基于Kp=LDU分解的多变量离散时间系统鲁棒模型参考自适应控制

针对具有未建模动态且相对阶大于1的一类多变量离散时间系统,利用其高频增益矩阵分解建立新的参数模型,在较弱假设下,进一步研究了直接型鲁棒模型参考自适应控制问题.由离散时间系统的交换引理,建立了闭环系统的所有信号与规范化信号的联系.以一种系统化的方法,严格地分析了闭环系统的稳定性与鲁棒性.     

离散时间直接型模型参考自适应控制

本文针对一类离散时间系统, 研究了具有规范化自适应律的直接型模型参考自适应控制 (MRAC). 我们重新证明了离散时间系统的输入与输出间的 Lp 范数与 L2δ 范数关系特性和离散时间的交换引理 1 与引理 2. 并建立了离散时间自适应律的性质, 定义了规范化信号, 把闭环系统中的所有信号与其建立联系. 从而, 正如连续时间系统一样, 以一种系统化的方法严格分析了离散时间 MRAC 方案的稳定性与收敛性.     

间接型的混杂模型参考自适应控制

针对相对阶为1的理想系统,本文考虑了具有混杂自适应律的间接型模型参考自适应控制问题.通过建立系统和控制器的离散参数估计和它们的插值四者之间关系的性质,严格地分析了闭环系统的稳定性,证明了闭环系统中所有的信号都一致有界,并且跟踪误差渐进收敛于零.     

数字闭环石英挠性加速度计表头离散化研究

数字闭环加速度计可以解决传统模拟加速度计在输出电流到数字量(I/D)转换中带来的精度损失问题,而为实现数字闭环需将加速度计表头进行离散化.针对这一问题提出了一种表头离散化方法,将石英摆片、采样开关与零阶保持器整体离散化.建立数字闭环系统离散域数学模型,基于此模型进行仿真研究,仿真表明:此离散化方法可行,系统阶跃响应超调在10％左右,闭环带宽达到300 Hz以上.将其应用在数字闭环加速度计的实验系统,验证了仿真结果,为数字闭环加速度计的深入研究提供必要的理论指导和实验基础.     

非线性采样系统指数稳定的新条件

研究了非线性采样系统的稳定性问题. 对以采样周期为参数的离散时间系统族, 证明了全局指数稳定的Lyapunov定理和逆定理. 分别基于系统的一般近似模型和Euler近似模型, 给出了闭环系统全局指数稳定的新条件. 与现有结果相比, 取消了Lyapunov函数全局Lipschitz连续的假设, 减弱了闭环系统全局指数稳定的充分条件.     

数字DC/DC开关电源环路补偿器设计

建立了数字控制DC/DC开关电源闭环系统的s域小信号模型,采用数字重设计法针对给定的系统参数设计了数字补偿器。应用SISO Design Tool仿真平台,在伯德图分析和根轨迹法的基础上设计了连续域的模拟补偿器,并进行了离散化处理。在建立系统s域模型时引入了模数转换器和数字脉宽调制发生器产生的延迟效应,使补偿器的设计考虑了采样速率对系统的影响,改善了传统离散设计的误差。基于数字重设计法构建的数字补偿器实现了对脉宽调制信号的可编程精确控制,保证了变换器闭环工作良好的动态特性。仿真实验结果验证了所设计的数字补偿器的性能。     

直升机系统离散模糊建模与姿态稳定控制

关于直升机姿态稳定性控制问题,由于直升机系统具有非线性,外部环境多变的干扰影响姿态稳定性.传统的建模与控制方法,面临构造Lyapunov函数难、控制稳定性难度大的问题.为解决上述问题,提出直升机系统离散模糊建模与姿态稳定控制方法.根据直升机系统姿态动力学模型,建立其连续、离散时间模糊模型.采用线性矩阵不等式(LMI)方法,提出离散模糊状态反馈控制方法.所得控制率,不仅使闭环系统渐进稳定且能够获得较好的控制性能.改进方法具有响应速度快、过度过程时间短、无超调、能有效抑制外部干扰等优点,表明了改进方法的有效性.     

基于混合控制的闭环MEMS陀螺建模与仿真

论述了MEMS陀螺力平衡式闭环检测的基本原理,将∑-△调制器用于MEMS陀螺的闭环检测,设计了基于∑-△调制器的离散信号和基于比例积分与测速反馈的混合闭环检测反馈网络。建立了MEMS陀螺仪闭环检测系统的模型,并进行了仿真验证,本文设计的闭环MEMS陀螺相对于开环检测方式,电路噪声造成的误差减小了67．7％,零偏稳定性提高了3倍。仿真结果验证了所设计的混合闭环检测方案的有效性。     

离散事件系统的学习控制初探

本文建立了包含状态持续时间的离散事件系统的自动机模型，然后引入学习算法用以确定描述闭环系统进行了语言K,从而当控制指标是以时间形式给出时，仍能利用监控理论来设计散事件系统。     

电容式微机械陀螺双环路闭环驱动电路研究

为了提高微机械陀螺系统的检测灵敏度,对微机械陀螺系统的驱动电路进行了研究.分析了微陀螺闭环驱动系统理论,基于此提出一种双环路闭环驱动方法,并且利用数学工具simulink建立系统模型,验证此方法的可行性,最后设计完成相应电路.此方法引入锁相环实现闭环驱动电路的稳频控制;采用自动增益控制器(AGC)实现恒幅控制.利用Hspice完成电路级仿真.结果表明,微机械陀螺双环路闭环驱动电路建立稳定振荡的时间为45 ms,稳定振荡频率为2.7553 KHz,频率偏差为0.1 z,频率抖动为0.056563 Hz.相对于传统的AGC闭环驱动电路,此闭环驱动电路建立稳定振荡时间缩短了30.77%,频率稳定性是传统AGC闭环驱动电路的32.72%.微机械陀螺环路闭环驱动电路提高驱动信号性能,对于微机械陀螺检测灵敏度的提高有着重要意义.     

