具有两级线性辨识结构的极点配置自校正控制器

提出了一种极点配置自校正控制器的设计方法,这种方法用两级线性辨识器可直接获得控制器参数,无需求解Diophantine方程,也不需要辨识多余的辅助参数,而且可以消除确定扰动的影响.由于辨识器具有低阶、线性的特点,因此有较好的收敛性.文中证明了辨识器的有效性,给出了仿真结果.     

非线性系统模糊神经网络控制的改进策略

针对以模糊神经网络自适应方法为核心的不确定非线性系统控制问题, 以常规静态模糊神经网络控制结构为基础, 分别就控制器、辨识器及优化算法3个方面展开改进研究. 以一种改进结构的动态PID型模糊神经网络为控制器, 最小二乘支持向量机为辨识器构成控制系统. 利用带混沌搜索的量子粒子群算法离线优化结合在线误差反传微调的寻优策略优化控制器参数, 带混沌扰动的粒子群离线优化支持向量机的核参数, 并通过对系统稳定性的讨 论将改进的控制系统逐步完善. 对某热交换对象模型的数值仿真验证了该改进方法的可行性和有效性.     

基于扰动和摩擦补偿的柔性机械臂系统非奇异快速终端滑模控制

本文针对系统中存在的关节摩擦、动力学参数不确定性和外部负载干扰等因素引起的柔性机械臂系统控制性能下降的问题,提出了一种基于扰动和摩擦补偿的非奇异快速终端滑模控制方法(NFTSMC-DE-FC).首先,设计扰动估计器(DE)对系统未知动态参数和负载干扰进行估计.然后,针对扰动估计器不能精确估计的关节摩擦力矩进行辨识.最后,利用滑模控制技术设计非奇异快速终端滑模控制器,并将扰动估计值和摩擦力辨识值以前馈的方式进行补偿,实现对柔性机械臂系统给定参考轨迹跟踪的准确性以及对外界扰动的鲁棒性.值得注意的是,与传统只使用扰动估计器的方法相比,本文考虑到了摩擦力等非线性因素的影响,并利用辨识技术对摩擦力进行辨识,提高了控制精度.利用Lyapunov稳定性定理从理论上证明了所设计的控制器可以保证闭环系统的稳定性.实验结果表明,相较于非奇异快速终端滑模控制方法(NFTSMC)和基于扰动估计器的非奇异快速终端滑模控制方法(NFTSMC-DE),所提方法提高了柔性机械臂系统的轨迹跟踪性能.     

目标规划法在预测控制滚动优化及在线辨识中的应用

针对有约束多目标多自由度预测控制问题,应用目标规划方法,提出了一种既适合于参数模型又适合于非参数模型的在线滚动优化策略,并且通过计算饥仿真研究,验证了该方法的有效性.然后,对于参数模型预测控制问题,提出了一种抗扰动的最小绝对值辨识算法.由于该辨识算法可用目标规划快速求解,因此可作为慢时变工业过程控制的在线辨识算法.     

基于状态观测与教学优化算法的多变量系统历史数据驱动辨识

常规智能算法与历史数据结合进行多变量系统辨识的方法,选取表征系统由稳态过渡到动态过程的数据作建模数据,当该过程含有未知扰动时,无法准确建立对象模型.本文提出一种基于状态观测与教学优化算法的多变量系统历史数据驱动辨识方法.该方法选取系统由动态回归稳态的历史数据,并根据其稳态终值进行去稳态分量处理.再将其分为两段,应用状态观测器与预估模型对第1段数据末端的系统状态进行估计,并将估计值作为第2段数据对应的系统初态;应用第2段数据的输入对预估模型进行仿真,采用教学优化算法寻优预估模型参数,使仿真输出接近实际输出.仿真实验表明该方法可以克服扰动对模型辨识精度的影响.最后对某火电机组协调控制系统进行建模,结果表明了该方法的有效性.     

基于优化模型补偿自抗扰的伺服控制方法研究

在永磁同步电机(PMSM)自抗扰控制器(ADRC)系统中,扩张状态观测器(ESO)在扰动较多且幅值变化大时难以保证估计精度,而普通的模型补偿自抗扰控制器的性能又受到参数辨识精度和辨识算法复杂度的限制.针对该问题,提出一种伺服系统优化的模型补偿自抗扰控制方法.以交轴电流和实际转速作为线性扩张状态观测器(LESO)输入,采用二阶LESO对系统总扰动进行观测,将此观测值作为补偿模型补偿到速度环ADRC的ESO中,并在控制量的扰动补偿项中去除该补偿模型,实现模型补偿的目的.仿真和实验结果表明,该方法显著降低了ADRC中ESO要估计量的变化幅度,提高了扰动估计精度,同时不需要进行额外的参数辨识,可实时在线获取补偿模型,具有较好的动态特性与抗扰动能力.     

