A Design of Automobile Cruise Control System Based on Fuzzy PID Chengqun QIU School of Physics and Electron University of Yancheng Teachers Yancheng City, Jiangsu, 224051, China
[email protected] automobile driver set a speed value input to controller, when the wheel speed sensors take speed the actual value into controller, PID will get the set speed value and the actual value of the deviation between the speeds. The proportional control of controller is according to the size of the speed value deviation and output corresponding control quantity, to control the launch the throttle valve, which makes the speed approaching have set the speed limit value. The integral control of controller is by strengthening the control and reducing the speed of the deviation of the cumulative speed deviation, which makes the speed keep constant and steady working state. The differential control of controller plays a part in the prediction. When the controlled object is the complex characteristics, and has strong nonlinear or time variation, the conventional PID controls parameter will make the control system vibrate when the adjustment is inappropriate, so make the work of the state not stable, and the control effect is poor performance, hard to realize the effective control. [3-9]
Abstract—Coping with the problem of the automobile cruise control system, a method based on fuzzy PID is proposed. Signal input device, CCS ECU and actuator etc are used to construct the control system. The PID fuzzy control algorithm is proposed. The fuzzy rules and proportion factor of fuzzy output are optimized in MATLAB. It retains the advantages of conventional fuzzy control and meanwhile, it is improved the performance of cruise control system. It proposes fuzzy PID control as control basis. It builds the automobile longitudinal force model of PID and simulated the validation and control effect of system in MATLAB. The results analysis shows that increased in the average response time. Automobile experiment indicates that the system work effectively and stably. Keywords—control system; speed; MATLAB; fuzzy PID
Cruise Control System (CCS). Automobile cruise control system can reduce vehicle exhaust pollution and improve the dynamic performance and automobile ride comfort, etc. Automobiles cruise control system has the very strong nonlinear and uncertainty, and the traditional proportionalintegral-derivative (PID) can't meet the actual speed cruise control requirements. Automobile cruising system research key is to find suitable cruise control algorithm, make fixed speed cruise control performance is good. In order to improve the precision and stability of the vehicle CCS, to optimize the fuzzy control rules and online output scale factor, and can both retain the advantages of the traditional fuzzy control, and can effectively improve the cruise control system of the quality control, so is put forward based on MALTLAB and fuzzy PID adaptive fuzzy control method. [1-6] I.
B. Design of fuzzy controller The template is used to format your paper and style the text. The image is affected by many factors, such as: optical We put the speed error absolute value /E/ of the cruise control system and the velocity error rate in the absolute value /EC/ as input language variables of fuzzy controller. With their range change to define the fuzzy set theory on the domain. [2-6]
AUTOMOBILE CRUISE CONTROL SYSTEM BASED ON MATLAB AND FUZZY PID
/E/ = {0, 1, 2, 3, 4, 5}
(1)
/EC/ = {0, 1, 2, 3, 4, 5}
(2)
The corresponding fuzzy subsets is：
A. Traditional PID control in the automobile cruise control CCS is comprised of signal input device, cruise control electronic control units and actuators, etc. When the automobile all sorts of relevant sensors and switches take signal into CCS electronic control units, electronic control units will calculate engine the throttle valve, and control actuator work, to automatically adjust the engine the throttle valve. PID control is proportional-integral-derivative. It is based on the actual automobile driving and deviation between the set speed, reference for the past, present, and future conditions such as estimates, to realize the system parameters of the same automobile cruise control. In the course of driving automobiles, ____________________________________ 978-1-4799-3197-2/14/$31.00 ©2014 IEEE
/E/ = {Zero (Z), Small(S), Middle (M), Big (B)}
(3)
/EC/ = {Zero (Z), Small(S), Middle (M), Big (B)}
(4)
Define three output language variables: 1) KP` as scale coefficients adjustment parameters; 2) TI` as integral coefficients adjustment parameters; 3) Td` as differential coefficient adjustment parameters.Defining the corresponding language respectively for value is: KP`= {Zero (Z), Small(S), Middle (M), Big (B)}
(5)
TI` = {Zero (Z), Small(S), Middle (M), Big (B)}
(6)
Td`= {Zero (Z), Small(S), Middle (M), Big (B)}
(7)
We formulate control rules from various aspects like the system stability, the response speed, overshoots and steady precision and so on. What making the rules of the fuzzy system should pay attention to is when the speed error value is smaller, we need to increase the function of proportional control, reduce the integral control function, but the system can have the certain error. Fuzzy controller has two input variables (/E/ 、 /EC/) and three output language variables (Kp`、Ti` 、Td`). Then we can induce the rules of the fuzzy control table. We membership functions and parameters adjustment rules input fuzzy logic toolbox, and then we can complete the design of fuzzy controller, also get PID parameters fuzzy matrix form. When running, the control system will result in the fuzzy rule data processing, and then complete the PID parameters adjustment. [5-9] The plate area in image extracted by edge has significantly When the velocity error absolute value and the velocity error rate in the absolute value added to the input fuzzy controller, which will be transformed into the fuzzy input language variables via the fuzzy process, and it gets fuzzy output according to the fuzzy reasoning rules, finally after solution the output of the process of fuzzy control is accurate, their value becomes between 0~1. In the practical application of PID controller， to get real PID parameters Kp, Ti, Td，it needs multiplied by proper proportion factor Gp, Gi, Gd. II.
