带参数辨识的自适应二阶滑模观测器PMSM无传感器矢量控制

针对表贴式永磁同步电机无位置传感器矢量控制系统, 提出一种具有电机参数在线辨识的基于Super-twisting algorithm的自适应二阶滑模观测器.在两相静止坐标系下,将模型参考自适应方法与基于Super-twisting algorithm的二阶滑模方法相结合,实现反电动势的准确估计.采用李亚普诺夫理论证明观测器的稳定性,并由李亚普诺夫稳定性方程推导定子电阻和转子转速的自适应律.在同步旋转坐标系下,采用二阶滑模观测器估计永磁磁链,并将其输入位置跟踪观测器估计转子位置.该算法充分抑制了滑模抖振,同时避免了低通滤波和相位补偿环节的使用,转子位置检测不受定子电阻和永磁磁链变化的影响,具有较强的鲁棒性.仿真结果验证了所提出算法的有效性.     

基于混合滑模控制器和反正切观测器的SPMSM直接转矩控制

针对基于传统PI控制的表贴式永磁同步电机(SPMSM)直接转矩控制系统抖振和相位延迟等问题,在转速环节设计新型趋近律,采用模糊自适应方法,实现趋近律参数的动态调节,并通过Lyapunov方法证明稳定性.利用super-twisting滑模策略生成参考电压矢量,完成混合滑模控制器的设计,建立基于反正切函数的滑模观测器,并对转子位置进行合理补偿.仿真实验表明,与PI控制、基于指数趋近律的滑模控制器相比,所设计的控制器在电机空载起动和外加干扰情况下均能有效提高系统响应,显著降低抖振,与其他模型参考自适应观测器相比,所设计观测器能有效减小相位延迟,转子位置辨识结果更准确.     

高阶终端滑模控制在稳定平台中的应用

针对稳定平台伺服系统中干扰对跟踪精度的影响,提出一种高阶终端滑模控制算法.首先,提出一种基于super-twisting算法的高阶滑模干扰观测器的设计方法,实现对系统中干扰的快速估计与实时补偿.其次,提出一种基于Lyapunov函数的高阶终端滑模控制器,由到达条件推导而得,高阶终端滑模控制将不连续的切换项作用于变量的高...     

考虑输入饱和的近空间飞行器姿态容错控制

针对近空间飞行器姿态控制中出现的执行器故障,输入饱和与外部干扰等问题,设计了一种基于二阶滑模干扰观测器和辅助系统的鲁棒容错跟踪控制方法.首先,将系统不确定,外部扰动和执行器故障作为复合干扰,设计super-twisting二阶滑模干扰观测器对其进行估计.然后为解决输入饱和问题构造了辅助分析系统,并借助backstepping方法,设计姿态容错跟踪控制器.利用Lyapunov方法,严格证明了所有闭环系统信号的收敛性.最后将所设计的控制方法应用于近空间飞行器姿态控制中,仿真结果验证了该控制方法的有效性.     

基于迭代学习观测器的航天器姿态主动容错控制

针对含有外部扰动和执行器故障的一类航天器姿态控制系统,本文提出基于迭代学习观测器的主动容错控制方案.首先,建立了含有外部扰动和执行器故障的航天器姿态控制系统的运动学和动力学模型.其次,为了提高观测器的故障估计精度,在传统迭代学习观测器设计基础上引入上一时刻状态估计误差信息,文章提出一种改进型学习估计算法.进一步,基于滑模控制和指定时间稳定理论,利用学习观测器的故障估计信息设计指定时间主动容错控制器.与现有的航天器主动容错控制方案相比,本文所提出的算法的优势在于可以使故障系统的姿态能在指定时间跟踪上指令信号.基于Lyapunov方法,本文从理论上证明了改进型学习观测器和姿态容错控制系统的稳定性.最后,通过数值仿真,说明了所提容错控制方案的有效性和可行性.     

