针对一类非线性系统的多变量线性 扩张状态观测器及其收敛性分析

针对一类非线性不确定系统,构造了一种多变量线性扩张状态观测器（Multi-variable linear extended state observer,MVLESO）,用于实时估计非线性系统的不确定动态.采用频域分析方法,剖析了所构造的MVLESO在非线性系统不确定动态估计方面的收敛性,并推导出不确定动态的频域估计误差模型.仿真结果表明,所设计的MVLESO可以较为精准地估计出非线性系统当前的不确定动态,该特性为基于MVLESO鲁棒控制方案的有效实施奠定了基础.     

用于带有量测噪声系统的新型扩张状态观测器

提出了扩张状态观测器的一种新的形式,用于处理量测环节带有噪声干扰时的情况.ESO可以对不确定系统中的内外扰动进行观测,并以此为基础构成自抗扰控制器.但其性能会受量测噪声的影响.本文利用滤波器消除噪声的影响,并把已知的滤波器方程扩展到原有的ESO中,以补偿滤波器对实际输出信号的偏移作用.数字仿真表明,该方法可以有效的解决输出噪声对扩张状态观测器的影响.     

扩张状态观测器的性能与应用

研究自抗扰控制方法中的扩张状态观测器(ESO)。通过频域分析可得出该观测器性能随频率升高而逐渐衰减的结论,衰减程度取决于观测器的参数和系统的采样频率,并指出对现有的参数配置方法加以改进可以获得补偿性能更好的观测器。提出了一种非线性扩张状态观测器,在同等采样率条件下能够提高观测器的跟踪性能。仿真结果表明,所提出的状态观测器与现有的观测器相比,在主动控制中效果更好。     

串联型扩张状态观测器构成的自抗扰控制器

利用自抗扰控制器控制ｍ阶对象,需要调整扩张状态观测器的ｍ＋１个参数。结构和参数相同的ｍ个二阶扩张状态观测器串联而成的串联型扩充状态观测器,具有ｍ＋１阶扩张状态观测器的功能。用其构成的自抗扰控制器参数易于调整,便于工程应用。     

基于神经网络的非线性扩张状态观测器

自抗扰控制方法是一种新型的非线性设计方法,在自抗扰控制器中主要存在着确定待定参数的问题;非线性扩张状态观测器是自抗扰控制器的核心,在研究非线性扩张观测器中的参数整定问题时,将神经网络的思想引入参数整定,提出了基于神经网络的非线性扩张状态观测器的设计方法,运用该方法可以对任意阶的非线性扩张状态观测器进行参数设计;大量仿真算例表明,设计出的观测器具有良好的鲁棒性,有一定工程应用参考价值.     

线性/非线性切换扩张状态观测器的收敛性和稳定性分析

线性/非线性切换扩张状态观测器(linear/nonlinear switching extended state observer, SESO)是为追求更高的控制精度和抗扰能力,在进行状态估计时根据状态误差大小在线性扩张状态观测器(linear extended state observer, LESO)和非线性扩张状态观测器(nonlinear extended state observer, NLESO)之间进行切换的一种扩张状态观测器.但由于该算法存在非线性结构,目前还没有从频域角度对该方法进行收敛性和控制系统稳定性分析的相关研究.针对此问题,以二阶系统为研究对象,首先对SESO的结构及参数选择进行了介绍,然后,分别采用等效增益法和描述函数法,从频域角度对了其状态估计误差的收敛性和基于SESO的自抗扰控制系统的稳定性进行了研究.结果表明,三阶SESO具有良好的收敛性,能实现对系统状态和"总扰动"的误差估计,并且选择适当的SESO参数,可避免极限环的产生.     

自适应扩张状态观测器收敛性分析与应用

为了实现自适应扩张状态观测器理论分析的简洁和工程应用的直观,本文提出了一种自适应扩张状态观测器(adaptive extended state observer, AESO),并给出了收敛性证明过程.然后,将其应用到直线电机伺服系统中,设计了基于AESO的PD控制器.实验结果表明,提出的AESO可以准确估计系统的状态和受到的干扰,并且可以消除高增益线性扩张状态观测器固有的峰化现象,同时还验证了设计的观测器–控制器的有效性.     

修改型扩张状态观测器:分析与实现

利用系统已知信息构造的扩张状态观测器(extended state observer,ESO)称之为修改型扩张状态观测器.本文完善了修改型扩张状态观测器(modified extended state observer,MESO)的假设条件,并分析了相对于常规的扩张状态观测器,修改型扩张状态观测器估计精度更高的原因.针对系统模型参数不确定情况,提出了基于最小二乘法的修改型扩张状态观测器实现方法,并详细论述了最小二乘法在线辨识系统模型的原理.仿真结果与理论分析结果一致,验证了修改型扩张状态观测器理论分析的正确性和实现方法的可行性.     

基于扩张状态观测器的鲁棒迭代学习控制

针对一类包含模型不确定和外界干扰等非重复扰动的线性离散系统,本文通过将迭代学习控制与自抗扰技术相结合,提出一种新的基于扩张观测器的鲁棒迭代学习控制方法.本文以时间轴和迭代轴两个方向同时出发考虑系统的非重复扰动估计和稳定收敛问题.将与时间和迭代轴同时相关的模型不确定及外界干扰等因素归纳为系统总扰动,针对其非重复变化特性给出了扩张观测器的设计,保证在批次内快速、准确地估计系统总扰动;基于上述扰动估计,设计新型的迭代学习控制律,利用线性矩阵不等式方法证明了整个鲁棒迭代学习系统的稳定性和收敛性,并给出合理的控制器参数估计条件.此外,讨论了迭代学习控制中第一批次的控制律设计问题,给出合理的自抗扰控制器设计.最后通过仿真对比实验验证了本文方法的可行性和有效性.     

