九点五态控制器作用下迟延系统的稳定性分析

九点五态控制器是根据偏差和偏差变化率，在相平面上采用不同的作用力以达到控制要求的控制器。该文介绍了九点五态控制器分别在0型系统和Ⅰ型系统的迟延系统作用下稳定性分析，并且比较了在相同对象下施加不同的作用力和在不同对象下施加同样的作用力下的控制效果。从而得出：对于任意的带延迟的二阶对象在任意小的允许误差要求下，总能找到满足基本运行条件的一组作用力使控制系统满足允许误差要求，达到系统稳定。     

二十七点九态控制器及跟踪仿真分析

二十七点九态控制器是根据偏差、偏差的变化和偏差变化的变化所构成的相空间上采用不同的控制作用以达到满足控制要求的控制器.分析此控制器的九种不同的作用力在相空间和响应曲线的一一对应关系,通过作用力来研究响应曲线,进而揭示出三阶及高阶对象难以控制的原因;通过不同的三阶对象分别在单位阶跃、正弦和斜波下的跟踪仿真,可得出结论：在此控制器作用下对于不同的三阶对象（含不稳定的）,可以达到满意的控制效果.     

基于九点控制器的斜坡跟踪

九点控制器是根据偏差和偏差变化的大小在相平面上采用不同的作用力以达到控制要求的控制器,是一种新型的智能控制器。该文介绍了在九点控制器作用下的斜坡跟踪原理。借助于受控相平面,详细分析了控制作用力参数Ki(i=0,±0,±2,±3,±4)对斜坡跟踪稳定性能的影响,并给出了如何调整各个控制作用力参数以达到斜坡跟踪性能的要求。在二阶系统下,与PID控制器相比,九点控制器对性能指标进行了分解,同时能克服PID控制器参数难以整定的缺陷,具有优良的控制效果。     

九点控制闭环特性的研究与仿真

九点控制器是根据偏差和偏差变化的大小在相平面上采用不同的作用力使系统达到控制要求的控制器。本文首先在相平面上对控制力K0的作用范围进行了调整,用以消除稳态误差;其次,分析了九点控制系统的稳定性和抗干扰性能与九个作用力的关系;最后,仿真比较了单位阶跃输入时九点控制与闭环PID控制系统的抗干扰能力,仿真结果表明该控制器不仅能使开环不稳定系统稳定而且具有良好的抗干扰能力,验证了理论分析的有效性。     

柔性控制系统中七态控制器的仿真分析

基于智能控制的思想,在九点控制器的基础上,提出了七态控制器的概念.在控制器作用下对柔性控制系统中的二阶对象进行仿真分析,可得出结论:无论二阶对象是否稳定,七态控制器对不同的控制对象可以达到满意的控制效果.     

九点控制器作用下的正弦跟踪仿真分析

九点控制器是根据偏差和偏差变化的大小在相平面上采用不同的作用力以达到控制要求的控制器。该文介绍在九点控制器作用下的正弦跟踪 ,借助于相平面工具详细地分析控制作用力参数Ki(i=0 ,± 1,± 2 ,± 3 ,± 4)对正弦跟踪稳定性能的影响 ,并给出了如何调整控制作用力参数以达到正弦跟踪性能的要求。     

超低空空投货物出舱过程的动态逆鲁棒控制

针对运输机在执行超低空空投任务时,地面效应的作用使飞机的气动特性发生改变,货物向舱门的移动过程使飞机的受力和力矩发生变化,货物出舱过程和地面效应的作用使飞机姿态和轨迹都大幅度偏离空投前的稳定飞行状态,如果控制失当,极易使飞机振荡发散,导致飞行事故的问题,设计一控制器保证超低空空投货物出舱过程中运输机的稳定性和安全性.由于被控对象是一个多变量耦合且参数变化的非线性系统,提出了一种基于动态逆理论和鲁棒控制理论的解决方案,控制器内环采用动态逆方法使系统解耦线性化,外环采用H∞鲁棒控制方法解决不确定性问题.仿真结果表明,该控制器能够很好地保持飞行速度和轨迹、稳定飞机姿态,具有良好的控制效果,且对于系统不确定性具有较强的鲁棒性.     

