Networked control and supervision system based on LonWorks fieldbus and Intranet/Internet

A networked control and supervision system （NCSS） based on LonWorks fieldbus and lntranet/Intemet was designed, which was composed of the universal intelligent control nodes （ICNs）, the visual control and supervision configuration platforms （VCCP and VSCP） and an Intranet/Internet-based remote supervision platform （RSP）. The ICNs were connected to field devices, such as sensors, actuators and controllers. The VCCP and VSCP were implemented by means of a graphical programming environment and network management so as to simplify the tasks of programming and maintaining the ICNs. The RSP was employed to perform the remote supervision function, which was based on a three-layer browser/server（B/S） structure mode. The validity of the NCSS was demonstrated by laboratory experiments.

A UAV-Assisted V2X Network Architecture with Separated Data Transmission and Network Control

With the explosive increasing number of connecting devices such as smart phones, vehicles,drones, and satellites in the wireless networks, how to manage and control such a huge number of networking nodes has become a great challenge. In this paper, we combine the advantages of centralized networks and distributed networks approaches for vehicular networks with the aid of Unmanned Aerial Vehicle(UAV), and propose a Center-controlled Multihop Wireless(CMW) networking scheme consisting of data transmission plane performed by vehicles and the network control plane implemented by the UAV.Besides, we jointly explore the advantages of Medium Access Control(MAC) protocols in the link layer and routing schemes in the network layer to facilitate the multi-hop data transmission for the ground vehicles.Particularly, the network control plane in the UAV can manage the whole network effectively via fully exploiting the acquired network topology information and traffic requests from each vehicle, and implements various kinds of control based on different traffic demands, which can enhance the networking flexibility and scalability significantly in vehicular networks.Simulation results validate the advantages of the proposed scheme compared with existing methods.

Robust fault detection for a class of nonlinear network control system with communication delay

To investigate a class of nonlinear network control system, a robust fault diagnosis method is presented based on the robust state observer. To access the objective that the designed robust filter is maximally tolerant to disturbances and sensitive to fault, the robustness and stability properties of the fault diagnosis scheme are established rigorously. Using the residual vector, a fault tolerant controller is established in order to guarantee the stability of the closed-loop system, and the controller law can be obtained by solving a set of linear matrix inequalities. Then, some relevant sufficient conditions for the existence of a solution are given by applying Lyapunov stability theory. Finally, a simulation example is performed to show the effectiveness of the proposed approach.

COMPUTER SIMULATION OF NEURAL NETWORK CONTROL SYSTEM FOR CO_2 WELDING PROCESS

In this paper, neural network control systems for decreasing the spatter of CO2 welding have been created. The Generalized inverse Learning Architecture(GILA), the SPecialized inverse Learning Architecture(SILA)-I & H and the Error Back Propagating Model(EBPM) are adopted respectively to simulate the static and dynamic welding control processes. The results of simulation and experiment show that the SILA-I and EBPM have betted properties. The factors affecting the simulating results and the dynamic response quality have also been analyzed.

Computer Network Control System in Automatic Automobile Detection

An automobile test controlling and managing system with computer network is introduced. The architecture of the local network, hardware structure, software structure, design of the test process, and error tolerating redundant design in work position substituting are presented. At last, the pivotal questions solved are discussed. With an advanced structure, this system is multi functional and flexible. Furthermore, advanced computer technology is adopted to improve its technical degree.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though many load balancing methods exist,there is still a need for sophisticated load bal-ancing mechanism for not letting the clients to get frustrated.In this work,the ser-ver with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests.The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low,medium and high load by the load balancing application.Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system.Many Load Balancing schemes are based on the graded thresholds,because the exact information about the networkflux is difficult to obtain.Using two thresholds L and U,it is possible to indicate the load on particular server as low,medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L,between L and U or above U respectively.However,the existing works of load balancing in the server farm incorporatefixed time to measure real time response time,which in general are not optimal for all traffic conditions.Therefore,an algorithm based on Propor-tional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal perfor-mance.The emulation results has shown a significant gain in the performance by tuning the threshold time.In addition to that,tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune thefixed time slots.

一类基于LAN/Control Net/Fieldbus分层结构的网络控制系统设计

大型的生产线网络结构一般分为3层:生产现场层,过程监控制层和管理层,文章以某企业控制系统改造为背景,基于MelsecNet/10控制网络,对网络控制系统设计中的控制方案,控制网络与企业管理层局域网的连接及系统运行模式等主要技术问题作了讨论.

An Adaptive RBF Neural Network Control Method for a Class of Nonlinear Systems

This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.

Global approximation based adaptive RBF neural network control for supercavitating vehicles

A global approximation based adaptive radial basis function（RBF） neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles（SV）.A nominal model is built firstly with the unknown disturbance.Next, the control scheme is established consisting of a computed torque controller（CTC） for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.

Optimization of robustness of network controllability against malicious attacks

As the controllability of complex networks has attracted much attention recently, how to design and optimize the robustness of network controllability has become a common and urgent problem in the engineering field. In this work, we propose a method that modifies any given network with strict structural perturbation to effectively enhance its robustness against malicious attacks, called dynamic optimization of controllability. Unlike other structural perturbations, the strict perturbation only swaps the links and keeps the in- and out-degree unchanged. A series of extensive experiments show that the robustness of controllability and connectivity can be improved dramatically. Furthermore, the effectiveness of our method is explained from the views of underlying structure. The analysis results indicate that the optimization algorithm makes networks more homogenous and assortative.

