The practical implementation of the ACC system using the fuzzy PID control methods in the application to the real-time single board computer based robotic vehicle is presented in this paper。 Furthermore, the results of fuzzy PID controller for the ACC system are compared against those of the conventional PI controller (Shakouri et al, 2012)。

Conventional PID controller is commonly utilized in industry for various control applications, because of their simple structure, ease of design, and low cost in implementation。

978-3-902823-43-4/2013 © IFAC 192 10。3182/20130828-3-UK-2039。00075

However, it may not offer satisfactory performance if the system is highly nonlinear。 While, fuzzy control is capable of handling nonlinearities and uncertainties by using fuzzy    set

transfer function of the system is obtained such that it would present the best estimation of the robot:

theory。 Therefore, a better control performance can be obtained by combining these two techniques together which is  so-called  the  fuzzy  PID  controller  (Feng,  2007)。 Fuzzy

0。000197s2 0。17804s 3。75721



s2 2。59525s 3。87288

logic control is a widely adopted technique for developing ACC systems, due to its well-known ability to handle nonlinearity and uncertainties (Naranjo et al。, 2006; Naranjo et al。, 2003; Masouminia, 2011; Tsai et al。,  2010)。 Fuzzy logic control is a rule-based decision method, which uses defined rules to control a fuzzy system based on the current values of input variables。 Linguistic variables, membership functions, and rules are the three main parts that build a fuzzy system (National Instrument Corporation, 2009)。 A fuzzy system utilizes a mode of approximate reasoning allowing it to make decisions based on inaccurate and incomplete information in a way similar to human beings (Harisha et al。, 2008)。 A fuzzy system does not require the precise mathematical or logical models。 Fuzzy control provides a flexible tool to model the relationship between input information and control output and is distinguished by its robustness with respect to noise and variation of system parameters。

The remainder of the paper is organised as follows: Section 2 describes the hardware characteristics and the system identification。 The ACC architecture based on the fuzzy PID controller is presented in Section 3。 Estimation of the unmeasured parameters using a Kalman filter is described in Section 4。 Section 5 presents the results of experiments and simulations, and implementation into curriculum is described in Section 6。 Finally, conclusions are drawn in Section 7。

Fig。1。 NI LabVIEW starter kit robot

2。HARWARE CHARACTERISTIC AND SYSTEM IDENTIFICATION

This application uses two NI LabVIEW starter kit robots。 The robots can be programmed either by using the high-level LabVIEW function, or by using the LabVIEW  FPGA module。 The robot ‘follower’ is equipped with ultrasonic sensors for velocity and distance tracking of the ‘leader’。 In order to design a controller, the model  describing the dynamic behaviour of the system, i。e。 starter kit robot, needs to be identified。 The system identification process involves acquiring data from a real-world system then numerically analyzing stimulus and response data to estimate the parameters of the plant。 The LabVIEW System Identification Toolkit is used to estimate the model reflecting the dynamic behaviour  of  the  system  based  on  the  acquired  data。 The

where vsp and vsp respectively denote  the robot’s velocity and velocity setpoint (input signal)。

The simulated response obtained from the estimated transfer function (1) and response of the real system have been compared and illustrated in Fig。 2。 The  root  mean square error (RMSE) between the two responses is 0。0237 (m/s)。