Design of intelligent PWM controlled locomotive brake control unit

1 Introduction

With the vigorous development of my country's national economy and the improvement of people's living standards, the passenger and freight volume of railways will increase, and the train traction weight and running speed will continue to increase. The development of high-speed passenger and heavy-load freight trains has put forward higher and newer requirements for train braking systems.

Developed countries abroad all use microcomputers to apply advanced control theories to achieve precise control of locomotive brake cylinders. However, the DK-1 and JZ-7 brake machines widely used on Chinese locomotives can only achieve some simple logic control functions for locomotives, and cannot achieve closed-loop control of locomotive brake cylinders and equalizing air cylinders, which is difficult to meet the needs of locomotive brake control. With the increasing maturity of the application of electronic technology and microcomputer control technology, it is necessary to apply modern electronic technology and advanced control theory, and use the powerful functions of microcomputers to achieve precise braking of locomotives. At present, most railways in various countries in the world still use air brakes. To achieve control of gas pressure, especially small flow pressure control, obvious nonlinearity and uncertainty should be considered. In addition, the uncertainty of the load leads to the uncertainty of the entire system model. The classical control method and the modern control theory that relies on specific mathematical models are difficult to achieve the requirements of system control. In this case, combining intelligent control methods with conventional control methods is expected to achieve better control effects.

This paper introduces a design and implementation method of a locomotive brake control unit based on intelligent pulse width modulation (PWM) control. PWM control is implemented on the high-speed electric control valve of the brake cylinder, that is, the opening and closing time of the high-speed electric control valve within a certain period is controlled by adjusting the duty cycle of the signal.

By establishing the fuzzy control rules of the locomotive brake cylinder and using fuzzy reasoning to realize PID control, the precise braking of the locomotive is realized. This effectively solves the problem that the current DK-1 and JZ-7 brakes in my country cannot achieve precise braking, and has a great role in promoting the safe operation and informatization of my country's locomotives.

2 System Hardware Structure

The locomotive brake control unit (Brake Control Unit, BCU) is mainly divided into the following parts: analog input, analog output, digital input, digital output, PWM output, microprocessor part and communication with peripheral parts, etc. The overall structure of the system is shown in Figure 1.

The analog input part mainly includes sensor analog signal preprocessing and A/D conversion. Signal preprocessing mainly converts the 4-20 mA current signal obtained from the sensor into the voltage signal required for A/D conversion. Through processing, we can get the cylinder pressure. The accuracy of A/D conversion is directly related to the accuracy of cylinder pressure control. In order to meet the needs of control, a 16-bit A/D conversion chip is selected in this system. The sampling experiment shows that the sampling value deviation is very small and within the allowable error range.

The microcomputer processing part actually contains two microprocessors, one is a single-chip microcomputer and the other is PC104. They realize different functions, and they realize high-speed data communication through dual-port RAM. The single-chip microcomputer mainly realizes analog A/D conversion control, D/A conversion control and intelligent PWM control. Because PC104 is powerful, it can realize more powerful data processing functions. PC104 mainly outputs digital quantities after data processing of the obtained digital input. In addition, through the fast data processing of PC104 and the powerful functions of software, the brake control unit also has the functions of locomotive brake monitoring and fault detection, diagnosis, display, alarm, recording, single-machine automatic testing, etc. In this system, the precise control of cylinder pressure is completed by the single-chip microcomputer. PC104 obtains the pressure value required by the cylinder by processing various signals such as analog and digital signals. The single-chip microcomputer obtains the pressure value through the dual-port RAM and uses intelligent PWM control to realize precise control of pressure. This part will be introduced in detail in the following chapters.

3-segment control

In order to achieve both precise control of the locomotive brake cylinder and rapid target value, we implement segmented control of the cylinder pressure. The single-chip microcomputer has 4 switch outputs, corresponding to the intake valve, exhaust valve of the brake cylinder and the intake valve and exhaust valve of the balancing air cylinder. Output 1 represents opening the valve, and 0 represents closing the valve. We use pt to represent the pressure target value, pi to represent the current cylinder pressure value, and E to represent the deviation value. Therefore, E=pi-pt. M1 and M2 represent the absolute value of the pressure deviation, where M1>M2, M1 represents a value close to the target value, and M2 represents the maximum allowable error. The segmented control rules are shown in Table 1.

4 Intelligent PWM control

4.1 Introduction to Intelligent PID

PID control is one of the earliest developed control strategies. It is widely used in process control and motion control due to its simple algorithm, good robustness and high reliability. It is especially suitable for control systems that can establish accurate mathematical models. However, since the actual industrial production process is often nonlinear and time-varying and uncertain, it is difficult to establish an accurate mathematical model, so it is difficult for conventional PID controllers to achieve ideal control effects. [page]

In recent years, intelligent control has made great progress both in theory and in technical application. New methods that combine intelligent control methods with conventional PID control methods have emerged, forming many intelligent PID controllers. These intelligent controllers not only have the ability of self-learning, self-adaptation, and self-organization, but also have the characteristics of conventional PID controllers, such as simple structure, strong robustness, high reliability, and familiarity with on-site engineering designers. At present, there are four main types of intelligent PID control: expert-based intelligent PID control, fuzzy-based reasoning-based PID control, neural network-based PID control, and genetic algorithm-based PID control.

