给定阶控制器e1优化设计方法

研究了基于多输入多输出离散时间系统的给定阶控制器e1优化设计问题．基于Youla参数化法，对自由参数Q分为多项式和有理多项式两种情况进行了讨论，并将有理式的情形转化为多项式的形式，最后将给定阶的控制器优化问题归结为求解标准的线性规划问题．在给定控制器阶次的条件下，得到使闭环系统内部稳定并使相应的e1范数最小的最优控制器．最后给出了算例来验证算法的有效性．     

PID 控制器的频域特性与无模型参数调节

为了使PID参数调整不依赖于模型参数,而是直接基于闭环响应,首先分析PID参数对闭环系统性能的影响,然后以振荡最小、开环增益最大等为基本原则给出一种无模型PID参数调整方法。该方法只需要闭环响应曲线中的振荡频率信息,避免了模型参数辨识误差对调整结果的影响,简化了参数调节的过程。最后通过实验验证了所提出方法的有效性。     

一种具有极点在线优化的复合自适应控制系统

极点配置自适应控制,通常存在如何选择合理的闭环极点问题。本文在极点配置控制基础上,运用优化理论并基于跟踪误差指标最小的原则,建立起一种具有极点参数在线优化的复合自适应控制方法,使系统在参数自适应的同时,实现动态响应最优化。     

闭环连续系统仿真若干问题的分析研究

分析研究了对闭环系统的纯滞后环节如何处理的问题,找到开环系统和闭环系统的理论输出值计算方法,并比较3种Pade近似和全极点近似的精度。将此算法应用到闭环系统中,进一步比较4种近似,以求得闭环系统中误差最小的近似方法。同时提出在计算过程中对纯滞后处理的改进方法,使得误差大大减小,精度提高了一个数量级。研究结果表明,在开环系统和闭环系统,全极点近似由于没有引入零点,使得误差最小;改进方法使误差精度提高了一个数量级。     

基于神经元网和带死区的最小二乘算法的非线性离散时间系统的自适应控制

针对非线性离散时间系统,提出了一种用带死区的最小二乘算法去调节神经网参数的算法,同其他算法相比,这种算法具有非常高的收敛速度.对于这种自适应控制算法,证明了闭环系统的所有信号是有界的,跟踪误差收敛到以零为原点的球中.     

一种基于LPV系统的$H_infty$切换控制器设计方法

针对线性参数变化(LPV)系统提出一种切换控制器参数化设计方法.基于Youla参数化思想,将控制器设计过程分解为两个步骤.首先,设计一个中心控制器保证闭环系统的全局$H_\infty$性能;其次,将参数变化区域划分为若干个子区域,在每个子区域中将中心控制器进行线性分式变换,得到切换控制器自由参数的状态空间实现,将切换控制器转换为自由参数之间的切换.基于所提出的切换LPV控制器线性分式变换实现方法,不仅可以保证在任意切换的情况下子系统各自局部的$H_\infty$性能,而且可以保证整个闭环系统满足某一整体的$H_\infty$性能,并通过仿真结果验证了所提出方法的有效性.     

基于神经元网和带死区的最小二乘算法的非线性离散时间系统 …

针对非线性离散时间系统,提出了一种用带死区的最小二乘算法子调节神经网参数的算法,同时他算法相比,这种算法具有非常高的收敛速度,对于这种自适应控制算法,证明了闭环系统的所有信号是有界的,跟踪误差收敛到以零为原点的球中。     

基于l∞范数和l1范数最小化的二自由度最优鲁棒跟踪控制

研究了具有乘摄动模型不确定性并存在未知干扰系统的最优鲁棒跟踪控制问题,采用二自由度控制器结构Youla参数化方法将最优鲁棒跟踪控制问题转化为两个相互独立的优化问题,跟踪问题和鲁棒设计问题,跟踪问题以l∞范数为性能指标通过极小化跟踪误差的最大幅值实现最优跟踪控制;鲁棒性设计问题中,将模型不确定性视为一种外界干扰,通过极小化干扰到误差的灵敏度函数的l1范数使得干扰对跟踪误差的影响最小,通过截断处理,上述两种优化问题均可化为标准线性规划问题,给出了截断阶数与逼近误差之间的关系,仿真结果表明新方法的有效性。     