基于滑模观测器的临近空间飞行器容错控制

针对临近空间飞行器中结构未知的执行器故障,提出一种基于滑模观测器的容错控制方法。采用Edwards-Spurgeon观测器结构设计滑模观测器,实现对执行器故障的鲁棒估计。根据所得的故障信息设计模型参考滑模容错控制器,控制律的非线性增益分为两部分,保证系统的鲁棒容错性能。一部分利用参数不确定的界值条件设计,一部分基于鲁棒故障估计信息设计。基于李雅普诺夫稳定性理论证明了系统的渐近稳定性。仿真结果表明,该方法能有效实现对执行器故障的鲁棒估计,并保证故障条件下飞行器对参考模型的稳定跟踪性能,达到了期望的容错效果。     

基于时变增益扩张状态观测器的逆变器系统自适应super-twisting电压鲁棒控制

针对电压源逆变器系统中负载扰动和参数摄动,本文提出了一种基于时变增益扩张状态观测器的自适应super-twisting鲁棒电压控制新方法.首先考虑负载扰动,建立单相逆变器系统的动态模型,进而考虑系统参数摄动,通过引入非测量辅助状态变量,将上述动态模型转化为只包含匹配扰动的状态方程;其次,设计时变增益扩张状态观测器,以实现对非测量辅助状态变量与包含负载变化和参数摄动等因素在内的集总不确定项的估计;最后,基于此扩张状态观测器,设计采用自适应super-twisting算法的滑模控制律,以实现逆变器系统输出电压对其参考电压的快速准确跟踪并增强系统鲁棒性.仿真实验结果表明:所提出的时变增益扩张状态观测器可在保证观测误差收敛的同时,有效抑制"初始微分峰值"现象;采用自适应super-twisting算法的滑模控制策略可使逆变器系统输出电压具有较高跟踪精度和较小总谐波失真率,增强系统的抗干扰能力,并降低控制输入信号"抖振".     

一种基于滑模—神经网络观测器的故障检测和诊断方法

本文针对一类非线性系统，提出了一种用于故障检测和诊断的滑模观测器方法．其 中，观测器中的滑模项保证了该系统在无故障情况时的鲁棒性，并且系统运行的滑动区域提供了故障检测的条件．当检测出故障之后，观测器中的故障估计部分被启动，利用RBF神经网络估计故障，从而能在线辨识故障的形态．仿真结果验证了该方法的有效性．     

基于模糊滑模控制器和两级滤波观测器的永磁同步电机无位置传感器混合控制

针对传统带有滑模观测器的永磁同步电机控制系统中的转矩脉动大、抖振明显、反电动势估计精度差等问题,在速度环提出了基于双曲正弦函数的新型趋近率,结合模糊控制思想对趋近率参数实现自整定,设计了一种基于新型趋近率的模糊积分滑模速度环控制器;同时,在滑模观测器中提出基于变截止频率低通滤波器和修正反电动势观测器的两级滤波结构来抑制反电动势中的高频分量和纹波分量,并对转子位置进行合理补偿,设计了两级滤波滑模观测器;通过Lyapunov判据对本文提出的控制策略的稳定性进行了推导证明.仿真结果表明,与传统滑模观测器相比,本文控制器可使电机在启动和受到外部扰动时系统响应良好.     

自适应Terminal滑模控制及其在UASV再入中的应用

针对传统模糊干扰观测器在观测误差较小时学习速度缓慢的缺点,提出一种新型快速模糊干扰观测器;然后设计了基于快速模糊干扰观测器的自适应Terminal滑模控制方案,严格证明了跟踪误差和观测误差均在有限时间内收敛到非常小的区域;最后,仿真结果表明了所设计干扰观测器的优越性和闭环控制方案的有效性．     

Adaptive dual-layer super-twisting control and observation

ABSTRACT

In this paper, a super-twisting-like structure with adaptive gains is proposed. The structure is parameterised by two scalar gains, both of which adapt, and by an additional time-varying term. The magnitudes of the adaptive terms are allowed to both increase and decrease as appropriate so that they are as small as possible, in the sense that they do not unnecessarily over-bound the uncertainty, and yet are large enough to sustain a sliding motion. In the paper, a new time varying gain is incorporated into the traditional super-twisting architecture. The proposed adaption law has a dual-layer structure which is formally analyzed using Lyapunov techniques. The additional term has the effect of simplifying the stability analysis whilst guaranteeing the second-order sliding mode properties of the traditional super-twisting scheme.     