基于混沌小扰动抑制的电力系统混沌振荡控制方法研究

针对电力系统在周期扰动下可能发生混沌振荡,影响电力系统稳定运行的问题,提出基于混沌小扰动抑制的电力系统混沌振荡控制方法。构建电力系统数学模型,模拟电力系统混沌振荡过程,确定扰动与混沌振荡之间的关系。根据电力系统实时运行数据的采集结果,计算混沌振荡信号特征参数,判定系统振荡状态,计算混沌扰动抑制控制量。在混沌振荡控制器的支持下,利用混沌小扰动抑制技术限制混沌振荡幅值,进而实现电力系统的混沌振荡控制任务。从实验结果中可以看出,与传统控制方法相比,优化设计方法控制作用下,电力系统的混沌振荡信号的幅值和频率更低,振荡幅值和频率的控制误差分别降低了约1.57db和0.015Hz,即优化设计方法的控制性能更优。     

带有误差修正项的自适应学习控制

本文讨论了二阶系统在任意初态偏差下的自适应控制问题,借助学习控制及其初始修正的思想,提出了两种带有修正初态偏差功能的自适应控制策略:一阶吸引子控制器和零阶吸引子控制器.两种控制器都是将整个控制过程分成若干个等长时间的子过程,在每个子过程中控制算法都会进行误差校正和参数学习.其中,一阶吸引子控制器在每个子过程中同时修正所有状态偏差;而零阶吸引子控制器在每个子过程中先修正高阶状态偏差,再修正低阶状态偏差.并且两种控制器在控制过程中,都利用反正切函数对控制量进行连续化处理,解决了控制过程中的颤振问题.最后,通过计算机仿真验证了算法的有效性.     

二阶动态滑模控制在磁悬浮控制系统中的应用

磁悬浮是典型的非线性、不稳定系统.为了实现其控制,本文采用了一种结合二阶滑模和动态滑模两种设计思想的二阶动态滑模控制器的设计方法,首先推导了磁悬浮系统的线性化状态空间模型;然后针对被控对象设计了二阶动态滑模控制器;最后与普通滑模控制器进行对比仿真试验.仿真结果表明,所设计的二阶动态滑模控制器在使小球快速稳定到达平衡点的同时,大大削弱了普通滑模控制器中存在的严重抖振现象.     

一维模糊PID控制器的钝性稳定性分析

本文研究新近提出的模糊PID控制器的稳定性问题．由于该模糊PID控制器的模 糊推理部分具有非线性特性，因此本文采用钝性定理来分析它的稳定性．本文给出分析稳定 性的方法，并针对一阶和二阶对象的一般形式，给出了保证系统稳定的参数范围,用仿真验 证了所得结果．高阶对象也可以使用圆判据求取相应结果．     

An approach for the robustness comparison between piecewise linear PID -like fuzzy and classical PID controllers

The comparison of the stability robustness between the classical PID controller and two piecewise linear PID-like fuzzy controllers to the variations of the parameters in the second order plant is provided in this paper. The definition of a stability robust controller (to the parameter variations of the plant model) is presented. Then Kharitonov’s theorem is applied to find the regions of robustness to the parameter variations for the control systems with different controllers. Based on the size of regions of robustness, the relative robustness factor is defined, and the robustness comparison is provided. For every classical PID controller with gain coefficients determined and fixed, it is shown that we can always design the piecewise linear PID-like fuzzy controllers to be more robust than the specific classical PID controller. The results of robustness comparison is further confirmed in the simulation included for the second order uncertain plant.     

具有有界输入的网络控制系统时延独立稳定性

基于有界时延、无数据包丢失、传感器时钟驱动、执行器事件驱动,文章提出了用一个特定的实值函数来调节动态输出反馈控制器的输出以确保控制对象输入有界的方法,建立了具有分布时延、有界输入、动态输出反馈网络控制系统的非线性数学模型.用李雅普诺夫第二方法和线性矩阵不等式描述分析了系统的渐近稳定性,并推导出与时延无关的系统渐近稳定的充分条件.最后MATLAB仿真算例说明：稳定判据是可行的,有界输入的控制方法是有效的.     

