Aiming at the issues of random delay and delay uncertainty in both the before channel and feedback channel for network control system, the root causes of random delay influence closed-loop control system by case is analysis, and the predictive control method based on neural network to solve the feasibility of existence network random delay in control system closed-loop control has provided. Simulation results show that the method can reflect and predict the delay characteristics of between measurement data represents the network path, and can effectively substitute for the actual network in the design of closed-loop control system based on Internet to research; the method used fast and accurate can be used for online learning network model and forecast the network delay value, provides a new way to remote closed-loop control based on Internet.

Network control system based on Internet broke through the many limitations on control system based on field bus and become the new development direction of the network control system, stable, fast, and accurate still the ultimate goal of network control systems pursued [

See from the current study, analysis and design network controller gradually development by a single variable to multivariate, determine to random, classical control theory to intelligent control theory and advanced control algorithm. But this is only the beginning, so far does not have a systematic approach for analysis, modeling, design whole network control system, and the architecture of the network control system continues to change, the current method is largely concentrated in the condition of network delay is no more than one sampling period, and other cases have yet to be depth.

Self-learning and adaptive capacity of neural network made the neural network model predictive control applications and research gaining increasing attention in the control system, and the prediction control based on neural network has strong robustness can adapt to the changing of system status and network latency links. This paper applied the neural network model predictive control to the network closed-loop system to reduce the impact of random delay to the system, and verified validity of the method by simulation, the method is an effective way to solve the network latency closed-loop control.

In order to study the impact of network latency on the remote closed-loop control system, set up remote motor control system platform based on Internet, a brushless DC motor as charged object, developed DSP as core and motor drive modules with serial communication functions which directly connected the server serial port in order to facilitate the research on motor network control technology and control network functions embedded in the information networks, for the development of the control network search a more portable way, that is though the method of control functions embedded in the information network to build control information network. Remote motor control based on Internet shown in Fig.

Network Remote Control Systems

This double-loop control system based on improve the performance of the remote controller can achieve safe, reliable and real-time closed-loop control under the conditions of network latency. The inner ring of DSP controller as core complete conventional closed-loop controls of the brushless DC motor, such as complete the speed closed-loop control of brushless DC motor based on DSP. The outer ring constituted by the client-server, DSP controller and brushless DC motor to complete the macroscopic closed-loop control of the client. For example, the client issued the directive forward 2cm and server sent the directive to the DSP controller, the DSP controller on their own to complete the instruction and maintain communication with the client. During the directive implementation, even if it is disconnected from the network, the DSP controller also on their own to complete the control task regardless of the impact of network performance.

The remote control of such a complex computer network based on Internet, information transmission and processing on the network takes time, the sender and receiver of information can be viewed as a network transmission delay, the transmission delay existence made network real-time restrict, which is response time determined by the inherent properties of the network system and is inevitable. The presence of network delay and its uncertainty is not conducive to achieve closed-loop control based on network, because in such a system, the network transmission delay not only appears in the before control channel the system, but also appear in the information feedback channel shown in Fig.

The closed loop transfer function of network closed-loop control system can be drawn from Figure.

The characteristic equation is:

Visible, characteristic equation of transfer function of closed-loop control system with the network transmission delay links is a transcendental function of complex variable s, the root of the characteristic equation is no longer finitely but an unlimited number. This is also an important feature of time-delay systems, from the point of information transmission; network delay closed-loop system is a time-delay systems of transmission delay included in the forward channel and feedback channel on the time. Delays caused a negative effect for most of the linear control system and the system changes from stable to unstable. Visible the presence of network delay links not only affect the dynamic quality, but also affect the system stability. Therefore, analysis the time-delay system stability and controller design is a very difficult subject.