多变量CARMA模型的结构辨识

本文提出了多变量受控的自回归滑动平均(CARMA)模型结构辨识的新方法.根据模型参数的递推增广最小二乘法(RELS)估计,给出了确定模型的阶、子阶和时滞的F检验判决器,且可得到节省参数模型.本方法推广和改进了Bokor和Kevizky的结构辨识方法,数值模拟例子证明了所提出方法的有效性.     

基于模型分解算法模型辨识器的设计与应用

介绍了模型辨识器的设计和实际应用.模型辨识器的辨识算法采用基于模型分解方法的辨识算法,软件设计上采用模块化的程序设计原则.辨识器从实用性出发,注重辨识的速度、简洁,可以方便给出系统对象的阶次、参数和时延的无偏估计,可以灵活地对多变量系统进行辨识,辨识出的模型有利于控制器的设计.还对一个实际的装置进行了应用研究,获得的结果是精确的,说明了该软件的有效性和实用性.     

非线性系统第一类模糊辨识器的设计及仿真

该文针对非线性系统的辨识问题,给出了第一类模糊辨识器的设计方案,该方案通过引入最优逼近误差的自适应律参数项,实时地调整参数来实现对非线性系统的辨识.采用此方法可使辨识器模型的输出很快收敛到真实系统,且辨识误差渐进收敛到零.该文根据此算法编写了便于仿真实现的MATLAB程序,且给出了此程序的解算流程图.最后对Rossler混沌系统的实例进行仿真,绘制了系统真实曲线和辨识器模型输出的估计值曲线,仿真结果说明了该方法在非线性系统辨识中的使用性和可行性.     

神经网络并联辨识算法的收敛性研究

神经网络可用来建立非线性动态系统的模型，其辨识模型可分为串联并联辨识模型和并联辨识模型两种，后者的思路源于基于参考模型自适应方案的输出误差辨识模型，对观测扰动有较强的抑制能力。本文对这种神经网络并联辨识结构的收敛性进行了研究，指出在网络参数满足一定条件时并联预测过程收敛，且并联辨识算法具有局部收敛性，仿真实验验证了上述结论。     

Development of grey box state estimators for systems subjected to time correlated unmeasured disturbances

Unmeasured disturbances, which arise from uncertainties in the physical input sources, are commonly encountered in a process operation. For the purpose of developing Bayesian state estimators, such disturbances have been traditionally treated as Gaussian white noise processes. In practice, however, such disturbances are often correlated in time and the simplistic white noise assumption may not hold. Thus, to generate accurate estimates of the states, it is essential to obtain a reasonably accurate characterisation of the dynamics associated with the unmeasured disturbances. In this work, a systematic approach has been developed for identifying discrete time stochastic disturbance models, which captures the dynamics associated with such unmeasured disturbances. Under certain simplifying assumptions, the discrete time unmeasured disturbance models are combined with a continuous time mechanistic model to derive a discrete nonlinear grey box model. The grey box model is further used to formulate a nonlinear Bayesian state estimator. A constrained optimisation problem, that maximizes the log likelihood function of the innovation sequence generated by the state estimator, is formulated and solved for estimation of the parameters of the unmeasured disturbance model and the measurement noise covariance from the input–output data. The efficacy of this approach is demonstrated by simulating a benchmark continuous fermenter system and using experimental data obtained from a heater-mixer setup. The simulation studies demonstrate that the proposed approach is able to identify correlated disturbance models that closely match the characteristics of the true unmeasured disturbance models.     

Disturbance modeling for offset-free linear model predictive control

An offset-free controller is one that drives controlled outputs to their desired targets at steady state. In the linear model predictive control (MPC) framework, offset-free control is usually achieved by adding step disturbances to the process model. The most widely-used industrial MPC implementations assume a constant output disturbance that can lead to sluggish rejection of disturbances that enter the process elsewhere. This paper presents a general disturbance model that accommodates unmeasured disturbances entering through the process input, state, or output. Conditions that guarantee detectability of the augmented system model are provided, and a steady-state target calculation is constructed to remove the effects of estimated disturbances. Conditions for which offset-free control is possible are stated for the combined estimator, steady-state target calculation, and dynamic controller. Simulation examples are provided to illustrate trade-offs in disturbance model design.     

Optimal Output Feedback Controllers for Spacecraft Attitude Tracking

This paper investigates the problem of output feedback attitude tracking control of a rigid spacecraft in the presence of external disturbances. Two optimal control laws with a disturbance estimator are developed to deal with this problem. An adapted extended state observer is used to estimate the angular velocity tracking errors and to allow for compensation for the total disturbances. The proposed control can be expressed as the sum of a nonlinear optimal controller and an estimated disturbance. For the optimal controller, the state‐dependent Riccati equation and optimal Lyapunov techniques are employed to solve the infinite‐time nonlinear optimal control problem. The developed controllers can minimize a performance index and ensure the stability of the closed‐loop system and external disturbance attenuation. On the other hand, using the adapted extended state observer, the asymptotic convergence of estimation error dynamics is proven. An example of multiaxial attitude manoeuvres is given and simulation results are included to demonstrate and verify the usefulness of the proposed controllers.     

