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.     

全状态约束的时滞系统神经网络输出反馈控制

针对一类严格反馈形式的单输入单输出时滞系统,研究在全状态约束下的输出反馈控制.首先,设计状态观测器估计不可测量的状态;其次,利用RBF神经网络逼近未知的非线性函数,利用障碍Lyapunov函数确保全状态约束及Lyapunov-Krasovskii方法消除时滞对系统的影响;最后,设计输出反馈控制器,并且有更少的更新参数减少了计算负荷.所设计的控制器可以保证闭环系统中所有信号半全局一致最终有界,信号误差收敛到小的领域内.仿真例子进一步验证了所提出方法的有效性.     

时滞系统的降维状态预测观测器及预测控制器设计

研究控制项含有时滞的线性系统的预测控制问题.利用被控对象的预测输出向量和系统的控制向量,设计了一种降维状态预测观测器,并将该状态观测器用于时滞控制系统的最优状态反馈控制中.利用该状态预测观测器可将闭环系统的时滞项移至系统闭环结构之外,从而最优控制规律完全可以按无时滞系统进行设计.由性能指标计算公式表明,该预测控制器关于二次型性能指标是次优的.     

时滞系统的状态反馈和基于观测嚣的输出反馈设计

考虑了同时具有状态和输入时滞线性定常系统的H∞镇定问题．基于动态耗散理论和微分对策原理,通过采用带积分项的储存函数,对系统的状态反馈控制器和基于观测器的输出反馈设计问题进行了处理．它们的可解充分条件可以化为与时滞相关的矩阵不等式和Riccati方程的形式．得到的与时滞相关的状态反馈控制律和基于观测器的输出反馈控制律都能使闭环系统内稳且具有H∞干扰衰减．     

基于观测器的一类连续非线性系统的采样控制

首先使用反演方法分别设计了系统的连续时间状态反馈控制器、连续时间观测器和基于连续时间观测器的连续时间控制器. 接下来, 利用零阶保持法对连续时间状态反馈控制器进行离散化, 获得了状态反馈采样控制器; 利用零阶保持法对基于连续时间观测器 的连续时间控制器离散化, 获得了基于连续时间观测器的采样控制器; 利用Euler法对连续时间观测器离散化, 同时利用零阶保持法对控制器离散化, 从而获得了采样观测器和基于采样观测器的采样控制器. 本文论证了上述状态反馈采样控制器和基于连续时间观测器的采样控制器可以保证闭环系统渐近稳定, 而基于采样观测器的采样控制器可以保证被控对象的状态是有界的, 其最终边界依赖于设计参数与采样周期. 最后, 通过选择适当的采样周期, 完成了闭环采样控制系统的设计. 一个船舶航向控制的例子表明应用本文 所提方法设计出的三种采样控制器具有良好的控制效果.     

基于降阶扩张状态观测器的重复控制系统设计

针对一类含有非匹配状态相关不确定性和外界干扰的伺服系统,提出一种基于降阶扩张状态观测器的重复控制方法,通过对不确定性和扰动进行实时估计和主动补偿,实现对周期性参考输入信号的高精度跟踪.首先,利用系统可测输出和控制输入信号设计降阶扩张状态观测器,对系统的不可测状态以及包含不确定性和外界干扰的总扰动进行估计;其次,通过选择合适的扰动补偿增益,构造基于扰动动态补偿的复合重复控制规律,消除总扰动对系统输出的影响,保证系统输出对周期性参考信号的准确跟踪;然后,基于小增益定理推导出系统稳定性条件和控制器参数设计算法;最后,通过数值仿真实例和实验验证所提方法的有效性和优越性.     

基于滑模状态观测器的电液位置伺服系统控制

在电液伺服系统优化设计的研究中,针对电液位置伺服系统的高阶非线性特性、系统参数不确定性以及系统状态信号测量困难的情况,提出一种基于滑模状态观测器的反演控制策略.策略采用滑模方法设计状态观测器,只需要位置传感器,不需要速度传感器和加速度传感器.对采用状态观测器之后的系统,设计反演控制器,针对系统中的不确定性,在反演控制的最后一步采用滑模控制设计,基于Lyapunov方法证明了系统中所有信号是一致最终有界的,闭环系统是稳定的.仿真结果表明,上述策略为电液伺服系统优化设计提供了参考.     

