Iterative learning control for nonlinear systems with uncertain state delay and arbitrary initial error

Most of the existing iterative learning control algorithms proposed for time-delay systems are based on the condition that the time-delay is precisely available, and the initial state is reset to the desired one or a fixed value at the start of each operation, which makes great limitation on the practical application of corresponding results. In this paper, a new iterative learning control algorithm is studied for a class of nonlinear system with uncertain state delay and arbitrary initial error. This algorithm needs to know only the boundary estimation of the state delay, and the initial state is updated, while the convergence of the system is guaranteed. Without state disturbance and output measurement noise, the system output will strictly track the desired trajectory after successive iteration. Furthermore, in the presence of state disturbance and measurement noise, the tracking error will be bounded uniformly. The convergence is strictly proved mathematically, and sufficient conditions are obtained. A numerical example is shown to demonstrate the effectiveness of the proposed approach.     

一种基于QRD—RLS分数倍时延估计的方法

数字预失真技术可以有效的增大功放线性工作区间来提高功放效率。数字预失真做为自适应系统,在直接学习和间接学习架构中都试图使得进入预失真器的信号与从功放耦合的反馈信号的差值趋于最小。因此输入信号和功放反馈信号在时域上必须要严格对齐,从保证预失真器的收敛。但是由于进入预失真器的信号经过处理后,再经由DAC、射频处理链路、反馈射频链路和反馈下变频得到对应的反馈信号,使得两者间存在一定时延差。常见的延时估计法有插值估计法和基于自适应算的估计算法,文章提出的基于QRD—RLS的延时估计方法具有实现简单以及适应性好的特点。     

双线性状态空间系统的状态观测器设计

针对受过程噪声和量测噪声干扰的双线性状态空间系统,研究其状态估计算法.借助双线性系统的特殊结构,将其等价表示为线性时变模型,推导基于Kalman滤波的状态估计算法.针对线性时变模型中存在的未知变量,基于辅助模型辨识思想,通过构造一个辅助模型,将未知变量用该模型的输出代替,提出基于辅助模型的双线性系统状态估计算法.构造双线性状态观测器,引入delta算子极小化状态估计误差协方差矩阵,从而得到最优状态估计增益,并提出基于delta算子的双线性系统状态估计算法.所提出的算法能够避免线性化过程带来的估计精度差的问题,提高双线性系统的状态估计精度.通过仿真实验验证了所提出算法的有效性,并对比分析了不同噪声情况下所提出算法的估计效果.     

预测反馈控制法及其特性研究

基于与延迟反馈控制法相似的原理，提出了一种新的混沌系统控制方法——预测反馈控制法．该方法基于目标系统的预测状态来构造控制信号，更为简单实用．首先系统阐述了该方法的基本原理和算法过程；然后严格论证了新方法在不同条件下的稳定性；最后借助于实例模拟总结了预测反馈控制法的一些重要特性，这对该方法的深入理解及应用提供了重要的指导作用．     

基于DFT寻径的压缩感知信道估计改进算法

正交频分复用系统中的信号在传输过程中受到无线信道环境衰落和延时的影响,容易产生符号间干扰（ISI）,对信道状态信息进行准确估计是降低ISI、提高信号传输准确率的有效方法。针对贪婪迭代类压缩感知信道估计算法存在的估计径错误及漏选问题,提出一种基于离散傅里叶变换（DFT）寻径的压缩感知信道估计算法DFT-OMP。通过DFT寻径的方式抑制由噪声引起的不理想原子,从而对OMP算法重构过程中的原子进行筛选,解决传统方法选取相关因子最大的原子作为重构原子而导致的依赖信号稀疏度问题。在原子预选后的贪婪迭代类压缩感知算法信道估计中引入残差精度控制,以提高信道估计的自适应性与鲁棒性。仿真结果表明,相对OMP算法,该算法能取得4 dB的信道估计性能增益,其适用于较大导频下的无线通信系统。     

