卫星天线跟踪指向控制系统的全系数自适应控制

本文根据全系数自动适应控制的原理，进行了卫星天线跟踪指向控制系统的设计和数学仿真研究，论文尝试将单变量全系数自适应控制向多变量全系数自适应控制进行推广，并在多变量系统中取得了满意的解耦控制效果，最后，进一步探讨了所设计的控制器在工程应用中可能产生的问题，并给出了满意工程要求的卫星天线跟踪指向控制系统数学仿真结果。     

基于PROFIBUS-DP总线的PLC控制系统实时性能研究

以自动化工程中常用的PLC和PROFIBUS组成的单主站控制系统为研究背景,针对单主站系统信息处理的特点,建立了信息处理模型,从理论上分析了令牌目的循环时间对系统测控周期的影响,研究了单主站控制系统的响应时间.     

磁悬浮控制系统动态特性研究

悬浮控制系统是磁悬浮列车的一个重要的子系统.在对悬浮系统动态性能的测试中发现,随着控制中比例系数的增加,系统超调量减小,该现象与教科书中所讲述的一般控制系统特性正好相反.文中以单电磁铁悬浮控制系统为研究对象,以教科书中典型系统参数为中间变量,层层深入地剖析了这一现象本质.采用理论分析与数值计算相结合的方法,得到闭环系统增益始终大于1以及积分器的存在,是系统出现随比例系数的增加超调量减小这一现象的根本原因.     

鲁棒极点配置控制系统的一种设计方法

本文提出了系统特征系数灵敏度的概念,导出了特征系数灵敏度与特征值灵敏度闻的关 系.在此基础上,提出了一种以加权特征系数灵敏度为性能指标,结构化小参数摄动下的鲁 棒极点配置控制系统的设计方法.特征系数灵敏度计算简单,目标函数的极小化采用标准的 具有二次收敛性质的参数最优化方法.     

前馈控制的工程实现

本文讨论了工程中常见的单回路和多回路前馈控制的工程实现,并把控制对象分为有自 平衡和无自平衡两大类,把每大类的传递函数化成标准表达式,用低阶近似方法求出各种前 馈控制及其参数的计算公式. 上述结果亦可推广到多变量控制系统.     

空调蒸发器的自适应预测控制策略

针对空调蒸发器运行状态,提出一种基于模糊神经模型的自适应单神经元预测控制器,该控制器具有结构简单、易于操作、控制器参数可在线调节的特点.离线建立空调蒸发器的模糊神经模型,再利用模型的梯度信息在线调节单神经元控制器参数,使控制系统较快地趋于稳定.仿真结果表明,提出的自适应单神经元预测控制器具有较好的动态性能和稳态性能,并能够成功地应用到空调蒸发器的控制中.     

基于支持向量机的烟气二氧化硫排放量预测模型

针对循环流化床锅炉控制系统的烟气SO₂对象的非线性特点,本文建立了一种基于支持向量机的烟气SO₂排放量预测模型. 由于直接网格搜索确定支持向量机回归模型参数的方法计算量大、搜索时间长,本文采用单变量参数搜索结合网格寻优的方法来确定模型参数. 仿真结果表明,基于支持向量机方法建立的循环流化床锅炉烟气SO₂排放量预测模型具有良好的预测效果.     

不确定时滞分布参数系统鲁棒控制的LMI方法

对常时滞、变时滞的不确定分布参数控制系统,提出了一种与现有的研究分布参数控制系统不同的鲁棒控制方法.该方法通过构造平均Lyapunov函数,利用线性矩阵不等式知识,在只要求系统本身所固有的系数是负定矩阵的条件下,给出了所给的分布参数系统镇定的充分条件.当模型中的时滞为常时滞时,所得的充分条件与时滞无关.当模型中的时滞为变时滞时,所得模型的镇定准则依赖于时滞.此外,该方法与已有方法比较的一个显著优点就是所获得的条件容易检验,因而易于应用.最后举了一个实例以说明该方法的有效性.     