时滞系统的状态预测观测器及预测控制器设计

研究基于状态空间模型的时滞控制系统的状态预测观测器及最优预测控制器的设计问题。针对控制项含有时滞的系统，设计一种全维状态预测观测器，并将其用于时滞控制系统的最优状态预测反馈控制。通过该状态预测观测器可将闭环系统的时滞项移至系统闭环结构之外，从而使其优化控制规律完全可按无时滞系统进行设计。所给出的性能指标计算公式表明，该预测控制器关于二次型性能指标是次优的。     

严格反馈非线性系统的自适应神经网络输出反馈控制

基于非线性反馈函数,文章设计神经网络状态观测器,解决一类非线性系统的输出反馈控制问题.非线性反馈神经网络观测器在系统存在不确定性函数的情况下实时估计系统状态.利用所获得的状态信号,设计了自适应神经网络动态面控制器,同时保证了闭环系统的稳定性和所有信号的有界性.通过调节设计参数的取值能够达到期望的闭环跟踪性能.数值仿真表明,所设计的状态观测器不需要对原系统做状态变换,能够克服输出反馈滑模控制器带来的抖震问题.     

双率采样系统的基于观测器的网络化H∞控制

研究一类带有网络传输时滞和丢包的双率采样系统的网络化H∞控制问题. 假设对象状态变量被分成两个分向量, 同一分向量的状态变量由同一类传感器以相同周期采样, 且两类传感器的采样频率不同. 采样后的分向量分别通过非理想网络传输到控制器端. 考虑到双率采样、网络传输时滞和丢包现象, 引入同步观测器来估计对象状态并设计基于估计状态的控制器来镇定双率采样系统. 基于这个思路, 将双率采样的网络化控制系统建模为带有两个时变时滞的连续系统. 利用Lyapunov-Krasovskii泛函方法, 以矩阵不等式形式给出该系统的稳定性判据和控制器设计方法. 最后, 通过数值例子验证所提方法的有效性.     

Inter-sample output predictor based sampled-data ADRC supporting high precision control of VCM servo systems

Ultra high precision servo control systems usually require fast sampling rate to achieve desired digital control performance, where output sampling restrictions (such as sensor bandwidth) pose major challenges for such applications. In this paper, we propose a novel sampled-data multi-rate active disturbance rejection control (ADRC) architecture under sampling rate restrictions, where a digital extended state observer (ESO) with inter-sample output prediction structure is employed to construct inter-sample dynamics between two consecutive sampling instants, such that high frequency disturbances can be better rejected. The convergence of the estimation and the stability of the closed-loop system are analyzed based on Lyapunov function method and separation principle. The proposed control architecture is implemented on a voice coil motor (VCM) actuated micro–nano servo gantry, demonstrating significant performance improvement over existing single-rate or multi-rate ADRC methods.     

Multirate forward-model disturbance observer for feedback regulation beyond Nyquist frequency

A fundamental challenge in digital control arises when the controlled plant is subjected to a fast disturbance dynamics but is only equipped with a relatively slow sensor. Such intrinsic difficulties are, however, commonly encountered in many novel applications such as laser- and electron-beam-based additive manufacturing, human–machine interaction, etc. This paper provides a discrete-time regulation scheme for exact sampled-data rejection of disturbances beyond Nyquist frequency. By introducing a model-based multirate predictor and a forward-model disturbance observer, we show that the inter-sample disturbances can be fully attenuated despite the limitations in sampling and sensing. The proposed control scheme offers several advantages in stability assurance and lucid design intuitions. Verification of the algorithm is conducted on a motion control platform that shares the general characteristics in several advanced manufacturing systems.     

Disturbance‐prediction–based control of input time delay systems for rejection of unknown frequency disturbances

In this paper, a novel disturbance rejection approach is presented for a class of input time‐delay systems subject to sinusoidal disturbances with unknown frequency. In particular, an auxiliary observer is proposed to represent the periodic disturbance in a parametric uncertainty form, where the unknown factor related to disturbance frequency can be estimated. Furthermore, the correlation between the future disturbance and the auxiliary observer output is analyzed, such that the future disturbances can be predicted and rejected through the input channel. Based on the aforementioned observer and predictor structure, the overall control architecture can be established as a framework of disturbance‐prediction–based control for systems with input time delays, where the conditions on the asymptotic stability of the closed‐loop systems are also derived. Finally, numerical examples are provided to illustrate the effectiveness of the proposed control approach.     

浊度大时滞过程的预测自抗扰控制器设计

待滤水浊度过程涉及复杂的物理、化学反应,具有明显的大时滞、不确定性和干扰多等特点,一直是制水行业公认的难控系统.针对其干扰多和不确定性特点,采用自抗扰控制来主动实时估计扰动并进行补偿;针对其大时滞特点,采用预测控制对输出提前预报来弥补大时滞系统中的信息不及时,从而得到一种既具信息预估又具主动补偿总扰动的预测自抗扰控制器.本文重点分析了预测自抗扰控制器的性能,给出了时滞系统在该控制器作用下的开环频率参数求取方法及简单实用的参数整定公式,最后将其与几种常见控制器进行了仿真比较.仿真结果表明:预测自抗扰控制器具有良好的抗扰恢复能力和设定值跟踪能力,且参数整定容易,具有简单、好用且有效等特点,为该控制器的工业化应用提供了积极的指导作用.     

