自适应控制算法的统一格式

本文提出了直接自适应控制算法的统一格式,它不仅包括了Egardt和Landau提出的统一格式,而且包括了控制非最小相位系统的自适应控制算法。本文还首次提出了间接自适应控制算法的统一格式,统一了7种主要显式自适应控制算法。文中讨论了直接算法统一格式与间接算法统一格式之间的联系,证明了两种统一格式的等价性。     

自适应控制算法统一格式的稳定性和收敛性

本文对文献提出的自适应控制算法的统一格式进行了稳定性和收敛性分析。首先表明:由直接自适应算法的统一格式得到的直接自适应算法即使用于可测干扰作用的非最小相位系统仍然具有全局收敛特性,即以概率1输入输出采样均方有界,广义输出跟踪误差条件采样均方极小。同时也表明:由间接自适应控制算法的统一格式得到的间接自适应算法不需要最小相位和系统延时已知条件而具有稳定和收敛特性。     

基于IMFAC的无人艇抗干扰航向自适应控制

针对无人艇在航向控制中易受风浪流等环境干扰,导致控制效果下降的问题,提出一种结合细菌觅食算法的改进无模型自适应控制算法;文章首先分析了偏格式动态线性化方法在无人艇航向控制中的应用问题,并设计了虚拟输出项以满足无模型自适应控制假设条件,建立了基于偏格式动态线性化方法的无模型自适应航向控制器;针对无模型自适应控制算法参数初始值选取范围问题,设计了改进细菌觅食算法对参数初始值进行预整定,保证了算法的快速收敛;最后通过半物理仿真试验验证了所设计算法的有效性;试验表明,在模拟的3级海况干扰下,无人艇在30°阶跃航向控制和±30°方形航向控制中,相较于传统算法出现的较大稳态误差,使用无模型自适应控制算法能在经过10 s左右调整后,将误差稳定趋近于零,实现无人艇的航向自适应控制。     

一种无模型自适应积分终端滑模控制方法

针对一类包含扰动的非线性离散时间系统,提出一种新的无模型自适应离散积分终端滑模控制算法.该算法基于紧格式动态线性化数据模型,利用离散积分终端滑模控制算法设计无模型自适应控制器,并采用扰动估计技术估计系统的扰动项,其中动态线性化方法中“伪偏导数”的估计算法仅依赖于被控系统的I/O测量数据.理论分析证明了系统输入输出有界,并通过仿真实验验证了所提算法的有效性.     

基于偏格式线性化的MIMO系统无模型自适应控制

针对常规PID控制器不能很好兼顾抗干扰性与鲁棒性以及基于模型的控制算法过于依赖受控系统数学模型的缺点,提出一种适用于离散时间多输入多输出(MIMO)非线性系统的无模型自适应控制算法.该算法首先通过偏格式线性化方法将非线性系统线性化,再利用一种新型的投影算法在线辨识受控系统参数,根据辨识得到的受控系统参数直接递推计算无模...     

非线性系统的模糊免疫PSD控制与仿真

针对模糊免疫PID控制算法中微分与积分增益不能根据系统特性自动调整的问题,提出了一种模糊免疫PSD(Proportional Summation Derivative)控制算法。该方法将自适应PSD算法与模糊免疫PID算法相结合,利用自适应PSD控制算法根据过程误差的几何特性建立的PSD控制规律,使得模糊免疫PID控制算法中的微分和积分增益可以随比例增益的变化而自适应调整,从而进一步提高控制算法的自适应性能。仿真实验表明,采用该算法可以提高非线性、时变系统的控制性能,并能减少参数调整的工作量。     

RTP自适应传输控制算法的研究

RTP自适应传输控制算法是在基于实时传输协议（RTP）的流媒体服务中进行端到端流量控制的算法,对于保证流媒体服务质量有重要作用。本文详细介绍了流媒体传输对RTP自适应传输控制算法的要求,对现有算法的各个组成棋块进行了深入分析;总结了目前RTP自适应传输控制算法的特点,指出了该算法今后研究和设计的趋势。     

一类非线性离散系统自适应准滑模控制

针对一般非线性离散时间系统的不确定性和扰动抑制问题, 提出一种新的自适应准滑模控制算法. 算法包括两部分, 其一是基于紧格式动态线性化模型的自适应准滑模控制器设计, 其中动态线性化方法中“伪偏导数”的估计算法仅依赖于系统I/O 实时量测值. 其二是采用径向基神经网络估计器来估计系统的综合不确定性. 理论分析证明了系统的BIBO稳定性. 仿真结果验证了所提算法的有效性.     

增益自调整单神经元自适应PID算法的研究

针对单神经元自适应PID算法对增益K的敏感性,提出了增益K自调整的单神经元自适应PID控制算法。仿真结果表明,这种控制算法具有良好的自适应性和鲁棒性。     

基于递推最小二乘的自适应滤波振动主动控制算法分析

针对自适应滤波X最小均方差(FXLMS)和滤波U最小均方差(FULMS)振动主动控制算法收敛性较为缓慢的问题,给出一种基于递归最小二乘(RLS)方法的自适应滤波控制算法。该算法大致有无限长脉冲响应(IIR)滤波器结构和RLS算法两部分组成,IIR滤波器作为整个算法的主体框架,采用RLS算法针对滤波器的权值进行实时调整,实现了自适应滤波控制算法的功能。仿真对比分析表明,所提算法收敛速度较快。经过实验平台验证,被控对象的整体振动响应下降了65%左右,证明了算法的有效性和可行性。     

