改进Turbo-BLAST检测算法及其仿真

Turbo-BLAST系统基于MMSE的迭代软干扰消除检测技术可有效克服由于V-BLAST的应用带来的多流干扰,但是在迭代检测译码过程中存在错误传播,并且收敛性较差,尤其在接收天线远小于发射天线时。而MAP迭代检测算法的性能接近最优,但复杂度随发射天线和调制阶数呈指数增长。所以本文把这两种算法相结合,提出改进的SIC-MAP/MMSE检测算法。仿真结果表明,在相同频谱利用率的条件下,这种新检测算法在性能和收敛性上明显优于MMSE的迭代软干扰消除(SIC-MMSE)检测技术。     

序贯博弈的多机器人协调避障

针对复杂环境下多机器人避障冲突问题,提出一种基于序贯博弈机制的多机器人协调避障算法,该算法以速度大小作为序贯博弈的优先级排序标准,将多机器人碰撞冲突解决问题转化为求解系列子博弈的均衡解,由子均衡解组合成系统的最优策略,算法为个体机器人设计了多目标优化性能函数,在提高系统性能的同时,实现了个体利益的尽可能最大,仿真验证了该算法的有效性.     

基于CMAC和PID控制的电液伺服加载系统

该文是对某型飞机起落架加载控制系统的研究,针对该电液伺服加载系统运动干扰产生的多余力,严重影响加载系统的精度,设计前馈补偿器进行校正,仿真表明前馈补偿能够有效地抑制加载中的多余力.同时提出基于CMAC和P复合控制算法的控制方案,对采用复合控制前后的模型进行仿真分析,结果表明CMAC和PID复合控制抗干扰能力强、跟踪精度高,提高了加载系统性能.     

空时分组码MC-CDMA系统中的多用户检测器

本文研究了空时分组码MC-CDMA系统中的多用户检测器,提出了一种适合下行链路的接收机.该方法可以在克服信道衰落的同时抑制多址干扰,在下行链路的接收方只需进行简单解码和多用户检测就可以获得同采用多天线接收相同的效果.由于采用发射分集,已将多天线接收的负担由移动方转移到了基站方;同时采用级联处理,计算量小.仿真结果也证明了该方法的有效性.     

一种改进的高速可见光通信系统信道均衡算法

衡量光通信系统的性能指标有可靠性和有效性,码间干扰是影响通信系统性能的一个重要因素,信道均衡能有效地克服信息在光通信系统传输中的多径干扰和消除码间干扰,是高速可见光通信的关键技术之一.本文研究了一种基于OFDM的高速可见光通信系统的信道均衡算法,仿真结果表明,该算法与传统算法相比降低了最小均方误差值和误码率,从而提高了光通信系统收发信号的性能.     

基于量化状态系统的多体系统动力学求解方法

针对多体系统动力学刚性方程的求解问题,基于量化状态系统(QSS)方法,提出一种多点校正(MCQSS)显式算法。算法运用两个迟滞量化函数对系统的状态变量进行离散,引入多点校正思想对状态变量导数进行修正,以有效提高刚性系统的求解精度和效率。通过仿真求解双摆系统证明了算法的可行性。从仿真精度与仿真效率两方面将MCQSS算法与传统数值积分方法、QSS方法和BQSS算法进行性能对比,结果表明该算法在保证仿真效率的同时能够有效提高仿真精度,算法性能优于其他算法。     

基于有限反馈的多小区协作迫零预编码

为了同时抑制小区间干扰和用户间干扰,在多小区多用户系统中,基站通过共享CSI,采用小区间干扰清零（ICIN）预编码算法,将2种干扰迫零。在该算法的基础上,提出了有限反馈情况下的自适应比特分配算法以最小化量化误差引起的速率损耗,更有效地利用反馈比特资源。并且,给出速率损耗保持恒定时,用户所需的总反馈比特数的变化规律。仿真结果表明,提出的自适应比特分配算法优于等比特分配算法,并且采用自适应比特分配时系统速率损耗较小,而复用增益与准确CSI时相同。     

基于双曲正割函数趋近律的多轴调平滑模控制研究

为解决复合材料压机多轴调平系统的控制精度不高的问题,将滑模变结构控制算法应用到多轴调平系统中。开展了多轴调平系统的不平衡机理、调平控制策略及算法的研究,建立了多轴调平系统的数学模型。结合基于双曲正割函数改进的指数趋近律,提出了一种多输入多输出滑模变结构控制算法,给出了控制器的设计方法,并将其应用到滑块的水平控制中。通过仿真分析和试验验证相结合的方式验证了算法的有效性。研究结果表明:该控制器能削弱抖振,使多轴调平的滑块系统快速准确地进行调平,能较好地抑制干扰和未建模动态,提高了系统的鲁棒性,且控制器简单,易于实现。     

基于噪声方差估计的稳健融合算法

给出一种基于噪声方差估计的稳健融合算法.针对集中式多传感器融合结构,利用随机逼近算法分析了各传感器权的最优分配原则,并给出了一种基于方差估计的权的自适应调整算法.结合多传感器融合跟踪系统,为了提高非线性系统的跟踪性能,滤波算法采用了非线性逼近能力更好的Unscented卡尔曼滤波算法.通过计算机仿真实验,获得了良好的融合效果.与经典的加权方法相比,在相同仿真航路条件下,融合精度和跟踪精度都得到了显著提高.     

