Uncertainty latency of the remote closed-loop control system in information transmission through Internet, Analysis delays how to influence closed-loop control system. Based on predictive control method of neural network, research the application of closed-loop control system control methods under a random network delay. Simulation results show that: This method is able to reflect and predict the delay characteristics of between network path represented by the measured data, and can be replace actual network to research in application based on Internet closed-loop control system; the methods used is fast and accurate, it can be used for online learning network model and predict the network delay value, provides a new way of remote closed-loop control based on Internet.

The remote control system is an integration of control technology and network communication technology, it applications in many fields more and more common as ocean development, space station maintenance, remote surgery, virtual reality in recent years, and stable, fast, accurate is the highest target remote control system pursue[

Closed-loop controller is to control by the disturbances of feedback, which is by comparative behavior of the system output and the deviation between expectations to make the appropriate control action to eliminate the bias in order to achieve the desired system performance. It has the ability to suppress interference, is not sensitive to changes in device characteristics, and can improve the response characteristics of the system.

The delay phenomenon exist in the field of remote control is a common problem exists in the remote closed-loop control applications. Delay does not only exist in the before control channel of system and in feedback channel. The delay in before control channel Makes the control signal unable to act on the controlled object, the delay in the feedback channel makes the controller can not found the change of controlled object immediately[

Delay value influence by the inherent properties of control information transmission network such as the network structure, the amount of data transmission, the transmission timing and transmission agreements and other factors[

Delay has a lot of influence on real-time, accuracy and stability performance of the remote control system. Kinetic of equation a single-link robotic arm second-order remote control system as follows[

Among them, ^{φ}represent the angle of robot arm; ^{μ} represent the behalf of DC motor torque. simulink block diagram of Mechanical Arm shown in Figure

Simulink Block Diagram of Mechanical Arm

Since the forward channel and feedback channel in remote control system is generally the same physical link, the article assumes that the forward channel and feedback channel delay values equal. First set the delay time Delay to 0, that is without delay, adjust the PID parameters (how much) to get the response curve satisfied. Secondly, to maintain the constant of the PID parameters, increase the network delay value gradually when the network delay value is set to 0.02s to get feedback curve in Figure

The Influence on the Stability of the Control System from Remote Control Delay

There are a lot of research on the stability improvement of the remote control system, in 1965, Ferrel put forward network delay problem of need to pay attention to time-varying in the network control [_{sc}_{ca}_{2}/_{∞} system state observer design method. Literature[

Response after regulate PID parameters at 0.06 seconds time delay

The adjustments of the PID control parameters need to be dynamic adjustment constantly with the size of the control system delay and values and other parameters of systems, which makes controlled object in the work environment unknown to dynamically adapt adjust PID values become remote intelligent control systems that are experiencing another problem. This paper based on the method of modify the control parameters of PID values to improve the stability of the remote control system, and propose intelligent remote control system design methods with adaptive function under a random delay based on neural network theory.

Remote control single-link manipulator, set the sampling period in figure 1 is 0.05s, and take the delay value of 0.05s, the control information in time k transmitted to the controller after 0.05s, as opposed to the system sampling time 0.05s, the controller receives status information at the moment of k has pass a sampling point, the state of the system has become the state in time k +1, that is state of the k time fed back to the PID controller at time k +1, the PID controller for time k, the state at time k+1 has not yet come, but this time system status values at k-1 after a sample time delay before it is passed controller, therefore, the controller can only decision at time k should be imposed control value u (k) based on the state of the k-1 times, and this control value can be a real work on the system after a time delay, while at the time k +1 and the state of the system has been turned into a time k+1 the state of X(k+1), while u (k) produce at the state time k-1, so u (k-1)grieved and u (k +1) required difference two sampling cycles. In these two sampling cycle, the state of the system state transition, that is x (k-1) transfer to the x (k+1), x (k-1) and x (k+1) often is different lead to u (k-1) and u (k +1) is different. In other word, the system control value produced offset and the greater delay the greater offset, which is the root source of result in deterioration of the system closed-loop control performance and even instability.

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

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 [

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 Figure

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 Figure

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 in Figure

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

Build the Simulation block diagram shown in Figure

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 different from the general control system of the difficulty lies in channel and feedback channel network of system existence uncertain delay which greatly reduces the stability of system and improve the design difficulty of control system. This paper elaborated network closed-loop control problems form uncertain network delay to includes network delay controller design method, and study 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.

National Network Engineering Testing LabFund project(GSYSJ2018012).