Robust Consensus Tracking Control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

In this paper, a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed graph. It is of two-degree-of-freedom nature. Specifically, a robust distributed controller is designed for consensus tracking, while a local disturbance estimator is designed for each agent without requiring the input channel information of disturbances. The condition for asymptotic di...     

自适应扰动估计的相关积分优化方法及应用

针对传统相关积分优化方法,当系统扰动与调优变量相关时,在迭代优化的过程中,目标函数难以收敛到最优值的问题,提出了一种改进的相关积分优化方法用于稳态操作调优.基于数据驱动稳态模型,构造了自适应扰动估计器用来估计扰动均值,对最小二乘方法计算得到的调优变量梯度均值进行补偿,并修正调优变量,确保目标函数在调优的过程中收敛于最优值.仿真对比及工业应用结果证实了所提方法的可行性和有效性.     

Robust output tracking control of a class of non-minimum phase systems and application to VTOL aircraft

In this article, we study the output tracking control of a class of MIMO nonlinear non-minimum phase systems in the presence of input disturbances. In order to attenuate the effects of disturbances, the method of uncertainty and disturbance estimator (UDE) is extended to the controller design for non-minimum phase systems. Due to the fact that the accumulated disturbances is composed of internal states and external disturbances, a different stability analysis is given, and the overall closed-loop system is proved to be semi-globally stable. The proposed state-feedback controller not only forces system outputs to asymptotically track desired trajectories, but also drives the unstable internal dynamics to follow bounded and causal ideal internal dynamics (IID) solved via stable system centre (SSC) method. Simulation results demonstrate that the proposed controller achieves excellent tracking and disturbance rejection performance via the example of VTOL aircraft which has been the benchmark of nonlinear non-minimum phase systems.     

Algebraic estimation and active disturbance rejection in the control of flat systems

This paper proposes a feedback method for the control of uncertain systems with unknown external disturbances, which includes an algebraic estimator and relies on the Active Disturbance Rejection Control (ADRC) approach. The proposed estimator considers a generalized disturbance in order to deal with systems which may simultaneously present time varying parameters, external disturbances, un-modeled dynamics, and process noise. The on-line estimated disturbance is obtained by means of differential algebraic methods and it is used as the major part of an on-line feedback cancellation scheme aiming at linearization and uncertainty suppression. The algebraic estimator proposed in the paper makes unnecessary the use of classical extended state observers, which are widely used in ADRC. The speed of response and reliability of the proposed algebraic disturbance estimator-based control scheme was experimentally tested on three laboratory systems, including a system of directly-coupled DC motors, a roto-magnet system, and a disc and beam system, showing that the experimental results are in excellent agreement with the predictions of the theory.     

基于滤波器的浸入与不变自适应控制

针对机电伺服系统存在参数不确定、未建模动态及时变扰动这一问题,提出一种基于滤波器的浸入与不变自适应算法,该算法能够准确估计伺服系统中的未知参数.首先,构造系统状态及回归函数的滤波器,再根据滤波后的辅助变量构造参数估计器;然后,依据浸入与不变理论设计参数估计器中的辅助函数,从而保证参数估计误差的收敛性.此外,为了进一步降低集总扰动对系统闭环性能的影响,提出一种扰动观测器,这种扰动观测器结构简单,并且能保证估计误差的渐近稳定,从而有效地补偿系统中的未建模动态和外部扰动.最后,利用Lyapunov理论分别证明了参数估计器、扰动观测器及闭环系统的稳定性,仿真与实验结果验证了所提出的自适应方法及扰动观测器的有效性.     

Design of UDE‐based controllers from their two‐degree‐of‐freedom nature

The control algorithm based on the uncertainty and disturbance estimator (UDE) is a robust control strategy and has received wide attention in recent years. In this paper, the two‐degree‐of‐freedom nature of UDE‐based controllers is revealed. The set‐point tracking response is determined by the reference model, whereas the disturbance response and robustness are determined by the error feedback gain and the filter introduced to estimate the uncertainty and disturbances. It is also revealed that the error dynamics of the system is determined by two filters, of which one is determined by the error feedback gain and the other is determined by the filter introduced to estimate the uncertainty and disturbances. The design of these two filters are decoupled in the frequency domain. Moreover, after introducing the UDE‐based control, the Laplace transform can be applied to some time‐varying systems for analysis and design because all the time‐varying parts are lumped into a signal. It has been shown that, in addition to the known advantages over the time‐delay control, the UDE‐based control also brings better performance than the time‐delay control under the same conditions. Design examples and simulation results are given to demonstrate the findings. Copyright © 2010 John Wiley & Sons, Ltd.