MIMO-OFDM系统盲信道估计算法的研究

MIMO-OFDM技术将OFDM与空时编码技术有机结合在一起,能够大幅度地提高无线通信系统的信道容量和传输速率,并能有效地抵抗多径衰落、抑制干扰和噪声,从而引起了通信界的广泛关注。介绍MIMO—OFDM系统的基本原理及移动通信系统信道估计的意义,对MIMO-OFDM系统目前存在的几种典型盲信道估计算法进行分析和讨论;针对目前大多数的算法不适用于MISO-OFDM系统,介绍一种新的适用于SISO、MISO以及MIMO-OFDM系统的盲信道估计算法;总结和展望了MIMO-OFDM系统信道估计算法的研究方向和关键问题。     

多输入多输出非线性静态系统神经网络辨识的研究

本文通过对现有的系统辨识特点的分析，提出使用神经网络理论与方法对多输入多输出非线性静态系统进行辨识，并给出了一个实际系统使用具有自适应学习率反传算法辨识的有关结果，研究表明：所用理论与方法是有效的     

多输入多输出Hammerstein Wiener交流电弧炉电极系统模型

在交流电弧炉中对于电极系统的描述,目前大都采用针对单相电极的单输入单输出的Hammerstein-Wiener(H-W)模型,这种模型过于简化真实电极系统结构,导致模型的预测精度较低。针对该问题,提出一种基于多输入多输出H-W模型的电极系统建模方法,该模型的结构与实际电极系统结构一致,有利于模型预测精度的提高,另外在多输入多输出的静态非线性块不可逆的条件下,提出可分非线性最小二乘算法对H-W模型参数进行辨识。最后采用实际数据验证,在预测精度上,多输入多输出H-W电极系统模型优于传统的单输入单输出H-W电极系统模型。     

STC-OFDM系统中的空时编码方案及仿真分析

多输入多输出天线系统(multiple input multiple-output,简称MIMO)通过实现全分集,可以大幅度提高通信系统的质量;但是其作为一种窄带技术,只适用于频率平坦性信道,对多径衰落无能为力。本文提出了将正交频分复用(orthogonal frequency division multiplexing,简称OFDM)与MIMO相结合的方案,建立了二者相结合的STC-OFDM系统模型,给出了该系统的空时编码方案,并利用MATLAB软件对STBC-OFDM系统的性能进行了仿真。仿真结果表明,结合后的STBC-OFDM系统与传统的OFDM系统相比,其可靠性获得了提升。     

极点配置不同算法的比较

极点配置是线性系统综合中的重要问题,尤其是SISO系统的极点配置问题尤为重要,很大程度上促进着MIMO系统极点配置问题的解决。通过不同算法的比较,介绍了一种更简洁地计算SISO系统的MIMO系统的极点配置方法,以使相对通用极点配置算法进一步完善。     

Design and Analysis of Fault Diagnosis and Fault-tolerant Control for a Class of MIMO Nonlinear State Systems

This paper presents a fault diagnosis and fault-tolerant control algorithm,which can be used for a class of multi-input multi-output(MIMO)nonlinear state system...     

Robust decentralized hybrid adaptive output feedback fuzzy control for a class of large-scale MIMO nonlinear systems and its application to AHS

This paper presents a novel observer-based decentralized hybrid adaptive fuzzy control scheme for a class of large-scale continuous-time multiple-input multiple-output (MIMO) uncertain nonlinear systems whose state variables are unmeasurable. The scheme integrates fuzzy logic systems, state observers, and strictly positive real conditions to deal with three issues in the control of a large-scale MIMO uncertain nonlinear system: algorithm design, controller singularity, and transient response. Then, the design of the hybrid adaptive fuzzy controller is extended to address a general large-scale uncertain nonlinear system. It is shown that the resultant closed-loop large-scale system keeps asymptotically stable and the tracking error converges to zero. The better characteristics of our scheme are demonstrated by simulations.     

A nonlinear regression model-based predictive control algorithm

This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.     

焦化鼓风机系统智能控制策略研究及应用

针对焦化鼓风机系统具有非线性时变、多变量、强耦合及存在随机干扰的特点,通过采用基于最近邻聚类方法的RBF神经网络快速学习算法,实时在线辨识,建立被控对象的精确逆模型并用于控制,实现了将具有强耦合特性的多输入多输出(MIMO)系统解耦成单个独立的伪线性对象,并提出一种基于RBF神经网络逆控制与非线性比例积分微分(PID)控制相结合的智能控制策略,保证了系统稳定的同时改善了控制系统性能.仿真和应用结果证实了该控制策略具有快速适应对象和过程变化的能力及较强的鲁棒性.     

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies.     

Finite time control for MIMO nonlinear system based on higher-order sliding mode

Considering a class of MIMO uncertain nonlinear system, a novel finite time stable control algorithm is proposed based on higher-order sliding mode concept. The higher-order sliding mode control problem of MIMO nonlinear system is firstly transformed into finite time stability problem of multivariable system. Then continuous control law, which can guarantee finite time stabilization of nominal integral chain system, is employed. The second-order sliding mode is used to overcome the system uncertainties. High frequency chattering phenomenon of sliding mode is greatly weakened, and the arbitrarily fast convergence is reached. The finite time stability is proved based on the quadratic form Lyapunov function. Examples concerning the triple integral chain system with uncertainty and the hovercraft trajectory tracking are simulated respectively to verify the effectiveness and the robustness of the proposed algorithm.