基于数据融合的加速度传感器的静态模型辨识

建立加速度计静态模型方程并根据工程应用对方程进行简化,针对加速度计静态模型的参数辨识提出一种新的方法--数据融合,使模型参数的精度有明显的提高.该方法的提出为控制系统的实时补偿提供了更好的条件.     

基于LFT的鲁棒飞行控制系统设计与仿真研究

针对飞机在大飞行包线飞行时,模型的参数会受高度和马赫数等发生变化.通常设计基于变化参数的飞机模型,模型阶次较高,导致设计的鲁棒控制系统阶次很高,难于工程实现.因此,设计具有低阶的鲁棒控制系统具有霞要的意义.提出了一种利用线性分式变换(LFT)建立参数不确定性模型和μ综合相结合设计低阶的鲁棒飞行控制系统的方法.通过飞机在不同状态下,选取m个状态点处的数据,进行最小二乘曲线拟合,利用莫顿方法建立参数不确定性飞机的LFT模型.用μ综合的方法对所建立的横侧向的LFT模型进行鲁棒飞行控制系统设计和仿真研究.仿真结果表明,该方法对大飞行包线的低阶的鲁棒飞行控制系统设计是有效和可行的.     

Contribution to the indirect decentralized adaptive control of manipulation robots

In this paper, efficient approaches to the synthesis of indirect decentralized adaptive control for manipulation robots are presented. The first part of control synthesis consists of the estimation of unknown dynamic robot parameters using the methods of recursive identification and fast dynamic as well as identification models in a symbolic form. The second part of synthesis includes the self-tuning control strategy which is a basis for adaptive control synthesis according to the estimates of the unknown dynamic parameters. Using the theory of decentralized systems, a new robust algorithm for adaptive control with the ability of adaptation in the feedforward or feedback loop are proposed. A complete stability and convergence analysis is presented. A special part of the paper represents an analysis of practical implementation of the proposed control algorithms on modern microprocessor-based robot controllers. Based on this analysis, an efficient application of indirect adaptive algorithms in real time with high-quality system performance is shown. Adaptive algorithms are verified through simulation of trajectory tracking for an industrial robot with unknown dynamic parameters of payload.     

Nonlinear adaptive control of robotic manipulators - hyperstability approach

A nonlinear model reference adaptive controller based on hyperstability approach, is presented for the control of robot manipulators. Use of hyperstability approach simplifies the stability proof of the adaptive system. The unknown parameters of the system, as well as its variable payload, are estimated on line and are adaptive to their actual values; tending to reduce the system error. In addition, any sudden change in the system parameters or payload is detected by the proposed intelligent controller. Robot path tracking, with unknown parameter values and variable payload, is simulated to show the effectiveness of the proposed adaptive control algorithm. Both system output error and parameter estimation error vanish under the proposed parameter adaptation algorithm.     

Adaptive sampled-data based linear quadratic optimal control of stochastic systems

The problem of sampled-data (SD) based adaptive linear quadratic (LQ) optimal control is considered for linear stochastic continuous-time systems with unknown parameters and disturbances. To overcome the difficulties caused by the unknown parameters and incompleteness of the state information, and to probe into the influence of sample size on system performance, a cost-biased parameter estimator and an adaptive control design method are presented. Under the assumption that the unknown parameter belongs to a known finite set, some sufficient conditions ensuring the convergence of the parameter estimate are obtained. It is shown that when the sample step size is small, the SD-based adaptive control is LQ optimal for the corresponding discretized system, and sub-optimal compared with that of the case where the parameter is known and the information is complete.     

Adaptive stabilization of a class of nonlinear systems with unstable internal dynamics

This note considers global stabilization of a class of uncertain nonlinear output feedback systems with unstable internal dynamics. The coefficients which characterize the internal dynamics are allowed to be functions of system output, and the uncertainty of the system is characterized by an unknown constant parameter vector. The key step in the proposed control design is to estimate the internal state variables and impose control over them. The control design is presented first for systems without unknown parameters, and then for the systems with unknown parameters using adaptive control techniques.     

