时延变化的滞后系统在两种控制器下的鲁棒性分析

本文首先用Smith自适应预估器估同滞后系统的时延变化率，然后分别在极点配置控制器下和MRA控制器下对变时延滞后系统的稳定性进行分析，确定了保证系统鲁棒稳定的时延变化的范围。     

电厂污水处理的自适应控制

污水处理的 PH值控制经常面对大滞后系统 ,且系统的过程特性随污水流量和原水 p H值等因素而变化 ,难以控制。本文提出的自适应前馈和自适应反馈控制技术可将污水 p H值平稳控制在设定值± 0 .15的范围内 ,采用普通 PL C实现。     

具有高阶时延的离散非仿射非线性系统的自适应跟踪控制

本文研究了一类具有高阶输入–输出时延的非仿射非线性离散不确定系统的自适应输出跟踪控制问题,提出了一种基于隐函数的自适应输出反馈输出跟踪控制方案.该方案主要解决了两个技术问题:一是构造了基于未知参数估计和未来时刻信号估计的隐函数方程解的自适应控制律,解决了因系统高阶时延导致的控制律因果矛盾问题并实现了闭环稳定和渐近输出跟踪;二是针对非仿射非线性控制律难求解问题,提出了基于迭代解的解析自适应控制律,实现了闭环稳定和实用输出跟踪.最后仿真研究证实了所提出控制方案的有效性.     

利用自适应编码调制的无线网络控制系统的分析和设计

研究了利用自适应编码调制的无线网络控制系统的分析和设计, 自适应编码机制引入无线网络能够提高衰退信道的能量效率, 增加数据传输率. 进一步, 通过等价时延的概念来描述无线传输信道中的时变速率, 干扰和路由特性. 基于时变的网络条件给出了等价时延的上下界. 无线网络控制系统被建模为具有时变输入时延的离散系统. 通过新的时滞系统技术给出了该闭环无线网络控制系统可稳定化的充分条件. 最后, 数值算例表明了所提控制策略的有效性.     

基于智能滤波的自适应纯滞后补偿器

将现代控制理论的自适应技术与经典的纯滞后补偿算法相结合,提出了一种基于智能滤波的自适应纯滞后补偿控制方法.仿真结果表明了这种自适应纯滞后补偿算法的有效性.     

基于自适应Smith预估的链条锅炉蒸汽压力控制系统

针对某厂两台10T/h链条锅炉的特性,设计了基于自适应Smith预估补偿控制算法的锅炉蒸汽压力控制系统.应用该系统后,较好地克服了系统存在的时延,改善了动态性能.     

时延复杂网络的自适应周期间歇同步控制

研究同时具有耦合时延和节点时延复杂网络的自适应周期间歇同步控制问题.运用 Lyapunov 稳定性理论,自适应控制、牵制控制和间歇控制方法,给出保证该时延复杂网络全局指数同步、且保守性更小的判定准则,并给出相应的自适应和牵制自适应间歇同步控制器设计,该控制策略对节点间的耦合强度和网络的拓扑结构等具有较强的鲁棒性.最后以时延非线性动力系统为节点对复杂网络进行数值仿真,验证了结论的正确性和有效性.     

一种开放混合实时系统的开放自适应调度算法

开放计算环境下的实时与非实时任务不确定并发,以及多种实时约束混合的复杂约束系统,即开放混合实时系统的需求越来越广泛.通过引入接收控制、调度服务器、自适应调节机制,提出一种开放环境下的自适应实时系统调度架构--OARtS(open adaptive real-time scheduling).它能适应开放计算环境的不确定性,有控制地接受实时任务运行;可根据系统空闲计算带宽变化,自适应地调节任务的实时等级,使得系统运行在最优的实时性能上;对于软实时任务,可根据其计算带宽需求变化,自适应地调节其计算带宽分配,以适应任务执行时间时变引起的实时不确定性.     

WiMAX系统的自适应实时带宽调度策略

提出一种自适应的实时轮询业务带宽分配机制,SS依据当前带宽需求和以往的实际分配带宽,提前预测实时业务数据包所需求的确切带宽,文中给出了数学分析模型和仿真,仿真结果表明,与传统的带宽分配机制和Mukul提出的自适应算法相比,该自适应带宽分配算法能更好地改善系统性能,提高吞吐量,减少时延和减少缓冲区需求.     

变风量空调的自适应模糊PID复合控制

研究空调系统控制的优化问题,针对变风量空调系统是一个时变、非线性、纯滞后的复杂系统,要求达到舒适节能的环境.传统模糊控制和PID控制方法难以获得较好的控制性能.为解决控制系统中超调量大、振荡和控制精度不高等问题,提出了史密斯预估的自适应模糊- PID复合控制策略.在传统的模糊控制中加入自适应环节,通过模糊自适应校正解决了因模糊规则粗糙而造成控制精度不高,适应能力弱的问题.针对大滞后系统,采用史密斯预估补偿,减小了控制系统的超调和振荡.同时,用模糊与PID相并联的复合控制方式,提高了系统的动态性能指标和稳态精度.仿真结果证明,改进的控制策略响应快、控制精度高、鲁棒性强,为变风量空调系统的智能控制设计提供了参考依据.     