基于l_∞范数和l_1范数最小化的二自由度最优鲁棒跟踪控制

研究了具有乘摄动模型不确定性并存在未知干扰系统的最优鲁棒跟踪控制问题 .采用二自由度控制器结构Youla参数化方法将最优鲁棒跟踪控制问题转化为两个相互独立的优化问题 :跟踪问题和鲁棒设计问题 .跟踪问题以l∞ 范数为性能指标通过极小化跟踪误差的最大幅值实现最优跟踪控制 ;鲁棒性设计问题中 ,将模型不确定性视为一种外界干扰 ,通过极小化干扰到误差的灵敏度函数的l1范数使得干扰对跟踪误差的影响最小 .通过截断处理 ,上述两种优化问题均可化为标准线性规划问题 .给出了截断阶数与逼近误差之间的关系 .仿真结果表明新方法的有效性 .     

H∞控制中Youla稳定补偿器参数的新算法

为使H∞控制频域方法更加便于应用,对H∞控制中Youla稳定补偿器的设计方法进行研究,基于传递函数矩阵互质分解和多项式矩阵最大公因子理论.提出Youla补偿器参数的两个新算法;在此基础上运用Smith意义上等价的概念,以定理形式提出另一个更便于计算机运算的算法;另外,基于规范化互质分解理论提出求解Youla补偿器参数的第四个定理.与传统的Youla补偿器算法相比,这些新算法具有过程简单、计算量小和便于工程应用等优点.     

控制能量约束条件下时滞系统最优控制与仿真

文中对时滞系统在控制能量存在约束时的最优控制问题进行了探讨.由控制器Youla参数化得到控制系统的敏感度函数和控制敏感度函数用来表示一个包含跟踪误差和控制能量在内的积分平方性能指标,针对不同类型的时滞过程运用谱分解的方法最小化该性能指标,从而导出相应控制器的设计方法,可使系统在控制能量约束条件下具有最优的控制性能.仿真研究进一步说明了所给设计方法的有效性.     

具有参数不确定性的非线性系统的鲁棒输出跟踪

研究具有非线性参数化的非线性系统的输出跟踪问题.采用时变状态反馈控制律, 指数镇定输出跟踪误差,并保证非线性系统的所有状态是有界的.为了实现时变状态反馈控 制律,设计高增益鲁棒观测器观测构造该控制律所需要的状态,使得整个闭环系统的输出能 渐近跟踪期望输出,且该闭环系统中所有信号都是有界的.     

互质因子摄动下的最优鲁棒稳态跟踪控制器设计

研究了存在未知外部干扰时的互质因子摄动系统的鲁棒稳态追踪问题.利用Youla参数化方法，提出了一个最坏情况稳态绝对误差的精确计算公式.该公式使得最优稳态跟踪控制器设计问题等价于保证系统鲁棒稳定时e1的最优鲁棒控制器设计问题.     

Parameterizing robust manipulator controllers under approximate inverse dynamics: A double‐Youla approach

We consider the goal of ensuring robust stability when a given manipulator feedback control law is modified online, for example, to safely improve the performance by a learning module. To this end, the factorization approach is applied to both the plant and controller models to characterize robustly stabilizing controllers for rigid‐body manipulators under approximate inverse dynamics control. Outer‐loop controllers to stabilize the nonlinear uncertain loop that results from approximate inverse dynamics are often derived by lumping uncertainty in a single term and subsequent analysis of the error system. Here, by contrast, the well‐known norm bounds of these uncertain dynamics are first recast into a generalized plant configuration that preserves the characteristic uncertainty structure. Then, the overall loop uncertainty is expressed with respect to the nominal outer‐loop feedback controller by means of an uncertain dual‐Youla operator. Therefore, using the dual‐Youla parameterization, we provide a novel way to rigorously quantify permissible perturbations of robot manipulator feedforward/feedback controllers. The method proposed in this paper does not constitute another robust control law for rigid‐body manipulators, but rather a characterization of a set of robustly stabilizing controllers. The resulting double‐Youla parameterization for the control of robot manipulators is amenable to numerous advanced design methods. The result is thoroughly discussed by a planar elbow manipulator and exemplified with a six‐degree‐of‐freedom robot scenario with varying payload.     