In order to study the impact of network delay on the closed-loop control system, the typical second-order system in a remote control, a simple single-link robot arm as control objects to study the network closed-loop control problems. The system dynamics equation [

Among them,

Simulink Block Diagram of Mechanical Arm

According to actual situation of the network closed-loop control let a delay link connected between the system controller and the controlled object, another delay link connected to the feedback channel. In the network control system, the forward channel and feedback channel is generally the same physical link, and sent in both directions at the same time, that these two values of delay links are the same, so this paper set the two delay value in delay link set to the same to study. First, delay time in the delay links adjust to 0, that is not including delay link, repeatedly adjust the PID parameters to obtain the satisfied response curve. Then, keeping the PID parameters unchanged, increase the network delay value gradually starting from 0.02 seconds obtain the response curve shown in Fig.

Realing the PID parameters of the of increase 0.06 seconds delay link instability control system obtain the response curve shown in Fig.

The manipulator control system in Figure

Response after increase network latency

The above analysis shows that the system performance deterioration caused by the remote network delay because of can not correctly calculate the amount of control exerted by the controller to the system, if the system model is known and the size of delay is known, then forecast the state of system in accordance with the principle of the system predict compensation, and calculate the size of control value need to be added the control system in accordance with the predicted state, that is time k applications to predict the state

So, if the predicted state

Model predictive control is according to the running state of the system over the past time and present moment, more accurate forecasting system desired output value in the future moment, calculated control value of the system should be added according to output value desired depending on certain optimization algorithm adaptive computer control of online solving control value [

For a module description of the alleged object behavior in the predictive control based on neural network belong to forward model of system, there use the training methods as shown in Fig.

Neural Network Training Block Diagram of the Manipulator

Neural Network Model Training Results of Manipulator

Rolling optimization is an optimal control algorithm, which uses the output of the rolling finite domain optimization that is the goal of optimization over time. Predictive control proposes optimization index based on the moment in every time instead of using global optimization indexes. Rolling optimization index locality through make it can only get the global optimal solution in the ideal case, but when the model mismatch or time-varying and non-linear or confounding factors can take into account this uncertainty in a timely manner compensate, reducing the deviation, keeping the actual optimal control, and it is also easy to use input/output value of finite difference time domain to identify rapidly the state of controlled object so as to implement the online adjustment to the control law and need for repeated optimization.

Optimization algorithm in this article also uses neural network to achieve, set the time-domain involved in the optimization value of 2, using the BP network neural of hidden layer neuron number 7, the same learning rule Levenberg-Marquartdt do the online training to achieve the control signal to the continuous optimization. Training block diagram is shown in the dashed box in Fig.

Feedback correction is forecast control to keep the dynamic correction forecasting model to ensure that the prediction model with infinitely close to the actual controlled object, and make optimization algorithm establish on the basis of the correct prediction of the system state then the new optimization. Error e1 is the amendment process of the neural network model of the controlled object. Neural network prediction model is built on the basis of the past run data in system, the new operating environment and the actual system has the nonlinear, time-varying, interference and other factors make prediction model based on neural networks need to constantly learn to modify their weights and even structure to ensure that it can well represent the actual controlled object to a control signal prediction.

Build the Simulation block diagram shown in Fig.

Simulation of Network Closed-Loop Control System based on Predictive Neural

Predictive Control Random Responses Curve based on Neural Network

Responses under Random Delay

This article discusses the difficulties of remote closed-loop control, that is the difficulty different from the general control system lies in the uncertain network delay exist in system channel and feedback channel and which greatly reduced the system stability and improved control system design difficulty. This paper described problems on the network closed-loop control from uncertain network delay to includes network delay controller design method, and studied the impact of network transmission delay on the network closed-loop control system, proposed by predictive control based on neural network to solve feasibility of the network control system which existence random delay closed-loop control, and verified the validity of the method by simulation.

The authors wish to thank the cooperators. This research is partially funded by the Project funds in Shaanxi province University Student Innovation and Entrepreneurship Fund Project (S202010702109) and the Project funds in engineering laboratory project (GSYSJ2018013).

Biography: Xu Shu-ping, (1974-05-07), female (the Han nationality), Shaanxi Province, Working in Xi’an technological university, professor, the research area is computer control.