Period-robust repetitive model predictive control

Repetitive model predictive control (RMPC) incorporates the idea of repetitive control (RC) into the basic formulation of model predictive control (MPC) to enable the user to take full advantage of the constraint handling, multivariable control features of MPC in controlling a periodic process. The RMPC achieves perfect asymptotic setpoint tracking/disturbance rejection in periodic processes, provided that the period length used in the control formulation matches the actual period of the reference/disturbance signal exactly. Even a small mismatch between the actual period of the process and the controller period can deteriorate the RMPCs performance significantly. The period mismatch can occur either from an inaccurate estimation of the actual frequency of disturbance due to resolution limit or from trying to force the controller period to be an integer multiple of the sampling time. For such cases, an extension of RMPC called “period-robust” repetitive model predictive control (pr-RMPC) is proposed. It is based on the idea of using weighted, multiple memory loops in RC, such that small changes in period length do not diminish the tracking/rejection properties by much. Simulation results show that, in case of a slight period mismatch, pr-RMPC achieves significant improvement over the standard RMPC in rejecting periodic disturbances.     

Static output feedback sliding mode control for time‐varying delay systems with time‐delayed nonlinear disturbances

In this paper, a robust stabilization problem for a class of linear time‐varying delay systems with disturbances is studied using sliding mode techniques. Both matched and mismatched disturbances, involving time‐varying delay, are considered. The disturbances are nonlinear and have nonlinear bounds which are employed for the control design. A sliding surface is designed and the stability of the corresponding sliding motion is analysed based on the Razumikhin Theorem. Then a static output feedback sliding mode control with time delay is synthesized to drive the system to the sliding surface in finite time. Conservatism is reduced by using features of sliding mode control and systems structure. Simulation results show the effectiveness of the proposed approach. Copyright © 2009 John Wiley & Sons, Ltd.     

Optimal disturbance rejection control approach based on a compound neural network prediction method

A new optimal disturbance rejection control method is proposed for the system with disturbances via a compound neural network prediction approach in this paper. The disturbances caused by external disturbances and model mismatches can be estimated by a disturbance observer, and the estimation of disturbances is introduced into the neural network predictive model to make the predictive output more accurate. Then based on the new compound neural network predictive model, a controller, which ensures both optimal performance by the receding horizon optimization and strong disturbance rejection ability, is obtained. The proposed scheme is applied to control the temperature of a simplified jacketed stirred tank heater (JSTH). Simulation results demonstrate the effectiveness of the proposed control method.     

Control system design based on a nonlinear first-order plus time delay model

Most chemical processes are nonlinear in nature. When large set point changes or load disturbances occur frequently, nonlinear control systems are required. Instead of using the differential geometric method or nonlinear model predictive method, simple gain scheduling may be sufficient for many nonlinear single input-single output (SISO) processes. To design such simple control systems systematically, a nonlinear first-order plus time delay model is proposed for model-based control. A logarithmic transformation which is very useful for control of high purity distillation columns is shown to be effective in general. Several chemical process examples are also given.     

Computation delay compensation for real time implementation of robust model predictive control

The implementation of model predictive control (MPC) requires to solve an optimization problem online. The computation time, often not negligible especially for nonlinear MPC (NMPC), introduces a delay in the feedback loop. Moreover, it impedes fast sampling rate setting for the controller to react to uncertainties quickly. In this paper, a dual time scale control scheme is proposed for linear/nonlinear systems with external disturbances. A pre-compensator works at fast sampling rate to suppress uncertainty, while the outer MPC controller updates the open loop input sequence at a slower rate. The computation delay is explicitly considered and compensated in the MPC design. Four robust MPC algorithms for linear/nonlinear systems in the literature are adopted and tailored for the proposed control scheme. The recursive feasibility and stability are rigorously analysed. Three simulation examples are provided to validate the proposed approaches.     

基于网络的运动控制系统预测控制研究

针对一类基于网络的运动控制系统中存在的时延和丢包问题进行研究。对于网络运动控制系统存在数据时延和丢包的情况,建立网络运动控制系统模型。在此模型下,对预测函数控制算法进行分析,设计了预测函数控制产生器和数据延时预测补偿器,并研究相应的补偿策略。仿真分析表明算法的合理性和有效性。