时变不确定性系统的二自由度最优鲁棒稳态跟踪控制器设计

研究了被控系统存在范数有界的时变模型摄动和未知外部干扰时鲁棒稳态跟踪问题. 利用二自由度控制结构和Youla参数化方法.提出了一个最坏情况稳态绝对误差的精确计算公 式,利用该公式最优稳态跟踪控制器设计问题可化为一个有限维l1优化问题.因此控制器设计 只需解一个标准线性规划问题.此外,还证明了所提出的控制器可同时保证系统的鲁棒稳定性 和最优跟踪性能.仿真结果表明了该方法的有效性和可行性.     

Quaternion‐based visual servo control in the presence of camera calibration error

Visual servo control systems use information from images along with knowledge of the optic parameters (i.e. camera calibration) to position the camera relative to some viewed object. If there are inaccuracies in the camera calibration, then performance degradation and potentially unpredictable response from the visual servo control system may occur. Motivated by the desire to incorporate robustness to the camera calibration, different control methods have been developed. Previous adaptive/robust controllers (especially for six degree‐of‐freedom camera motion) rely heavily on properties of the rotation parameterization to formulate state estimates and a measurable closed‐loop error system. All of these results are based on the singular axis–angle parameterization. Motivated by the desire to express the rotation by a non‐singular parameterization, efforts in this paper address the question: Can state estimates and a measurable closed‐loop error system be crafted in terms of the quaternion parameterization when the camera calibration parameters are unknown? To answer this question, a contribution of this paper is the development of a robust controller and closed‐loop error system based on a new quaternion‐based estimate of the rotation error. A Lyapunov‐based analysis is provided which indicates that the controller yields asymptotic regulation of the rotation and translation error signals given a sufficient approximate of the camera calibration parameters. Simulation results are provided that illustrate the performance of the controller for a range of calibration uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.     

Decentralized stable factors and a parameterization ofdecentralized controllers

Decentralized stable factors (DSF) are used to extend the stable factorization approach to the design of decentralized controllers. The DSF allow a parameterization of all the stabilizing decentralized controllers in the form of a Youla parameterization. A decentralized controller serves as the central controller, and the Youla parameter has a particular structure and must satisfy an interaction constraint. This parameterization encompasses several other parameterization results on decentralized control systems     

A two‐degree‐of‐freedom ℋ︁∞ control design method for robust model matching

We propose an ??_∞ controller design method which achieves a closed‐loop transfer function equal or otherwise sensibly close to a desired transfer function, viz. a model reference design. The proposed controller design method inherits the model reference feature of the internal model control design method and incorporates the weighting scheme of the ??_∞ loop‐shaping. It utilizes Youla–Kucera parameterization in a two‐degree‐of‐freedom scheme to achieve robust model reference and high performance design while ensuring a sensible robust stability margin, and can be readily applied to the generic class of LTI systems (SISO, MIMO, stable, unstable). Copyright © 2006 John Wiley & Sons, Ltd.     

Limitations in tracking systems

In this paper, we obtain information theoretical conditions for tracking in linear time-invariant control systems. We consider the particular case where the closed loop contains a channel in the feedback loop. The mutual information rate between the feedback signal and the reference input signal is used to quantify information about the reference signal that is available for feedback. This mutual information rate must be maximized in order to improve the tracking performance. The mutual information is shown to be upper bounded by a quantity that depends on the unstable eigenvalues of the plant and on the channel capacity. If the channel capacity reaches a lower limit, the feedback signal becomes completely uncorrelated with the reference signal, rendering feedback useless. We also find a lower bound on the expected squared tracking error in terms of the entropy of a random reference signal.We show a misleading case where the mutual information rate does not predict the expected effect of nonminimum phase zeros. However, mutual information rate helps generalize the concept that there is a tradeoff when tracking and disturbance rejection are simultaneous goals, and a constraint communication channel is present in the feedback loop. Examples and simulations are provided to demonstrate some of the results.     

基于神经网络的一类非线性系统自适应跟踪控制

提出一种非线性系统的自适应神经跟踪控制方案。通过利用RBF神经网络对未知非线性系统建模,并用一个滑模控制项消除网络建模误差和外部干扰的影响,从而能够保证闭环系统的全局稳定性和输出跟踪误差渐近收敛于零。