Output Feedback Adaptive Neural Control Without Seeking SPR Condition

For output‐feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low‐pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output‐feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output‐feedback control law is introduced based on a state‐feedback indirect ANC structure. An adaptive neural network (NN) observer is applied to estimate immeasurable system state variables. The output estimation error rather than the basis functions is filtered and the filter output is employed to update NNs. Under given initial conditions and sufficient control parameter constraints, it is proved that the closed‐loop system is uniformly ultimately bounded stable in the sense that both the state estimation errors and the tracking errors converge to small neighborhoods of zero. An illustrative example is provided to demonstrate the effectiveness of this approach.     

利用麦克风阵列的管道主动噪声控制方法

空调通风管道在运行时, 内部出现的低频噪声很难通过包裹消音材料等被动式降噪方法消除. 而在部署主动噪声控制时, 会出现声反馈现象, 影响降噪性能甚至造成控制系统的响应发散. 针对这种声反馈现象, 本文在分析其产生原因的基础上, 将麦克风阵列作为前馈, 对Duvall-Frost结构的线性约束最小方差波束成形算法加入预调向,提出了利用麦克风阵列的管道主动噪声控制方法, 实现单方向拾取来自管道上游的噪声信号, 避免声反馈带来的影响. 并利用滤波器x最小均方误差(FxLMS) 算法作为自适应控制算法, 针对4种典型低频噪声, 在真实管道环境下进行主动降噪实验. 实验结果表明, 相比不使用麦克风阵列的情况, 本文提出的主动噪声控制方法能达到明显的降噪性能, 且在稳定性方面取得较好结果.     

一类不确定非线性MIMO系统的神经网络输出反馈跟踪控制

针对一类具有外部干扰的不确定仿射非线性MIMO系统提出了一种神经网络输出反馈跟踪控制方法. 在仅输出可测的情况下, 控制律和神经网络权值更新律中仅用到输出误差, 无需设计状态观测器或加入低通滤波器使得估计误差动态满足严格正实条件. 为抑制外部干扰和子系统间的交叉耦合及神经网络逼近误差, 在控制律中加入鲁棒控制项. 基于Lyapunov稳定性定理证明了系统的稳定性及信号的有界性. 仿真例子证实了所提方法的可行性.     

基于遗传算法的非线性系统时变时滞的在线估计方法

针对系统输入带有纯时滞的一类非线性系统,选择有限点的的输出误差的平方和构成适应度函数,采用十进制编码技术,提出一种基于遗传算法的非线性系统时变时滞的在线估计方法,该方法具有一定的抗噪声能力。仿真实验结果验证了所提出方法的有效性。     

Feedback channel in linear noiseless dynamic systems controlled over the packet erasure network

This paper is concerned with tracking state trajectory at remote controller, stability and performance of linear time-invariant noiseless dynamic systems with multiple observations over the packet erasure network subject to random packet dropout and transmission delay that does not necessarily use feedback channel full time. Three cases are considered in this paper: (1) without feedback channel, (2) with feedback channel intermittently and (3) with full time availability of feedback channel. For all three cases, coding strategies that result in reliable tracking of state trajectory at remote controller with asymptotically zero mean absolute estimation error are presented. Asymptotic mean absolute stability of the controlled system equipped with each of these coding strategies is shown; trade-offs between duty cycle for feedback channel use, transmission delay and performance, which is defined in terms of the settling time, are studied.     