Identification and adaptive control of SISO singular systems

A general adaptive control scheme is presented for an unknown time invariant singular system of the form Ex(t + 1) = Ax(t) + Bu(t); y(t) = Cx(t). Owing to the non-causality of this kind of system, the identification of unknown parameters is re-considered and a new residual signal is constructed and used in the recursive calculations. A general design procedure is obtained that uses the identified parameters and includes two steps: (i) preliminary output feedback gain design in order to make the original system causal; (ii) adaptive control design for the causal system. It has been shown that any adaptive control algorithm can be combined with this scheme to obtain a globally stable closed-loop system. The design procedure is shown to perform well on a simulation of a third-order singular system     

利用模糊系统的直升机的自适应控制

针对直升机动力学为非线性,且存在不确定因素和状态变化,设计利用模糊系统的自适应控制器.设计的控制器是系统的输出跟踪参考模型输出的直接调整模糊控制器参数的自适应控制器.又利用Lyapunov函数保证了闭环控制系统的稳定性并推导最优的自适应规律.实验结果表明,有外部扰动的情况下所设计的自适应控制器比模糊控制器对直升机控制具有良好的动态响应和稳定性,是一种非常有效的控制方法.     

Duffing系统的同步化控制研究

本文研究含有未知参数的Duffing系统的同步化控制问题．首先，研究了系统参数已知情况下的同步化控制问题．其次，考虑了系统参数未知情况下的同步化控制问题；由于直接处理该问题比较困难，我们在控制法则中引入了可变参数来代替未知参数，该参数可由完善的自适应法则获得．再次，通过理论证明给出了控制目标，该控制目标可由提出的控制法则来实现．最后，用一些仿真实例证实了该方法的有效性．     

新超混沌系统的动力学行为及自适应控制和同步

分析了一个新混沌系统的超混沌动力学行为,给出了这个未知参数的超混沌系统的自适应控制和同步问题的数值模拟结果.运用相图、分岔图、Lyapunov指数谱和庞加莱截面图,返回映射和功率谱等揭示了系统混沌行为的普适特征,基于Lyapunov稳定性理论,采用自适应控制方法将系统的混沌运动控制到一个不稳定的平衡点.此外,设计自适应控制律以实现超混沌系统的状态同步,仿真结果表明所提出的方法的有效性.     

Adaptive control of manipulators using uncalibrated joint-torque sensing

The application of joint-torque sensory feedback (JTF) in robot control has been proposed in the past that, unlike the model-based controllers, does not require the dynamic model of the robot links. JTF, however, assumes precise measurement of joint torque and accurate friction model of the joints. This paper presents an adaptive JTF control algorithm that does not rely on these assumptions. First, the robot dynamics with JTF is presented in a standard form with a minimum number of parameters, where the inertia matrix appears symmetric and positive definite. Second, an adaptive JTF control law is developed that requires only incorporation of uncalibrated joint-torque signals, i.e., the gains and offsets of multiple sensors are unknown. Also, all physical parameters of the joints including inertia of the rotors, link twist angles, and friction parameters are assumed to be unknown to the controller. The stability analysis of the control system is presented. Experimental results demonstrating the tracking performance of the proposed adaptive JTF controller are presented.     

Adaptive nonlinear excitation control with L2 disturbance attenuation for power systems

This paper presents a design approach to nonlinear feedback excitation control of power systems with unknown disturbance and unknown parameters. It is shown that the stabilizing control law with desired L₂ gain from the disturbance to a penalty signal can be designed by a recursive way without linearization. A state feedback law is presented for the case of the system with known parameters, and then the control law is extended to adaptive controller for the case when the parameters of the electrical dynamics of the power system are unknown. Simulation results demonstrate that the proposed controllers guarantee transient stability of the system regardless of the system parameters and faults.