一类具有Markov跳跃参数的不确定混合线性时滞系统鲁棒稳定性研究

讨论了一类具有Markov跳跃参数的不确定混合线性时滞系统的鲁棒稳定性问题．分别给出了非匹配条件下不确定部分范数上界已知时使混合线性系统以概率1渐近稳定的充分条件,和匹配条件下不确定部分范数上界未知时同样可以实现混合系统以概率1渐近稳定的鲁棒自适应控制设计方案．文章研究结果表明,此控制方案对混合线性时滞系统的不确定部分是有效的．     

基于边缘计算的混合储能多微电网功率自适应控制

在微电网中,将多个双向接口变换器并联可提高微电网两侧的功率交换能力,但并联变换器的微电网难以有效控制。研究发现,微电网接入储能系统可以缓冲功率波动、降低功率控制难度。为保证微电网稳定运行,提高功率控制精度,本文提出了基于边缘计算的混合储能多微电网功率自适应控制方法。研究基于边缘计算技术,分析了混合储能多微电网结构,并构建了一个用于混合储能多微电网的自动管理平台。在此基础上,通过对母线实时电压的控制实现频率的调节,增强接口变换器的稳定性,以完成混合储能多微电网功率交互的调度与控制。在混合微电网中,我们接入储能系统,通过功率交换控制、自主功率分频自适应控制实现了混合储能多微电网的功率自适应控制。实验结果表明,所提方法在混合储能多微电网功率控制方面效果较好,有功功率控制误差较小,能够有效保证混合储能多微电网稳定运行,并提高功率自适应控制准确度。     

分散混杂自适应模糊H∞控制器及其在自动化公路系统中的应用

本文针对一类SISO不确定非线性大系统,提出了一种混杂间接和直接自适应分散模糊H∞控制器.通过组合模糊系统和H∞跟踪技术开发的分散自适应模糊控制算法避免了控制设计中含有的符号函数.两种自适应模糊控制器的组合消除了它们各自均不能够同时融合被控对象知识与控制知识的局限.闭环大系统被证明是稳定的,且具有H∞跟踪性能.该算法应用于自动化公路系统中车辆的纵向跟随控制,仿真结果表明混杂自适应模糊H∞控制系统的跟踪性能更好而相应的控制幅值却更小.     

σ校正混合自适应控制律

本文提出了一种σ校正混合自适应控制律,并且证明在对象具有相加和相乘未建模动 态(unmodeled dynamics)的情况下,若采用文中所给出的混合自适应控制系统结构,这种σ 校正混合自适应控制律可保证闭环系统中的所有信号有界,并且对于任何有界初始条件,系 统具有小的均值残余跟踪误差.数学模拟结果表明,本文所提出的混合自适应控制律优于相 类似的全连续和全离散自适应控制律.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

Microcomputer based self-tuning and self-selecting controllers

Classical control system design techniques are extended to include cases with large parameter and state variations. New results on stability of adaptive gain control systems are obtained and a new method to design self-selecting controllers is presented.It is then shown that microcomputers are well suited for hybrid implementation of the resulting control systems.Several examples and a short survey on industrial applications are also given.     

Error models for stable hybrid adaptive systems

This paper presents several stable adaptive algorithms for the control of hybrid and discrete systems in which the control parameters are adjusted at rates slower than those at which the systems operate. Continuous algorithms of an integral type, recently suggested in the literature [5] are also shown to belong to this class. From a practical standpoint, the infrequent adjustment of the control parameters makes for more robust adaptive control while from a theoretical point of view, the algorithms are attractive since they provide a unified framework for the design of continuous, hybrid, and discrete adaptive systems. Simulation results are included to indicate the type of responses that can be expected using the different algorithms.     

Chaos control and hybrid projective synchronization for a class of new chaotic systems

The problems on chaos control and hybrid projective synchronization for a class of new chaotic systems are considered. First, new 4D chaotic systems are proposed by introducing an additional state into a 3D quadratic chaotic system and the states of the systems corresponding to the different ranges of parameter b are exhibited. Second, a single scalar adaptive feedback controller for chaos control of the systems is presented. Third, hybrid projective synchronization (HPS) of two of the chaotic systems with parameters in different conditions are investigated by presenting adaptive feedback control strategies with adaptive parameter update laws and considering controller simplification to achieve complete synchronization. Finally, numerical simulations are demonstrated to verify the effectiveness of the strategies.     

Stabilization of stochastic systems with hidden Markovian jumps

This paper considers the adaptive control of discrete-time hybrid stochastic systems with unknown randomly jumping parameters described by a finite-state hidden Markov chain. An intuitive yet longstanding conjecture in this area is that such hybrid systems can be adaptively stabilized whenever the rate of transition of the hidden Markov chain is small enough. This paper provides a rigorous positive answer to this conjecture by establishing the global stability of a gradient-algorithm-based adaptive linear-quadratic control.     

An adaptive dead-zone inverse controller for systems with sandwiched dead-zones

This paper addresses adaptive control of sandwich non-linear systems having an unknown sandwiched dead-zone between the linear dynamic blocks, as illustrated by a hydraulic valve system. An adaptive hybrid control scheme for control of such sandwiched dead-zone systems is developed. The proposed control scheme employs an inner-loop discrete-time feedback design and an outer-loop continuous-time feedback design, combined with an adaptive dead-zone inverse to cancel the dead-zone effect for improving output tracking. Stability and tracking performance of the closed-loop control system are analysed. Simulation results are used to illustrate the effectiveness of the proposed adaptive dead-zone inverse controller.