Control reconfiguration after actuator failures by Markov parameter matching

Two novel linear control reconfiguration methods for plants subject to actuator failures are described. The common idea is to place a reconfiguration block between the faulty plant and the nominal controller in order to re-route the signals around the broken actuator. The first method uses a computationally simple static reconfiguration block. It recovers the nominal plant input/output-behaviour by assigning the faulty plant the same Markov parameters as the faultless plant. The second method concerns the design of the feedforward part in the virtual actuator using the idea and results of the first approach. The virtual actuator is a dynamical reconfiguration block. Existence conditions and solution algorithms are provided, and it is shown that both approaches guarantee the closed-loop stability if the existence conditions are met. An experimental study demonstrates the practical usability of the proposed approaches.     

Robust stability region of fractional order PIλ controller for fractional order interval plant

In this article, by using the fractional order PI^λ controller, we propose a simple and effective method to compute the robust stability region for the fractional order linear time-invariant plant with interval type uncertainties in both fractional orders and relevant coefficients. The presented method is based on decomposing the fractional order interval plant into several vertex plants using the lower and upper bounds of the fractional orders and relevant coefficients and then constructing the characteristic quasi-polynomial of each vertex plant, in which the value set of vertex characteristic quasi-polynomial in the complex plane is a polygon. The D-decomposition method is used to characterise the stability boundaries of each vertex characteristic quasi-polynomial in the space of controller parameters, which can obtain the stability region by varying λ orders in the range (0,?2). These regions of each vertex plant are computed by using three stability boundaries: real root boundary (RRB), complex root boundary (CRB) and infinite root boundary (IRB). The method gives the explicit formulae corresponding to these boundaries in terms of fractional order PI^λ controller parameters. Thus, the robust stability region for fractional order interval plant can be obtained by intersecting stability region of each vertex plant. The robustness of stability region is tested by the value set approach and zero exclusion principle. Our presented technique does not require sweeping over the parameters and also does not need linear programming to solve a set of inequalities. It also offers several advantages over existing results obtained in this direction. The method in this article is useful for analysing and designing the fractional order PI^λ controller for the fractional order interval plant. An example is given to illustrate this method.     

Smooth indirect adaptive sliding mode control

In this paper, an indirect approach to the dual‐mode adaptive robust controller (DMARC) is proposed, which combines the typical transient and robustness properties of variable structure systems with a smooth control signal in steady state, typical of conventional adaptive controllers, as model reference adaptive controller. The aim of this indirect version, here named indirect DMARC, is to provide a more intuitive controller design, based on physical plant parameters, as resistances, inertia moments, capacitances, and so on, maintaining DMARC properties. In this paper, a stability analysis for the proposed controller and simulations to an unstable second‐order plant will be presented. Copyright © 2013 John Wiley & Sons, Ltd.     

A perceptron network for functional identification and control ofnonlinear systems

Tracking control of a general class of nonlinear systems using a perceptron neural network (PNN) is presented. The basic structure of the PNN and its training law are first derived. A novel discrete-time control strategy is introduced that employs the PNN for direct online estimation of the required feedforward control input. The developed controller can be applied to both discrete- and continuous-time plants. Unlike most of the existing direct adaptive or learning schemes, the nonlinear plant is not assumed to be feedback linearizable. The stability of the neural controller under ideal conditions and its robust stability to inexact modeling information are rigorously analyzed.     

Robust controller design based on reduced order plants

Two dual controller design methods are proposed for linear, time-invariant, multi-input multi-output systems, where designs based on a reduced order plant robustly stabilizer higher order plants with additional poles or zeros in the stable region. The additional poles (or zeros) are considered as multiplicative perturbations of the reduced plant. The methods are tailored towards closed-loop stability and performance and they yield estimates for the stability robustness and performance of the final design. They can be considered as formalizations of two classical heuristic model reduction techniques. One method neglects a plant-pole sufficiently far to the left of dominant poles and the other cancels a sufficiently small stable plant-zero with a pole at the origin.     

A new approach to adaptive control: no nonlinearities

In adaptive control the goal is to design a controller to control an uncertain system whose parameters may be changing with time. Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability. However, a typical adaptive controller does not track time-varying parameters very well, and it is often highly nonlinear, which can result in undesirable behaviour, such as large transients or a large control signal. Furthermore, most adaptive controllers provide only asymptotic tracking, with no ability to design for a pre-specified settling time.Here we propose an alternative approach, which yields a linear periodic controller. Rather than estimating the plant or compensator parameters, instead we estimate what the control signal would be if the plant parameters were known. In this paper we argue the utility of this approach and then examine the first order case in detail, including a simulation. We also explore the benefits and limitations of the approach.