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.

Research on robust fault-tolerant control for networked control system with packet dropout

A kind of networked control system with network-induced delay and packet dropout, modeled on asynchronous dynamical systems was tested, and the integrity design of the networked control system with sensors failures and actuators failures was analyzed using hybrid systems technique based on the robust fault-tolerant control theory. The parametric expression of controller is given based on the feasible solution of linear matrix inequality. The simulation results are provided on the basis of detailed theoretical analysis, which further demonstrate the validity of the proposed schema.

Adaptive Control Based on Neural Networks for an Uncertain 2-DOF Helicopter System With Input Deadzone and Output Constraints

In this paper, a study of control for an uncertain2-degree of freedom(DOF) helicopter system is given. The2-DOF helicopter is subject to input deadzone and output constraints. In order to cope with system uncertainties and input deadzone, the neural network technique is introduced because of its capability in approximation. In order to update the weights of the neural network, an adaptive control method is utilized to improve the system adaptability. Furthermore, the integral barrier Lyapunov function(IBLF) is adopt in control design to guarantee the condition of output constraints and boundedness of the corresponding tracking errors. The Lyapunov direct method is applied in the control design to analyze system stability and convergence. Finally, numerical simulations are conducted to prove the feasibility and effectiveness of the proposed control based on the model of Quanser's 2-DOF helicopter.

Fault-Tolerant Control Research for Networked Control System under Communication Constraints

Implementing a control system over a communication network induces inevitable time delays that may degrade performance and even cause instability. One of the most effective ways to reduce the negative effect of delays on the performance of networked control system (NCS) is to reduce network traffic. In this paper, adjustable deadbands are explored as a solution to reduce network traffic in NCS. A method of fault-tolerant control of networked control system is presented, which takes into account system response as well as network traffic. The integrity design for a kind of NCS with sensor failures and actuator failures is analyzed based on robust fault-tolerant control theory and information scheduling. After detailed theoretical analysis, the paper also provides the simulation results, which further validate the proposed scheme.

Robust Fuzzy Tracking Control for Nonlinear Networked Control Systems with Integral Quadratic Constraints

This paper investigates the robust tracking control problcm for a class of nonlinear networked control systems （NCSs） using the Takagi-Sugeno （T-S） fuzzy model approach. Based on a time-varying delay system transformed from the NCSs, an augmented Lyapunov function containing more useful information is constructed. A less conservative sufficient condition is established such that the closed-loop systems stability and time-domain integral quadratic constraints （IQCs） are satisfied while both time-varying network- induced delays and packet losses are taken into account. The fuzzy tracking controllers design scheme is derived in terms of linear matrix inequalities （LMIs） and parallel distributed compensation （PDC）. Furthermore, robust stabilization criterion for nonlinear NCSs is given as an extension of the tracking control result. Finally, numerical simulations are provided to illustrate the effectiveness and merits of the proposed method.

Method for Incorporation of Controllability in Heat Exchanger Network Synthesis by Integrating Mathematical Programming and Knowledge Engineering

A method for incorporation of controlling the heat exchanger networks with or without splits is proposed by integrating mathemati-cal programming and knowledge engineering. The simultaneous optimal mathematical model is established. This method can be practically used in the integration of large-scale heat exchanger networks, not only to synthesize automatically but also to satisfy the requirement of struc-tural controllability with more objective human intervention.

Adaptive integral dynamic surface control based on fully tuned radial basis function neural network

An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.

Predictive Decision and Reliable Accessing for UAV Communication in Space-Air-Ground Integrated Networks

The cooperation of multiple Unmanned Aerial Vehicles(UAVs) has become a promising scenario in Space-Air-Ground Integrated Networks(SAGINs) recently due to their widespread applications,where wireless communication is a basic necessity and is normally categorized into control and nonpayload communication(CNPC) as well as payload communication. In this paper, we attempt to tackle two challenges of UAV communication respectively on establishing reliable CNPC links against the high mobility of UAVs as well as changeable communication conditions, and on offering dynamic resource optimization for Quality-of-Service(QoS) guaranteed payload communication with variable link connectivity. Firstly, we propose the concept of air controlling center(ACC), a virtual application equipped on the infrastructure in SAGINs, which can collect global information for estimating UAV trajectory and communication channels. We then introduce the knapsack problem for modelling resource optimization of UAV communication in order to provide optimal access points for both CNPC and payload communication. Meanwhile, using the air controlling information, predictive decision algorithm and handover strategy are introduced for the reliable connection with multiple access points. Simulation results demonstrate that our proposal ensures an approximate always-on reliable accessing of communication links and outperforms the existing methods against high mobility,sparse distribution, and physical obstacles.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

Hierarchical hybrid control network design based on LON and master-slave RS-422/485 protocol

Aiming at the weaknesses of LON bus, combining the coexistence of fieldbus and DCS (Distribu ted Control Systems) in control networks, the authors introduce a hierarchical hybrid control network design based on LON and master slave RS 422/485 protocol. This design adopts LON as the trunk, master slave RS 422/485 control networks are connected to LON as special subnets by dedicated gateways. It is an implementation method for isomerous control network integration. Data management is ranked according to real time requirements for different network data. The core components, such as control network nodes, router and gateway, are detailed in the paper. The design utilizes both communication advantage of LonWorks technology and the more powerful control ability of universal MCUs or PLCs, thus it greatly increases system response speed and performance cost ratio.