Adaptive functional link network control of near-space vehicles with dynamical uncertainties

The control law design for a near-space hypersonic vehicle（NHV） is highly challenging due to its inherent nonlinearity,plant uncertainties and sensitivity to disturbances.This paper presents a novel functional link network（FLN） control method for an NHV with dynamical thrust and parameter uncertainties.The approach devises a new partially-feedback-functional-link-network（PFFLN） adaptive law and combines it with the nonlinear generalized predictive control（NGPC） algorithm.The PFFLN is employed for approximating uncertainties in flight.Its weights are online tuned based on Lyapunov stability theorem for the first time.The learning process does not need any offline training phase.Additionally,a robust controller with an adaptive gain is designed to offset the approximation error.Finally,simulation results show a satisfactory performance for the NHV attitude tracking,and also illustrate the controller＇s robustness.

Adaptive Neural Network Control with Control Allocation for A Manned Submersible in Deep Sea

This paper thoroughly studies a control system with control allocation for a manned submersible in deep sea being developed in China. The proposed control system consists of a neural-network-based direct adaptive controller and a dynamic control allocation module. A control energy cost function is used as the optimization criteria of the control allocation module, and weighted pseudo-inverse is used to find the solution of the control allocation problem. In the presence of bounded unknown disturbance and neural networks approximation error, stability of the closed-loop control system of manned submersible is proved with Lyaponov theory. The feasibility and validity of the proposed control system is further verified through experiments conducted on a semi-physical simulation platform for the manned submersible in deep sea.

Decentralized direct adaptive neural network control for a class of interconnected systems

The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconneetions is studied in this paper. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized di- rect adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by adjusted the weights and sliding mode gains on-line for each subsystem using only local informa- tion. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero. Simulation results demonstrate the effectiveness of the proposed approach.

Aerodynamic Effects Compensation on Multi-Rotor UAVs Based on a Neural Network Control Allocation Approach

This paper shows that the aerodynamic effects can be compensated in a quadrotor system by means of a control allocation approach using neural networks.Thus,the system performance can be improved by replacing the classic allocation matrix,without using the aerodynamic inflow equations directly.The network training is performed offline,which requires low computational power.The target system is a Parrot MAMBO drone whose flight control is composed of PD-PID controllers followed by the proposed neural network control allocation algorithm.Such a quadrotor is particularly susceptible to the aerodynamics effects of interest to this work,because of its small size.We compared the mechanical torques commanded by the flight controller,i.e.,the control input,to those actually generated by the actuators and established at the aircraft.It was observed that the proposed neural network was able to closely match them,while the classic allocation matrix could not achieve that.The allocation error was also determined in both cases.Furthermore,the closed-loop performance also improved with the use of the proposed neural network control allocation,as well as the quality of the thrust and torque signals,in which we perceived a much less noisy behavior.

Fuzzy neural network control of underwater vehicles based on desired state programming

Due to the nonlinearity and uncertainty, the precise control of underwater vehicles in some intelligent operations hasn’t been solved very well yet. A novel method of control based on desired state programming was presented, which used the technique of fuzzy neural network. The structure of fuzzy neural network was constructed according to the moving characters and the back propagation algorithm was deduced. Simulation experiments were conducted on general detection remotely operated vehicle. The results show that there is a great improvement in response and precision over traditional control, and good robustness to the model’s uncertainty and external disturbance, which has theoretical and practical value.

Co-design for an SoC embedded network controller

With the development of Ethernet systems and the growing capacity of modem silicon technology, embedded communication networks are playing an increasingly important role in embedded and safety critical systems. Hardware/software co-design is a methodology for solving design problems in processor based embedded systems. In this work, we implemented a new 1-cycle pipeline microprocessor and a fast Ethemet transceiver and established a low cost, high performance embedded network controller, and designed a TCP/IP stack to access the Intemet. We discussed the hardware/software architecture in the forepart, and then the whole system-on-a-chip on Altera Stratix EP1S25F780C6 device. Using the FPGA environment and SmartBit tester, we tested the system＇s throughput. Our simulation results showed that the maximum throughput of Ethemet packets is up to 7 Mbps, that of UDP packets is up to 5.8 Mbps, and that of TCP packets is up to 3.4 Mbps, which showed that this embedded system can easily transmit basic voice and video signals through Ethemet, and that using only one chip can realize that many electronic devices access to the Intemet directly and get high performance.

A Fuzzy-Neural Network Control of Nonlinear Dynamic Systems

In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.

FUZZY NEURAL NETWORK CONTROL FOR VIBRATION WAVEFORM SYSTEM OF MOLD

Combining with the characteristic of the fuzzy control and the neural networkcontrol(NNC), a new kind of the fuzzy neural network controller is proposed, and the synthesisdesign method of the control law and fast speed learning algorithm of the parameters of networks areput forward. The output of the controller is composed of two parts, part one is derived on basis ofthe principle of sliding control, the lower order model and the estimated parameters of the plantare only required, part two is derived on basis FNN, it is used to compensate the uncertainties ofthe systems. Because new type of FNN controller extracts from the advantages of the intelligentcontrol and model based sliding mode control, the numbers of adjusting parameters and the structureof FNN are simplified at large, and the practical significance and variation range are attached toeach layer of the network and its connected weights, the control performance and learning speed areincreased at large. The Tightness of the conclusions is verified by the experiment of anelectro-hydraulic position servo system of the mold of the continuous casting machinery.

Predictive control and scheduling codesign in network control systems

A codesign approach combining predictive control compensation and network scheduling is presented in this paper to overcome the adverse influences of stochastic time delays and packet losses encountered in network-based real-time control systems. The state estimation and control prediction compensation algorithms are used for the random network delays in the feedback and forward channels, and the stability criteria are analyzed. The proper sampling rate is given with network scheduling to meet the desired system performance, while the network-induced delay is tolerated. Simulations show that the codesign approach works well with the bounded network delay.