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The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor) of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

With the implementation of the strategy on Western development and west-east gas transmission project, the city gas has developed rapidly. How to use gas for higher energy saving and environmental protection and ensure the safety and reliability of the system has been a key problem and this brings opportunity to gas pressure regulating. Gas pressure regulating is one of the most important parts of city gas pipeline system and the key technology of highly efficient use of natural gas resources. The gas pressure regulating can ensure the output pressure in special range; when the pressure reaches a certain value, it can be effectively shut off to ensure safe delivery of gas. Currently, the accuracy of gas pressure regulators at home and abroad that adopt the traditional direct- or indirect-acting regulating principle is not high and susceptible to external environmental factors. The accuracy of gas pressure regulator affects the downstream gas supply system directly and reduces the efficiency of energy conversion, thus resulting in energy waste and environmental pollution and even possible supply security issues [

The current electric gas pressure regulator often adopts the conventional PID control mode to take drive control of the core part (micromotor) of electric gas pressure regulator [

Researches show that RBF [

The basic principle of traditional pressure regulating is mechanical balance. The electric gas pressure regulating system range from simple single stage [

Self-actuated regulator.

The principle diagram of electric gas pressure regulating system.

For static and dynamic analysis of the system, we need to model the system that mainly includes drive motor model, transmission model, and regulator valve model. According to the pneumatic dynamics, the transfer function of controlled object system of pressure valve [

Choose the stepper motor as the operator of the electric gas pressure regulator and use digital incremental PID control mode; the control algorithm can be expressed by the following difference equation:

Suppose there are m particles in a swarm; the information of

particle swarm initialization: determine the number of particle swarm, the initial position, speed, individual extremum, and swarm extremum;

map each individual component to the RBF network parameters and put them into the neural network to calculate the fitness value;

identify individual extremum and swarm extremum according to fitness value;

update the particle’s velocities and positions according to formulas (

if the number of iterations reaches a predetermined maximum or meet the performance requirements of minimum error, stop the iteration and output current swarm extremum as initial parameters of the RBF neural network or return to Step

RBF neural network-based PID control algorithm can solve the shortage problem of conventional PID control and PSO algorithm can solve the problem of the initial parameters of RBF network. The structure of PID controller of PSO-RBF neural network is shown in Figure

The PID control block diagram set by PSO-RBF online.

The controller is composed of two parts: the classic PID controller with

select RBF structure: initialize the input layer node number

determine the output weights, center node, and initial value of node basis-width vector of RBF neural network algorithm by using particle swarm algorithm;

take the Jacobian information into formulas (

make

The function of electric gas pressure regulator system can refer to formula (

To test the ability of the system response to a step input, RBF network structure is chosen as 3-6-1. Suppose

The step response of three kinds of algorithms.

The parameters adaptive tuning curve of PSO-RBF neural network PID controller is shown in Figure

The adaptive tuning curve of PSO-RBF neural network PID controller.

The minimum error evolution of PSO-RBF neural network is shown in Figure

The minimum error evolution of PSO-RBF neural network.

According to the actual needs of the regulator, electric pressure regulator will be formulated according to the input pressure, fast regulate valve opening, and make the outlet pressure stable in the range of errors. So we need to test the tracking performance of the control system. The tracking curves of three kinds of control algorithms are shown in Figure

The tracking curves of three kinds of control algorithm.

An in-depth research has been conducted on the mechanism of electric gas regulator about its nonhigh control precision and stability and a longer response time. It shows that the existing electric gas regulators widely use conventional PID control mode for it is simple and easy to be implemented. However, it is difficult to establish a precise mathematical model given that the pressure regulating system is time-varying and nonlinear. Meanwhile, the conventional PID controller parameters, usually set in line with human experience, cannot be modified online. So, it is hard to achieve the expected control effect to use conventional incremental PID control mode. Therefore, on the basis of the original incremental PID control mode, the RBF neural network based on PSO optimization is utilized to determine the initial parameters of the network, and then again set PID parameters online; thus an improved PID intelligent control algorithm suitable for electric gas pressure regulator is established. Theoretical analysis and experimental results demonstrate that the algorithm improves the control accuracy, response speed, and tracking performance of electric gas pressure regulator and can be widely applied to the field of electric gas pressure.

The authors declare that there is no conflict of interests regarding the publication of this paper.

This paper was supported by the National Natural Science Foundation of China (no. 10C26215113031) and the Scientific and Technological Project of Chongqing (no. CSTC2010gg-yyjs40010).