Intelligent Control of SIRES Using Neural Networks and Fuzzy Logic

Development of energy-resources-poor remote rural areas of the world has been discussed by many in the past. Harnessing locally available renewable energy resources as an environmentally friendly option is gaining momentum. Smart Integrated Renewable Energy Systems (SIRES) offer a resilient and economic path to “energize” the area and reach this goal. This paper discusses its intelligent control using neural networks and fuzzy logic.

Terminal Angular Constraint Integrated Guidance and Control for Flexible Hypersonic Vehicle with Dead-Zone Input Nonlinearity

This paper presents an integrated guidance and control model for a flexible hypersonic vehicle with terminal angular constraints.The integrated guidance and control model is bounded and the dead-zone input nonlinearity is considered in the system dynamics.The line of sight angle,line of sight angle rate,attack angle and pitch rate are involved in the integrated guidance and control system.The controller is designed with a backstepping method,in which a first order filter is employed to avoid the differential explosion.The full tuned radial basis function(RBF)neural network(NN)is used to approximate the system dynamics with robust item coping with the reconstruction errors,the exactitude model requirement is reduced in the controller design.In the last step of backstepping method design,the adaptive control with Nussbaum function is used for the unknown dynamics with a time-varying control gain function.The uniform ultimate boundedness stability of the control system is proved.The simulation results validate the effectiveness of the controller design.

盾构掘进姿态控制技术研究现状与未来展望

为系统地分析我国盾构掘进姿态控制技术的研究进展,基于知网检索到的32篇相关文献,总结盾构掘进姿态的主要表征参数和影响因素,并以盾构液压推进系统为例论述其控制原理。同时,结合盾构姿态智能控制的部分案例,总结PID控制、自适应控制、模糊控制、基于神经网络的控制和基于智能算法的控制等技术的优劣势及应用场景。基于以上分析,对盾构姿态控制技术的发展方向进行展望。研究发现:1)盾构掘进姿态的影响因素主要包括几何参数、地层参数和盾构掘进参数。2)由于盾构推进系统需要同时完成盾构向前推进和姿态调整等复杂任务,因此该系统的参数对盾构姿态有着很大的影响,是姿态控制的关键因素之一。3)相较于传统PID控制方法,智能控制方法与PID控制的结合可以提高系统的响应速度、精度、适应能力和鲁棒性。4)未来研究可以围绕基于多源数据融合的控制算法、构建数据-机制混合驱动的控制技术以及加强控制技术在实际工程中的实用性等方面展开,实现更精准、更高效的盾构掘进姿态控制。

垂直起降系统的互补滑模与情感神经网络控制

针对具有未知扰动的垂直起降机双闭环控制系统,提出一种基于连续径向基情感神经网络的互补积分滑模控制策略。建立了包含未知扰动项的垂直起降机的数学模型,为系统内外环分别设计滑模控制器和互补积分滑模控制器,基于对系统鲁棒性的考量,利用连续径向基情感神经网络逼近系统未知扰动项,给出神经网络权重的自适应律,形成一套新型的复合控制策略,利用Lyapunov方法分析了系统的稳定性。通过对垂直起降机的仿真实验与实物实验进行分析对比,证明了所设计的控制器具有更快的收敛速度、更高的跟踪精度和更佳的鲁棒性。

高比例分布式可再生能源交直流混合配电网运行调控:科学问题与技术框架

随着分布式可再生能源发电与中低压直流组网技术的快速发展,未来配电网必然向复杂的交直流混合形态演进,其运行调控技术面临许多新的挑战。在高比例分布式可再生能源交直流混合配电网(HR-HDN)运行调控技术发展需求分析的基础上,提炼出HR-HDN运行调控技术的3个科学问题,并以多类型可控设备协同、源-网-荷-储高度互动化和市场化需求为研究主线,从稳定控制、故障主动防御与恢复、电力市场机制辅助调控以及“平台+数据”数字化集成调控4个方面,提出了HR-HDN的运行调控技术框架。最后,对HR-HDN运行调控关键技术的未来发展进行了展望。