基于指数保性能的比例-积分-时滞滑模观测器设计

传统状态观测器仅基于当前观测误差重构系统状态,未充分利用系统历史观测数据.针对存在匹配扰动的二阶不确定线性系统,设计一种比例-积分-时滞滑模观测器,实现不确定线性系统状态的鲁棒确切估计.首先,设计带记忆滑模函数,形式为历史观测误差和当前观测误差的线性组合,设计参数包括滑模面增益和人工时滞两部分,将滑模面中的时滞项基于泰勒级数展开,将截断误差表示为积分形式;然后,设计带记忆输出反馈等效控制律,采用时滞依赖型Lyapunov泛函,进行滑模动态指数稳定性分析和观测补偿;接着,将观测器参数设计转化为多目标优化问题,优化目标包括:系统状态衰减率、控制代价、高频噪声不灵敏度,基于粒子群算法,在上述3个优化目标间实现设计参数优化整定,在“快、准、稳”方面进行合理折衷选择;最后,在无源网络系统中,验证所提出滑模观测器的可行性和有效性.     

Optimal Estimation of A Class of Linear Time‐Delay Uncertain Systems

The state estimation problem is investigated for a class of linear uncertain systems with state and noise delay. The optimal one‐step prediction algorithm is presented by introducing a fictitious noise. The predictor is designed based on the projection formula in Hilbert space and has the same dimensions as the original systems. The error covariance consists of two coupled Riccati‐type difference equations. The optimal filter and fixed‐lag smoother are provided based on the predictor. A numerical example is given to show the effectiveness of the proposed approach.     

Robust adaptive control of uncertain non-linear systems using neural networks

This paper presents a robust adaptive output feedback control design method for uncertain non-affine non-linear systems, which does not rely on state estimation. The approach is applicable to systems with unknown but bounded dimensions and with known relative degree. A neural network is employed to approximate the unknown modelling error. In fact, a neural network is considered to approximate and adaptively make ineffective unknown plant non-linearities. An adaptive law for the weights in the hidden layer and the output layer of the neural network are also established so that the entire closed-loop system is stable in the sense of Lyapunov. Moreover, the robustness of the system against the approximation error of neural network is achieved with the aid of an additional adaptive robustifying control term. In addition, the tracking error is guaranteed to be uniformly and asymptotically stable, rather than uniformly ultimately bounded, by using this additional control term. The proposed control algorithm is relatively straightforward and no restrictive conditions on the design parameters for achieving the systems stability are required. The effectiveness of the proposed scheme is shown through simulations of a non-affine non-linear system with unmodelled dynamics, and is compared with a second-sliding mode controller.     

Robust fusion steady‐state filtering for multisensor networked systems with one‐step random delay,missing measurements,and uncertain‐variance multiplicative and additive white noises

The robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, one‐step random delay, missing measurements, and uncertain noise variances, the phenomena of one‐step random delay and missing measurements occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, derandomization approach, and fictitious noise approach, the original multisensor system under study is converted into a multimodel multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. In addition, by using the proposed model transformation approach, the centralized fusion system is obtained, furthermore the robust centralized fusion steady‐state Kalman estimators are proposed. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of nonnegative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.     

An observer for a velocity-sensorless VTOL aircraft with time-varying measurement delay

This paper presents a kind of state observer for a velocity-sensorless vertical take-off and landing (VTOL) aircraft with bounded time-varying delay in its measurement outputs. The proposed observer predicts current state variables based on the delayed outputs, and the estimated state variables can be considered as the actual state variables for feedback control scheme design. Since the delay is time-varying, compared to the constant delay case, different analysis theory must be employed. Under the assumption that the delays are identical for different outputs and bounded input, the asymptotic convergence property of the estimation error based on Lyapunov–Razumikhin theorem is proved. A relative large time delay for the VTOL aircraft in the outputs has been tested in the numerical simulation, and the simulation results show the effectiveness of the proposed observer.     

n比特随机量子系统实时状态估计及其反馈控制

研究了$n $比特随机量子系统实时状态估计及其反馈控制的问题. 对于连续弱测量(Continuous weak measurement, CWM)过程存在高斯噪声的情况, 基于在线交替方向乘子法(Online alternating direction multiplier method, OADM)推导出一种适用于$n $比特随机量子系统的实时量子状态估计算法, 即QSE-OADM (Quantum state estimation based on OADM). 运用李雅普诺夫方法设计控制律, 实现基于实时量子状态估计的反馈控制, 并证明所提控制律的收敛性. 以2比特随机量子系统为例进行数值仿真实验, 通过与基于QST-OADM (Quantum state tomography based on OADM)算法和OPG-ADMM (Online proximal gradient-based alternating direction method of multipliers)算法的量子反馈控制方案的性能对比, 验证了所提控制方案的优越性.     