An adaptive sliding mode differentiator for actuator oscillatory failure case reconstruction

This paper proposes an adaptive sliding mode super-twisting differentiator which allows the gains to adapt based on the ‘quality’ of the sliding motion. A Lyapunov based analysis for the adaptive super-twisting scheme is presented to demonstrate its properties. As an example, the adaptive differentiator proposed in this paper has been used as part of a nonlinear FDI scheme for an Oscillatory Failure Case (OFC) in an actuator. The FDI scheme requires an estimate of the rod speed which is provided by the adaptive super-twisting differentiator. Due to the conditions in which the actuator operates, normally the differentiator gains are initialised at low values to ensure good rod speed estimation in fault free conditions. However for large amplitude/frequency OFCs, the gains must adapt in order to maintain sliding and provide a good estimation. Simulations on a high fidelity nonlinear aircraft benchmark model have been carried out for both liquid and solid OFCs.     

机械臂自适应反演超螺旋全局终端滑模控制

针对机械臂系统存在的系统参数摄动、非线性摩擦及外部干扰等不确定问题,提出一种自适应反演超螺旋全局终端滑模轨迹跟踪控制方法。该方法基于反演法、Lyapunov理论和全局快速终端滑模理论设计控制器,保证系统稳定性及全局收敛性,增强系统的鲁棒性。为解决系统集总扰动上界未知的问题,采用自适应技术设计切换控制律,抵消不确定性的影响,同时引入超螺旋算法抑制滑模控制固有的抖振现象。最后,通过理论分析和仿真算例验证了该控制器的有效性与可行性。     

Fault Diagnosis and Sliding Mode Fault Tolerant Control for Non‐Gaussian Stochastic Distribution Control Systems Using T‐s Fuzzy Model

For the non‐Gaussian stochastic distribution control system using Takagi‐Sugeno fuzzy model, a new fault diagnosis and sliding mode fault tolerant control algorithm is presented. First, a new adaptive fault diagnosis algorithm is adopted to diagnose the fault that occurred in the system, and the observation error system is proven to be uniformly bounded. Second, the sliding mode control algorithm is used to reconfigure the controller, based on the fault estimation information. The post‐fault probability density function can still track the given distribution, leading to fault tolerant control of non‐Gaussian stochastic distribution control systems using Takagi‐Sugeno fuzzy model. Finally, simulation results show the effectiveness of the proposed method.     

Robust fault diagnosis and fault‐tolerant control for non‐Gaussian uncertain stochastic distribution control systems

The purpose of fault diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density function to obtain the fault estimation information. A fault diagnosis and sliding mode fault‐tolerant control algorithms are proposed for non‐Gaussian uncertain stochastic distribution control systems with probability density function approximation error. The unknown input caused by model uncertainty can be considered as an exogenous disturbance, and the augmented observation error dynamic system is constructed using the thought of unknown input observer. Stability analysis is performed for the observation error dynamic system, and the H_∞ performance is guaranteed. Based on the information of fault estimation and the desired output probability density function, the sliding mode fault‐tolerant controller is designed to make the post‐fault output probability density function still track the desired distribution. This method avoids the difficulties of design of fault diagnosis observer caused by the uncertain input, and fault diagnosis and fault‐tolerant control are integrated. Two different illustrated examples are given to demonstrate the effectiveness of the proposed algorithm. Copyright © 2016 John Wiley & Sons, Ltd.     

基于双观测器并行结构的四旋翼无人机故障检测与重构

以四旋翼飞行器执行单元增益型故障的检测与重构为研究内容,设计基于滑模观测器的故障检测算法。针对飞行器姿态控制系统的驱动单元冗余特性,提出一种并行双降维观测器与超螺旋算法相结合的故障重构算法。对所提出的算法进行了理论分析,并通过数值仿真验证了所提出算法的有效性。     

On-line optimal autonomous reentry guidance based on improved Gauss pseudospectral method

The on-line optimal autonomous reentry guidance of the hypersonic vehicle is proposed based on the improved Gauss pseudospectral method （IGPM）. The autonomous reentry guidance requires the hypersonic vehicle can generate the optimal trajectory for the latest flight mission on line and track the new trajectory well under the uncertainty. These two problems are simplified into a nonlinear optimal control problem with nonlinear constraints. The IGPM is introduced to solve the above two problems. The trajectory has to change according to the flight mission and therefore the new optimal trajectory can be obtained by IGPM within about 10 s. The optimal feedback guidance law is used to track the reference trajectory to handle the uncertainty. In order to deal with the CPU time delay, which comes from optimizing the new trajectory, the new on-line optimal autonomous reentry guidance is depicted. Finally, the numerical simulation shows that the proposed autonomous guidance can generate optimal trajectory for the new flight mission and have a high tracking accuracy