一种新的基于运动矢量场及弹性模板的自适应快速搜索算法

该文提出了一种新的基于运动矢量场及弹性模板自适应快速搜索算法，它是以视频运动具有高度时空相关性为基础，从运动矢量场的均匀性出发，综合采用了快速块匹配、自适应弹性模板选择策略、自适应阈值与搜索中止准则等一系列技术，是一种具有伸缩性结构的自适应快速运动估计算法．实验结果表明，该算法以极小的搜索代价得到了与全搜索相当的效果，并在搜索速度和搜索效果方面明显优于MPEG-4最新推荐的快速运动估计算法，特别是在中低码率压缩时有着更优良的性能，非常适合于低码率实时视频压缩领域的应用．     

发动机爆震检测技术研究

汽车发动机爆震检测是典型的一唯时变信号模式识别问题,详细描述了基于离散傅立叶变换（DFT）进行发动机各气缸爆震强度模式识别的算法,并采用递阶模式特征判别和判决阈值自调整策略,有关算法集成到我们自主开发的发动机控制系统中,并在国产奥托微型轿车上取得满意的实际应用效果.     

A new algorithm for decentralized adaptive control

A new algorithm for decentralized adaptive control is proposed in this paper. This algorithm consists of an ordinary local adaptive controller and a variable structure adaptive controller. The adaptive variable structure component of this algorithm is used to compensate for uncertain interconnections among the subsystems and to ensure global stability of the overall system. Simulation results are also presented to demonstrate the performance of the closed-loop control system     

基于时空域相关预测的步长自适应运动估计算法

为了更好地消除视频中空间和时间冗余,快速并有效地获得足够精度的运动矢量,本文提出一种改进的自适应十字搜索算法。本文算法利用时间空间域相关来预测当前块的运动矢量,对于视频的边缘图像采取固定小步长来进行十字搜索,对于图像的非边缘部分则采取由粗到精的方式进行搜索,搜索模板的自适应臂长为预测得到的目标运动矢量的横纵坐标的最大值。通过实验仿真比较传统的自适应十字搜索算法及其他几种经典的运动估计算法,结果表明本文算法增强了搜索预测的准确性,减少了平均每块搜索的次数,提高了搜索速率。     

Pattern recognition and control by adaptive methods for an intelligent mobile vehicle

This paper describes adaptive methods for both pattern recognition and control in an experimental mobile vehicle (MV). An adaptive resonance theory (ART) neural network is used as the character recognizer. It can self-organize and self-stabilize in response to complex binary input vectors. New input patterns can be saved in such a fashion that the stored patterns are not forgotten or destroyed. By merging the advantages of the feed-forward neural network, adaptive algorithm, and fuzzy control, a neuro-fuzzy system also is proposed. This can deal with a large amount of training data in the neural network, from these data produce more reasonable fuzzy rules with the adaptive algorithm, and then control the object by fuzzy control. This is not a simple combination of the three methods, but a merger into one intelligent control system. Finally, the experimental results and some conclusions are given. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Comments on ‘ Asymptotic behaviour of root-loci of linear multivariable systems ’

In the first part of this paper, a continuous time adaptive identification algorithm via newly developed adaptive laws is presented. The principle of the algorithm is very simple, nevertheless the convergence property is quite excellent. In the second half part, two continuous time adaptive control algorithms via adaptive laws, as were used for adaptive identification, are presented. A new canonical form for adaptive control is first developed for this purpose. The design procedure for adaptive control systems requires neither positive realness nor augmented error signals and can be applied irrespective of the relative degree of the plant transfer function.     

An adaptive controller for batch feed bioprocess application to lysine production

This paper presents an adaptive algorithm designed for batch feed bioprocess control, The proposed algorithm is based on a non-linear ‘grey box’ model, which is adapted on-line to the process behaviour by the estimation of a key parameter. Stability and convergence analysis of this algorithm is presented, and from this, a practical tuning method is given. The proposed algorithm is compared to a standard adaptive generalized predictive controller approach, and is shown to exhibit similar performance while being easier to use. A real application of this adaptive algorithm to a batch feed lysine process is presented. Good results are obtained, which show that this control scheme is a worthwhile alternative for batch feed bioprocess control.     

Self-tuning leader-follower games

The optimization of a class of linear discrete-time stochastic systems with unknown parameters and multiple decision-makers or controllers each having his own single-stage quadratic objective is considered. Based on a centralized information pattern, a steady-state solution is obtained for the stochastic adaptive leader-follower game problem. It is shown that this class of leader-follower game problems can be transformed to an equivalent class of multiple-input-multiple-output self-tuning control problems with a single decision-maker. The convergence of the multiple-input-multiple-output self-tuning controller is established and applied to the leader-follower game. A numerical simulation illustrates the proposed self-tuning algorithm for an adaptive leader-follower strategy.     

A pattern adaptive thinning algorithm

A simple sequential thinning algorithm for peeling off pixels along contours is described. An adaptive algorithm obtained by incorporating shape adaptivity into this sequential process is also given. The distortions in the skeleton at the right-angle and acute-angle corners are minimized in the adaptive algorithm. The asymmetry of the skeleton, which is a characteristic of sequential algorithm, and is due to the presence of T-corners in some of the even-thickness pattern is eliminated. The performance (in terms of time requirements and shape preservation) is compared with that of a modern thinning algorithm.     

玻兹曼机(Boltzmann Machine)与自适应模式识别

本文综述了最新出现的一种并行计算模型--玻兹曼机的基本原理;学习过程;模拟退火 算法和它们在图象处理、模式识别等方面的应用.初步探讨了自适应模式识别算法与玻兹曼 机学习过程的关系.