Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Assessment of International GNSS Service Global Ionosphere Map products over China region based on measurements from the Crustal Movement Observation Network of China

The global ionosphere maps(GIM)provided by the International GNSS Service(IGS)are extensively utilized for ionospheric morphology monitoring,scientific research,and practical application.Assessing the credibility of GIM products in data-sparse regions is of paramount importance.In this study,measurements from the Crustal Movement Observation Network of China(CMONOC)are leveraged to evaluate the suitability of IGS-GIM products over China region in 2013-2014.The indices of mean error(ME),root mean square error(RMSE),and normalized RMSE(NRMSE)are then utilized to quantify the accuracy of IGS-GIM products.Results revealed distinct local time and latitudinal dependencies in IGS-GIM errors,with substantially high errors at nighttime(NRMSE:39%)and above 40°latitude(NRMSE:49%).Seasonal differences also emerged,with larger equinoctial deviations(NRMSE:33.5%)compared with summer(20%).A preliminary analysis implied that the irregular assimilation of sparse IGS observations,compounded by China’s distinct geomagnetic topology,may manifest as error variations.These results suggest that modeling based solely on IGS-GIM observations engenders inadequate representations across China and that a thorough examination would proffer the necessary foundation for advancing regional total electron content(TEC)constructions.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Fault detection for nonlinear networked control systems based on fuzzy observer

Security and reliability must be focused on control sys- tems firstly, and fault detection and diagnosis （FDD） is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems （LNCSs）, but nonlinear networked control systems （NNCSs） are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.

Stability analysis and controller design for a class of delay systems via observer based on network

This paper focuses on the problem of stability analysis and controller design for a class of delay systems based on networked control systems. By introducing some free matrix variables, some criteria for stability analysis and observer-based control law design can be obtained by the solving of linear matrix inequalities. A numerical example is also offered to prove the effectiveness of the proposed method.

Improved control of distributed parameter systems using wireless sensor and actuator networks:An observer-based method

In this paper,the control problem of distributed parameter systems is investigated by using wireless sensor and actuator networks with the observer-based method.Firstly,a centralized observer which makes use of the measurement information provided by the fixed sensors is designed to estimate the distributed parameter systems.The mobile agents,each of which is affixed with a controller and an actuator,can provide the observer-based control for the target systems.By using Lyapunov stability arguments,the stability for the estimation error system and distributed parameter control system is proved,meanwhile a guidance scheme for each mobile actuator is provided to improve the control performance.A numerical example is finally used to demonstrate the effectiveness and the advantages of the proposed approaches.

Fault Estimation and Accommodation for a Class of Nonlinear System Based on Neural Network Observer

The problem of fault estimation and accommodation of nonlinear systems with disturbances is studied using adaptive observer and neural network techniques.A robust adaptive learning algorithm based on switchingβsmodification is developed to realize the accurate and fast estimation of unknown actuator faults or component faults.Then a fault tolerant controller is designed to restore system performance.Dynamic error convergence and system stability can be guaranteed by Lyapunov stability theory.Finally,simulation results of quadrotor helicopter attitude systems are presented to illustrate the efficiency of the proposed techniques.

Fault detection based on H_∞ states observer for networked control systems

The influence of random short time-delay to networked control systems （NCS） is changed into an unknown bounded uncertain part. Without changing the structure of the system, an Hoo states observer is designed for NCS with short time-delay. Based on the designed states observer, a robust fault detection approach is proposed for NCS. In addition, an optimization method for the selection of the detection threshold is introduced for better tradeoff between the robustness and the sensitivity. Finally, some simulation results demonstrate that the presented states observer is robust and the fault detection for NCS is effective.

Robust H_∞ observer-based control for synchronization of a class of complex dynamical networks

This paper is concerned with the robust Hoo synchronization problem for a class of complex dynamical networks by applying the observer-based control. The proposed feedback control scheme is developed to ensure the asymptotic stability of the augmented system, to reconstruct the non-measurable state variables of each node and to improve the H∞ performance related to the synchronization error and observation error despite the external disturbance. Based on the Lyapunov stability theory, a synchronization criterion is obtained under which the controlled network can be robustly stabilized onto a desired state with a guaranteed H∞ performance. The controller and the observer gains can be given by the feasible solutions of a set of linear matrix inequalities （LMIs）. The effectiveness of the proposed control scheme is demonstrated by a numerical example through simulation.