In this system, PID control based on fuzzy reasoning is used to achieve precise control of the brake cylinder pressure of the locomotive. PID control based on fuzzy reasoning is to use the theory and method of fuzzy sets to summarize the setting experience and technical knowledge of operators or experts into a fuzzy rule model, form a query table and analytical expression of the microcomputer, and use fuzzy reasoning to achieve PID control according to the actual response of the system. On the basis of the PID control algorithm, the deviation E and the deviation change rate Ec at the sampling time are added. The idea of ​​fuzzy self-correction of parameters is to determine the direction and size of the correction of the three parameters kP, k1, and kD according to the response of the controlled object at the sampling time E and Ec. The algorithm process is to use the corresponding rule set to fuzzify the control index, and then use it to match the fuzzy rules in the knowledge base. If a rule is matched, the result part of the rule is executed to obtain the corresponding parameter correction value. Its structure diagram is shown in Figure 2.

4.2 Fuzzy PID Controller Design

The PID algorithm of the control system determines the size of the control quantity based on the proportional value, integral value, and differential value of the difference between the pressure target value and the actual value. The formula is:

In the formula, e(t) and e(t-1) are the sampling deviation values ​​of the tth and t-1th times respectively; pout(t) is the output value of the control quantity of the tth time; kP, kI, kD are the proportional coefficient, integral coefficient and differential coefficient respectively. The appropriate kP, kI, kD parameters are directly related to the control accuracy.

According to the theory and method of fuzzy mathematics, the debugging experience and technical knowledge obtained on site are summarized into fuzzy reasoning rules in the form of IF (condition) and THEN (result), and these fuzzy rules and related information (such as initial PID parameters) are stored in the computer. According to the response of the detection loop, the deviation E and the rate of change Ec at the sampling time are calculated, and fuzzy reasoning and fuzzy calculation are used to obtain kP, kI, and kD at that moment, so as to achieve the best adjustment of PID parameters.

Fuzzy-PID is to obtain the PID parameter pre-setting values ​​k′P, k′I, k′D according to the on-site debugging, and then use the fuzzy rules to adjust the three correction parameters △kP, △kI and △kD of the PID controller in real time online to achieve the optimal control of the pressure. The input and output variables of the fuzzy controller are both precise quantities, and the fuzzy reasoning is performed on the fuzzy quantity. Therefore, the controller must first fuzzify the input quantity. In the designed Fuzzy-PID controller, the language values ​​of the input and output variables are divided into 7 language values: {NB, NM, NS, 0, PS, PM, PB}, which represent negative large, negative medium, negative small, zero, positive small, positive medium and positive large respectively. The membership function adopts a highly sensitive trigonometric function, as shown in Figure 3.

The basic domain of the deviation E is [-5 kPa, +5 kPa], the basic domain of the deviation change rate Ec is [-0.5, +0.5], the basic domain of △kP is [-1, 1]; the basic domain of △kI is [-0.002, 0.002]; the basic domain of △kD is [-1, 1]. The fuzzy quantities of the above variables are E, Ec, △kP, △kI and △kD, and their domains are all [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6]. The quantization factors of the input quantities E and Ec are: ke=1.2, kec=12.

By summarizing the technical knowledge and practical experience of engineering designers, the fuzzy control tables for the three parameters kP, kI, and kD were obtained, as shown in Table 2, Table 3, and Table 4.

In this system, according to the deviation E and the deviation change rate Ec, the corresponding language value is obtained. According to the setting rule table in Table 1 to Table 3, the fuzzy values ​​of the three correction parameters △kP, △kI, and △kD are obtained through fuzzy decision-making by formula method. Then △kP, △kI, and △kD need to be defuzzified to obtain accurate values. There are several methods for defuzzification. Generally, the centroid method is more appropriate. The formula can be obtained:

Where u is the output after fuzzy judgment, uN(xi) is the membership function, and xi is the element in the domain. [page]
Then the correction parameters are obtained:

Where ku is the proportional factor of the output:

After the above process, three parameters of the fuzzy controller can be obtained:

5 Software Implementation

In the system control circuit, the single chip microcomputer uses AT89C55 from ATMEL, and the program is written in C51. The main modules of this system include the main program, T0 interrupt subroutine, fuzzy PID algorithm subroutine, etc. The main program flow is shown in Figure 4, and the program flow of the fuzzy PID algorithm is shown in Figure 5.

The main program performs a series of initializations and then waits for interrupts in a loop; T0 generates a 2 ms timing interrupt, and the T0 interrupt service subroutine counts the number of interrupts. Every 50 interrupts (100 ms) is a control cycle. In each control cycle, the current cylinder pressure sampling value is read in, and the fuzzy PID subroutine is called to accurately control the cylinder pressure.

6 Conclusion

The brake control unit based on intelligent PWM control has the following features:

(1) The system makes full use of the single-chip microcomputer and the hardware and software of PC104. The system has a simple structure, high reliability and strong anti-interference ability.

(2) The system uses the fuzzy PID algorithm to generate PWM signals from the microcontroller to achieve pressure control of the locomotive brake cylinder and the balancing air cylinder. The system has a wide pressure regulation range, good dynamic and static performance, high control accuracy, and strong adaptability.

The debugging of the brake machine in Zhuzhou Electric Locomotive Plant shows that the control accuracy of the brake control unit based on intelligent PWM control on the brake cylinder and the balancing air cylinder of the locomotive brake can reach ±0.5 kPa, which can meet the needs of electric locomotive braking control.