基于延迟抖动分析的TCP友好拥塞控制算法

基于因特网的以UDP为传输协议的实时多媒体数据传输需要在保证实时性和可靠性的基础上,能够与因特网其他服务所使用的TCP协议公平共享有限的带宽。本文采用基于实时传输协议（RTP）和实时传输控制协议（RTCP）的反馈拥塞控制算法,提出一种简单的拥塞控制机制,使UDP数据流能与TCP数据流和平共处;研究了基于速率控制的TCP友好拥塞控制策略-TFRC,分析了其基本机制和关键问题;提出利用延迟抖动作为潜在拥塞信号来改进TFRC的速率控制机制,以适应实时业务低抖动的要求,并通过NS仿真验证了改进的TFRC算法对实时业务的良好性能。     

Design of UDE‐based controllers from their two‐degree‐of‐freedom nature

The control algorithm based on the uncertainty and disturbance estimator (UDE) is a robust control strategy and has received wide attention in recent years. In this paper, the two‐degree‐of‐freedom nature of UDE‐based controllers is revealed. The set‐point tracking response is determined by the reference model, whereas the disturbance response and robustness are determined by the error feedback gain and the filter introduced to estimate the uncertainty and disturbances. It is also revealed that the error dynamics of the system is determined by two filters, of which one is determined by the error feedback gain and the other is determined by the filter introduced to estimate the uncertainty and disturbances. The design of these two filters are decoupled in the frequency domain. Moreover, after introducing the UDE‐based control, the Laplace transform can be applied to some time‐varying systems for analysis and design because all the time‐varying parts are lumped into a signal. It has been shown that, in addition to the known advantages over the time‐delay control, the UDE‐based control also brings better performance than the time‐delay control under the same conditions. Design examples and simulation results are given to demonstrate the findings. Copyright © 2010 John Wiley & Sons, Ltd.     

基于模糊神经网络的无人机数据传输时延控制模型

传输信道状态若是处于拥塞状态,会使得无人机数据传输时延大幅度增加,所以构建基于模糊神经网络的无人机数据传输时延控制模型。考虑直射、散射和反射等现象确定无人机数据传输信道,计算无人机数据传输信道传输时延,综合能量消耗、时延等因素判断无人机数据传输信道是否处于拥塞状态。利用基于模糊神经网络的时延控制模型生成时延控制指令,通过扩频调制、拥塞调度和队列管理等步骤,实现无人机数据传输时延控制。通实验结果表明,在该模型控制下无人机数据传输时延达到预期水平,控制误差约为0.03s,且未对数据传输进程产生明显不利影响,控制效果更好。     

Distributed control of uncertain systems using superpositions of linear operators

Control in the natural environment is difficult in part because of uncertainty in the effect of actions. Uncertainty can be due to added motor or sensory noise, unmodeled dynamics, or quantization of sensory feedback. Biological systems are faced with further difficulties, since control must be performed by networks of cooperating neurons and neural subsystems. Here, we propose a new mathematical framework for modeling and simulation of distributed control systems operating in an uncertain environment. Stochastic differential operators can be derived from the stochastic differential equation describing a system, and they map the current state density into the differential of the state density. Unlike discrete-time Markov update operators, stochastic differential operators combine linearly for a large class of linear and nonlinear systems, and therefore the combined effects of multiple controllable and uncontrollable subsystems can be predicted. Design using these operators yields systems whose statistical behavior can be specified throughout state-space. The relationship to Bayesian estimation and discrete-time Markov processes is described.