An Integrated Image Task Planning in Satellite Networks:From Instruction Release and Observation Perspective

The unreasonable observation arrangements in the satellite operation control center(SOCC)may result in the observation data cannot be downloaded as scheduled.Meanwhile,if the operation instructions released by the satellite telemetry tracking center(STTC)for the on-board payloads are not injected on the specific satellites in time,the corresponding satellites cannot perform the observation operations as planned.Therefore,there is an urgent need to design an integrated instruction release,and observation task planning(I-IRO-TP)scheme by efficiently collaborating the SOCC and STTC.Motivated by this fact,we design an interaction mechanism between the SOCC and the STTC,where we first formulate the I-IRO-TP problem as a constraint satisfaction problem aiming at maximizing the number of completed tasks.Furthermore,we propose an interactive imaging task planning algorithm based on the analysis of resource distribution in the STTC during the previous planning periods to preferentially select the observation arcs that not only satisfy the requirements in the observation resource allocation phase but also facilitate the arrangement of measurement and control instruction release.We conduct extensive simulations to demonstrate the effectiveness of the proposed algorithm in terms of the number of completed tasks.

陆域大地电磁基础网规划与展望

阵列式大地电磁测深是区域地球物理调查的重点发展方向之一,对标国外先进水平和国家社会的基础性需求,中国阵列式大地电磁测深工作程度依然较低,缺乏整体规划,开展全国性观测计划亟需提上日程。系统总结了当前区域地球物理学的发展趋势及国内外大地电磁网建设现状,提出了合作共建陆域多层级大地电磁基础网和大数据中心的顶层设计和规划布局,并对中国大地电磁基础网未来的重点发展方向进行了展望和思考。相关工作的顺利开展将为中国的区域地球物理调查拓展一项新属性参数,推动构建物性地球、结构地球和地球物理场四维模型,进而变革区域地球物理研究范式和工作模式,支撑中国深地探测走向世界前列。

面向无线传感网络的同步时钟技术研究

考虑到传统的时钟同步技术在信息交互过程中产生的延迟具有不确定性,为了消除其对时钟同步的影响,提出了面向无线传感网络的同步时钟技术研究。通过对任意一个网络节点在真实物理时间的本地时间进行变换处理,构建无线传感网络的线性时钟模型,根据高斯分布所具有的加减特性,计算两个连续同步轮次的动态响应时间差值,在离散形式下,构建无线传感网络观测模型。通过计算无线传感网络时钟差值的均值,得到无线传感网络节点的时间戳,将无线传感网络节点数据交互误差补偿的目标函数转换成向量的形式,补偿无线传感网络节点数据交互误差,实现无线传感网络的时钟同步处理。实验结果表明,在微秒级延迟下,该技术的同步精度仍较高,可以有效消除网络信息延迟的不确定性对同步时钟的影响。

基于自适应滑模的欠驱动无人艇轨迹跟踪控制算法

针对欠驱动水面无人艇在航行过程中存在的海洋环境干扰、数学模型参数不确定、执行器故障等问题,提出了一种基于扰动观测器与神经网络技术的自适应滑模轨迹跟踪控制算法;在欠驱动无人艇的三自由度模型基础上,结合视线制导律,提出了一种新的轨迹跟踪制导策略;采用自适应滑模控制技术设计了欠驱动无人艇的轨迹跟踪控制器,有效地抑制了执行器衰减故障对无人艇控制系统产生的影响;同时运用了非线性扰动观测器和自适应径向基函数神经网络分别对无人艇受到的外界时变干扰和模型参数不确定性进行补偿和拟合,提高了无人艇控制系统的鲁棒性;基于Lyapunov定理证明了所设计的控制算法的稳定性,并在MATLAB中进行了仿真测试;仿真结果表明,所提出的轨迹跟踪控制算法可以在较为复杂的环境下实现对欠驱动无人艇的精准控制;相较于对比算法,欠驱动无人艇在文章算法下的位置平均跟踪误差减小了80%以上,具备较高的稳定性和容错能力。

基于LMI和扰动观测器的电动伺服系统RBF神经网络控制

为了提高电动伺服系统的加载力跟踪精度,基于线性矩阵不等式(LMI)设计扰动观测器和控制器。针对系统中的非线性因素,采用RBF神经网络逼近系统的数学模型;在建立系统跟踪目标模型的基础上,根据LMI设计扰动观测器对控制器进行多余力的补偿,利用李雅普诺夫函数证明扰动观测器和控制器的收敛;在MATLAB/Simulink中搭建仿真模型,分析扰动观测器和RBF神经网络在不同工况下对系统相应量的精准估计,且误差均满足所设定的性能指标,同时与PID控制相比较,证明所提控制策略的控制性能更优。

