode observers. In particular, to show that – due to their high sensitivity to noise, that is unavoidable in mechanical systems applications – the performance of the two latter designs is well below par.