低附着路况条件下车辆横向稳定性控制

针对在冰雪环境下车辆横向稳定控制,为解决在低附着、分布不均的路面情况下车辆对参考轨迹的稳定跟踪问题,设计了基于神经网络调节的模糊PID(Proportional-Integral-Differential)制器,以及基于线性化车辆模型的模型预测控制(MPC:Model Predictive Controll)。以路面附着系数及车辆速度作为输入构建BP(Back-Propagation)神经网络,输出调节系数优化模糊PID控制器控制性能;设计了十自由度模型表征车辆在冰雪环境下的动力学特性,使用MPC实现车辆横向稳定控制。使用CarSim/Simulink进行联合仿真实验,结果表明该控制器能显著提高车辆轨迹跟踪性能。

磁力馈能主动悬架稳定性分析

为了提高车辆行驶平顺性和安全性,提出了一种新型磁力馈能主动悬架,并研究其稳定性。采用BP神经网络PID控制算法(BP-PID)搭建磁力馈能主动悬架控制系统,利用MATLAB/Simulink进行理论仿真。建立B级和C级随机路面,仿真分析不同速度、不同等级下,磁力馈能主动悬架的稳定性。结果表明,相比于被动悬架效果和PID控制的磁力馈能主动悬架效果,采用BP-PID控制可明显提高悬架稳定性。B级随机路面60 km/h时车身垂直加速度改善了45.90%,证明了BP-PID控制的合理性,可有效提升悬架稳定性。

致密油整体缝网压裂技术在杏树岗油田杏69-1井区扶余油层的应用实践

针对大庆致密油储层地质条件逐年变差,以及采用常规压裂改造工艺达不到预期增产效果的问题,创新采用整体缝网压裂工艺,主要从地质选层、缝储匹配、液性组合、有效改造4个方面进行方案优化。依托杏树岗油田杏69-1井区扶余油层“井缝控藏”理念,纵向上通过甜点优选压裂层段,平均单井压裂层数由4层减少至3层。横向上考虑砂体连通关系,以缝控储量最大为目标,优化施工规模,平均单井液体规模降低16%,支撑剂规模降低31.4%,单井成本降低4.7%。同时采用变黏压裂液、控缝高工艺,积极推行全链条挖潜增效,实现该井区扶余油层整体缝网压裂效益开发。现场应用表明,整体缝网压裂技术提高了缝控储量规模,实现了储层改造最大化、缝控储量最大化。实际平均单井日产油5.7t/d,比设计值提高103%;实际建设产能1.62×10^(4)t,超额完成22.7%。

面向立体化异构网络的智融协同传输方法

为应对空天地立体化异构网络中由于节点异构性及连通变化带来的复杂挑战,提出了具备有界无环无阻策略更新能力的传输控制方法HWCTC.该方法以跨层协同控制的方式将网络路由算法引入传输控制框架;在此基础上将路由更新节点的选择问题建模为节点搜索问题,使网络传输中需考虑被调用的资源限定在有界范围内.在此基础上,设计了一种基于广度优先的启发式递增搜索算法,该算法能够有效进行全局和局部的路由配置更新,同时确保新路径无环路且无网络黑洞.此外,为适应空天地立体化异构网络环境的波动性,还设计了一种多模式混合的拥塞控制机制,该机制能在接近网络带宽阈值时切换到更平缓的增窗模式,及时调整策略以应对网络中可能出现的多种情况.仿真实验的结果表明,HWCTC方法在动态且高丢包率的空天地立体化异构网络环境下,不仅提供了高质量的数据传输服务,且相比于经典的Cubic和Reno方法,实现了约61.5%的吞吐量提升,显著增强了数据传输的稳定性,有效减少了节点路由动态变化对传输性能的影响.

空地一体化网络服务连续性保障的低功耗通信与控制联合优化方法

空地一体化网络(AGIN)充分利用了空中基站(ABSs)灵活部署的特点,为热点地区提供了按需覆盖与高质量服务。然而,空中基站的高动态性使得网络的服务连续性难以保障。而且,空中基站能量受限,提升服务连续性和降低功耗通常又对应不同的飞行动作,因此,低功耗的服务连续性保障尤为困难。针对上述问题,该文基于联邦深度强化学习(FDRL)提出了一种面向低功耗服务连续性保障的通信与控制联合优化方法。所提方法通过联合优化空中基站的移动控制、用户关联和功率分配来保障网络服务的连续性。针对空中基站的高动态性,通过在所提方法中设计了环境状态经验池来利用信道的时空相关性,并在奖励函数中引入速率方差来保障网络服务连续性。考虑到不同飞行动作的功耗差异,所提方法通过优化空中基站的飞行动作来降低网络功耗。仿真结果说明,该文所提算法在满足用户速率需求和速率方差需求的前提下,能够减小网络功耗,并且所提联邦深度强化学习的性能接近中心式强化学习的性能。