Input language variables range is [0~5], the output variables of the language range is [0~0.99]. In order to simplify the design of the system, this paper uses triangle membership functions and modifies the name of fuzzy subsets at last. The system is based on summarized the table of fuzzy control rules to list in article 16 control statements, and then will control statements input rules editor. Editing control rules, and then opening the rules for observation. When the input variables take different values, the system will be based on gravity method to get each of the input variables control volume ， then get proportionintegral-differential coefficient set-up parameters. [1-6]
Fig.1. Control surfaces of Kp ′.
DESIGN OF AUTOMOBILE CRUISE CONTROL SYSTEM BASED ON MATLAB AND FUZZY PID
MATLAB is produced by the company Math Works, used for algorithm development, data visualization, data analysis and numerical calculation of the advanced technical calculation language and interactive environment. Designers can use a variety of ways in MATLAB to generate and edit fuzzy reasoning system, with command line function to realize control system function. Established a PID controller based on the basic principle of PID control, in order to achieve improved and optimized control effect, in the process of fuzzy control we adjust PID control parameters for real-time. Taking the actual speed and setted speed as inputs, after the proportion, integral, differential link of PID controller, it outputs driving force. We run the software MATLAB and open the fuzzy logic edit window. In fuzzy PID control of automobile cruising system, the input language variable of fuzzy controller is the speed error absolute value/E/and the velocity error rate in the absolute value/EC/; Output language variables are Kp` 、 Ti` and Td` ， which are on-line adjustment parameters of PID control coefficient. Control surfaces of Kp` is shown in figure 1. Control surfaces of Ti` is shown in figure 2. Control surfaces of Td` is shown in figure 3. Adding the input and output language variables in the fuzzy logic editor window and modifying their name. The system is based on the defined input and output language variables and their fuzzy subsets, to complete the edit of the membership function of variables.
Fig.2. Control surfaces of Ti ′
Fig.3. Control surfaces of c. Td ′
the fuzzy PID control speed will be stable in 53s. But a single PID control speed appears overshoot in the 75s and in the 130s appears maximum overshoot, and it will be stable in 330s; when cruise speed is close to 80 km/h, the fuzzy PID control speed will be stable in 65s. But a single PID control speed appears overshoot in the 82s and in the 123s appears maximum overshoot, and it will be stable in 310s; when cruise speed is close to 100km/h, the fuzzy PID control speed will be stable in 80s. But a single PID control speed appears overshoot in the 78s or so and in the 122s appears maximum overshoot, and it will be stable in 340s. Automobile cruise control system based on fuzzy PID has benefited from the MATLAB software heavily, MATLAB has powerful functions, which includes the numerical computation and symbolic computation. The calculation results and programming are visual. Automobile experiment indicated that the automobile error effectively with a good application prospect.
Fig.4 Model of system control
Opening the surface for observation and observing three control surface of output language variable, PID parameters adjustment coefficient (Kp` 、 Ti` 、 Td`) control surface. Control surface is as nonlinear, which shows that fuzzy control itself is nonlinear, then the system saves the fuzzy control system has been established to working directory. Fuzzy controller set-up on three parameters of PID control, then the fuzzy control and PID control is effectively combined to build fuzzy PID control system. III.
ACKNOWLEDGMENT The preferred spelling of the word “acknowledgment” in The work was supported by the Natural Science of Foundation of Jiangsu provincial Department of Education of China (Project No. 12KJD510015) and Natural Science of Foundation of Yancheng Teachers University (Project No. 13YCKL003).
ALGORITHM SIMULATION AND SYSTEM TEST
REFERENCES
Establishing a automobile dynamic system model and then to establish fuzzy PID control system model and last to get automobile cruise control system model through the operation of the combination of the way in the MATLAB environment. As it is shown in figure 4. Through the simulation to verify the design of cruise control algorithm, the simulation program using M language. It has certain overshoot which the traditional single PID control through the simulation easy to get, and the response speed is slower. When using MATLAB and fuzzy PID control, the system overshoots decreases, and response speed, the control system has a good stability. The choice of the testing road surface dose accord with the standard road of the national real automobile test of PASSAT automobile (1.8 MT) of Shanghai Volkswagen is chosen to test. We install the cruising controller in the automobile, respectively set cruise speed for 40, 60, 80 and 100 km/h, then we test the two control system, one is the conventional fuzzy control system and the other is based on MATLAB and fuzzy PID control cruise system. If cruise speed is close to 40 km/h, the fuzzy PID control speed will be stable in 38s. But a single PID control speed appears overshoot in the 80 s or so and in the 120s appears maximum overshoot, and it will be stable in 300s; when cruise speed is close to 40 km/h,
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