地磁时变观测数据中高压直流输电干扰事件多尺度表示及识别方法

高效准确地识别地磁时变观测数据中受高压直流输电干扰的波形对于提高地磁时变观测数据质量具有重要意义.然而,由于高压直流输电干扰事件持续时间长短不一、干扰程度变化多样,给识别任务带来巨大困难.为了能自动识别长短不同的高压直流输电干扰事件,本文提出一种高压输电干扰事件的多尺度表示及识别方法:利用小波技术具有多尺度的特性,卷积神经网络具有自动特征提取的特性,将二者结合,设计了一个多输入卷积神经网络模型来识别地磁中的高压直流输电干扰事件.首先使用离散小波技术将地磁时变观测样本进行多尺度分解,得到原始样本的多尺度表示,再将分解后的多尺度地磁时变观测样本分别输入到含有多个输入分支的卷积神经网络中,每个分支分别自动提取不同尺度的特征,然后将多个尺度的特征融合,并加入注意力机制来自适应计算每个尺度特征的权重,对多尺度特征进行加权处理,再采用全连接层和SoftMax层进行分类,本文将该模型命名为CBAM-MCNN.在中国地震前兆台网中心提供的高压直流输电干扰样本上进行试验,并将本文所提出模型的识别效果与现有的全卷积网络、残差神经网络、多输入卷积神经网络、IICM-HVDCT-CNN-LSTM进行了对比,在5271条测试样本集上,本文所提出的CBAM-MCNN模型识别准确率达到了97.14%,F_1值达到了97.12%,远远高于其他4种对比模型.

面向新型配电网的能观性分析与精细化协同规划方法

现代控制理论和电力系统状态估计中的能观性定义难以满足配电网态势感知的需求,配电网规划模型中也缺乏对能观指标的考虑,为此,提出面向新型配电网的能观性分析与精细化协同规划方法。对配电网的变量与状态空间模型进行定义,并根据传统能观性定义对配电网中的传统/新型变量进行能观性分析;基于传统能观性定义的缺陷提出新的配电网能观性和能观指标定义,并建立考虑能观性的配电网精细化协同规划模型。算例分析结果表明,所提规划方法考虑了实际量测误差的影响,实现了一次网架规划和二次态势感知设备配置的综合最优,达到了经济性和能观性的平衡。

基于神经网络干扰观测器的巷道机器人鲁棒控制方法研究

以巷道机器人铲斗油缸电液控制系统为研究对象,提出了一种基于神经网络干扰观测器的鲁棒控制器来提高强时变负载力工况下油缸的控制水平。该控制器利用神经网络的学习功能,实时估计和补偿未知负载力。采用反步法设计了鲁棒控制器并进行了系统稳定性的验证。通过Simulink/Simscape搭建了铲斗油缸系统的物理模型,并进行了仿真分析。神经网络干扰观测器可有效完成对未知负载力的实时估计,经过负载力补偿后的鲁棒控制器相对PID控制器精度提高了约79%。

基于注意力的循环PPO算法及其应用

针对深度强化学习算法在部分可观测环境中面临信息掌握不足、存在随机因素等问题,提出了一种融合注意力机制与循环神经网络的近端策略优化算法(ARPPO算法)。该算法首先通过卷积网络层提取特征;其次采用注意力机制突出状态中重要的关键信息;再次通过LSTM网络提取数据的时域特性;最后基于Actor-Critic结构的PPO算法进行策略学习与训练提升。基于Gym-Minigrid环境设计了两项探索任务的消融与对比实验,实验结果表明ARPPO算法较已有的A2C算法、PPO算法、RPPO算法具有更快的收敛速度,且ARPPO算法在收敛之后具有很强的稳定性,并对存在随机因素的未知环境具备更强的适应力。

带有神经网络干扰观测器的视线角约束制导

针对具有终端视线(line-of-sight, LOS)角约束的机动目标拦截问题,提出一种基于径向基函数(radial basis function, RBF)神经网络干扰观测器的LOS角约束制导方法。首先,考虑目标机动过程中加速度信息无法获取的情况,给出了一种基于RBF神经网络的干扰观测器,实现了对目标机动的高精度估计;其次,充分考虑终端角度约束,结合超螺旋算法思想,通过幂次项的引入设计了一种改进的滑模制导律,从而有效提升了有限过载情况下的制导精度;在此基础上,通过Lyapunov定理对算法的收敛性和稳定性分别进行了证明;最后,通过仿真验证对比了3种不同方法在4种拦截场景下的制导性能,同时针对所提方法进行了蒙特卡罗打靶仿真,仿真结果表明所给出的LOS角约束制导律对机动目标拦截精度高、鲁棒性强。