三阶离散扩张状态观测器的稳定性分析及其综合

在连续扩张状态观测器(Extended State Observer, ESO)结构的基础上,提出了一种离散形式的三阶ESO,并给出了稳定性分析．当系统扰动变化不是很快的时候,所提出的ESO能够将状态估计误差和扰动估计误差限制在一个很小的范围内．仿真结果证明了理论分析的有效性．     

三阶扩张状态观测器的优化参数配置方法

研究三阶非线性扩张状态观测器扰动观测性能的优化问题。首先,采用系数冻结法固定与观测器状态相关的非线性系数,利用线性系统的极点配置方法进行频带拓展;然后,分析配置后的极点随非线性系数的变化规律,在此基础上,提出一种保证扰动观测带宽受系统状态变化影响最小的参数配置方法;最后,通过算例和仿真对比表明了所提出参数配置方法的有效性和优越性。     

Sampled-data extended state observer for uncertain nonlinear systems

In this paper, we present a sampled-data nonlinear extended state observer (NLESO) design method for a class of nonlinear systems with uncertainties and discrete time output measurement. To accommodate the inter-sample dynamics, an inter-sample output predictor is employed in the structure of the NLESO to estimate the system output in the sampling intervals, where the prediction is used in the proposed observer instead of the system output. The exponential convergence of the sampled-data NLESO is also discussed and a sufficient condition is given by the Lyapunov method. A numerical example is provided to illustrate the performance of the proposed observer.     

Extended state observer for MIMO nonlinear systems with stochastic uncertainties

ABSTRACT

In this paper, both linear extended state observer (ESO) and nonlinear ESO with homogeneous weighted functions are proposed for a class of multi-input multi-output (MIMO) nonlinear systems composed of coupled subsystems with large stochastic uncertainties. The stochastic uncertainties in each subsystem including internal coupled unmodelled dynamics and external stochastic disturbance without known statistical characteristics are lumped together as the stochastic total disturbance (extended state) of each subsystem. The linear ESO and nonlinear ESO are designed separately for real-time estimation of not only the unmeasured state but also the stochastic total disturbance of each subsystem. The practical mean square convergence of these two classes of ESOs are developed. Some numerical simulations are presented to demonstrate the effectiveness of the ESOs with the advantages of smaller peaking values and more accurate estimation by the nonlinear ESO.     

高阶线性自抗扰控制器的性能评估

以二阶系统为研究对象,在线性扩张观测器(LESO)的基础上,给出高增益形式的高阶LESO.基于高阶线性自抗扰控制器(HLADRC)二自由度闭环传递函数和频域特性曲线,证明了其状态估计误差的收敛性以及高阶线性自抗扰控制器的稳定性.同时,系统地分析了输入增益和模型参数不确定性对稳定鲁棒性的影响,推导出满足系统稳定条件的参数??的稳定域以及系统干扰抑制动态特性与带宽的关系.最后,通过与线性自抗扰控制器(LADRC)的对比仿真表明, HLADRC在干扰抑制方面具有很大的优势,而LADRC在稳定鲁棒性和控制品质方面具有更好的效果,从而为工程设计提供了理论依据和实践参考.     

Control of the common rail pressure in gasoline engines through an extended state observer based MPC

In this paper, a model predictive control (MPC) solution, assisted by extended state observer (ESO), is proposed for the common rail pressure control in gasoline engines. The rail pressure dynamic, nonlinear with large uncertainty, is modeled as a simple first order system. The discrepancy of the model from the real plant is lumped as ``total disturbance'', to be estimated in real-time by ESO and then mitigated in the nonlinear MPC, assuming the total disturbance does not change in the prediction horizon. The nonlinear MPC problem is solved using the Newton/generalized minimum residual (GMRES) algorithm. The proposed ESO-MPC solution, is compared with the conventional proportional-integral-differential (PID) controller, based on the high-fidelity model provided in the benchmark problem in IFAC-E-CoSM. Results show the following benefits from using ESO-MPC relative to PID (benchmark): 1) the disturbance rejection capability to fuel inject pulse step is improved by 12% in terms of recovery time; 2) the transient response of rail pressure is improved by 5% in terms of the integrated absolute tracking error; and 3) the robustness is improved without need for gain scheduling, which is required in PID. Additionally, increasing the bandwidth of ESO allows reducing the complexity of the model implemented in MPC, while maintaining the disturbance rejection performance at the cost of high noise-sensitivity. Therefore, the ESO-MPC combination offers a simpler and more practical solution for common rail pressure control, relative to the standard MPC, which is consistent with the findings in simulation.     

A hybrid fault prediction method for control systems based on extended state observer and hidden Markov model

Fault diagnosis and prediction for complex control systems rely either on the collection of rich data for training neural networks or on the system models and prior knowledge of faults. These methods are difficult to apply directly in complex integrated systems due to the large uncertainties in practical scenarios. A new fault diagnosis and prediction technique that is based on extended state observer (ESO) and a hidden Markov model (HMM) for control systems is proposed in this paper. Real-time and predictive information that is obtained by ESO of the active disturbance rejection control (ADRC) is utilized to improve the HMM method for the fault prediction of control systems with large uncertain disturbances. The proposed approach realizes a high recognition rate with a small demand for data, and the dependence on the system model is weak without prior knowledge of faults. Fault prediction of the control system output can be realized without additional sensors. The proposed solution is evaluated in simulations of an asynchronous servo motor control system against the traditional control method and the ADRC control. The results indicate that the proposed method performs well in fault prediction and outperforms the traditional method in terms of control when disturbances and failures occur.