Discrete-time modeling of sampled-data control systems with directfeedthrough

Discrete-time models of sampled-data control systems are addressed when both a continuous-time plant and a discrete-time controller have a feedthrough. It is pointed out that in this case discrete-time models which can be found in most references should not be used in the closed-loop context. A new state-space model appropriate for the closed-loop modeling and formulas for calculating the related discrete-time pulse transfer functions are derived. Intersample phenomena are studied and the feasibility of that model to describe systems with parasitic dynamics is emphasized. Examples from the literature illustrate the relevance of the issue     

Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

A novel model, termed the standard neural network model (SNNM), is advanced to describe some delayed (or non-delayed) discrete-time intelligent systems com- posed of neural networks and Takagi and Sugeno (T-S) fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based (or fuzzy) discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based (or fuzzy) discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.     

Robust adaptive NN control for a class of uncertain discrete-time nonlinear MIMO systems

A robust adaptive NN output feedback control is proposed to control a class of uncertain discrete-time nonlinear multi-input–multi-output (MIMO) systems. The high-order neural networks are utilized to approximate the unknown nonlinear functions in the systems. Compared with the previous research for discrete-time MIMO systems, robustness of the proposed adaptive algorithm is obvious improved. Using Lyapunov stability theorem, the results show all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking errors converge to a small neighborhood of zero by choosing the design parameters appropriately.     

A repetitive controller for discrete-time passive systems

This work proposes and studies a new repetitive controller for discrete-time systems which are required to track or to attenuate periodic signals. The main characteristic of the proposed controller is its passivity. This fact implies closed-loop stable behavior when it is used with discrete-time passive plants. The work also discusses the energetic structure, the frequency response and the time response of the proposed controller structure. A numerical example is included to illustrate its practical use.     

A Passive Repetitive Controller for Discrete-Time Finite-Frequency Positive-Real Systems

This work proposes and studies a new internal model for discrete-time passive or finite-frequency positive-real systems which can be used in repetitive control designs to track or to attenuate periodic signals. The main characteristic of the proposed internal model is its passivity. This property implies closed-loop stability when it is used with discrete-time passive plants, as well as the broader class of discrete-time finite-frequency positive real plants. This work discusses the internal model energy function and its frequency response. A design procedure for repetitive controllers based on the proposed internal model is also presented. Two numerical examples are included.     

不确定离散广义系统的D稳定鲁棒控制

研究了具有圆盘区域极点约束的一类不确定离散广义系统的鲁棒控制问题.首先,研 究了控制输入项不含扰动的不确定离散广义系统,提出了广义二次D镇定的概念,基于矩阵不 等式和广义Riccati方程,给出了一种广义二次D镇定器的设计方法,所得到的结论能够实现研 究目标;然后,讨论了控制输入项含有扰动的不确定离散广义系统,在一定的假设条件下,给出 了期望状态反馈增益阵的存在条件及其解析表达式.最后,用数值示例说明所给方法的有效性 及可行性.     

Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems

In this paper, a direct adaptive neural network control algorithm based on the backstepping technique is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. The neural networks are utilized to approximate unknown functions, and a stable adaptive neural network controller is synthesized. The fact that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded is proven and the tracking error can converge to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the previous research for discrete-time systems, the proposed algorithm improves the robustness of the systems. A simulation example is employed to illustrate the effectiveness of the proposed algorithm.     

Adaptive control of a class of discrete-time MIMO nonlinear systems with uncertain couplings

In this article, adaptive control is investigated for a class of discrete-time multi-input-multi-output nonlinear systems in block-triangular form with uncertain couplings of delayed states among subsystems. Future states prediction is carried out to facilitate adaptive control design and auxiliary outputs are introduced to develop a novel compensation mechanism for the uncertain nonlinear couplings. By using Lyapunov method and ordering signals growth rate, it is rigorously proved that all the signals in the whole closed-loop systems are globally bounded and the output tracking errors asymptotically converge to zeros. The effectiveness of the proposed control is demonstrated in the simulation study.     

Non-fragile state feedback H-infinity control for discrete-time systems with quantized signals

This paper presents a study on the problem of non-fragile state feedback H-infinity controller design for linear discrete-time systems with quantized signals. The quantizers considered here are dynamic and time-varying. With the consideration of controller gain variations and quantized signals at the same time, a new non-fragile H-infinity control strategy is proposed with updating quantizer＇s parameters, such that the quantized closed-loop system is asymptotically stable and with a prescribed H-infinity performance bound. An example is presented to illustrate the effectiveness of the proposed control strategy.