Abstract: Advanced control concepts present a teaching challenge - even at master level students benefit from these concepts being implemented and demonstrated on real hardware, rather than simply modeling the plant, applying control strategy and tuning。 This paper provides reference materials (both theoretical and test results), to be used in control teaching and assessment using a laboratory experiment, with a real- time single board computer based robotic vehicle (National Instruments Robotics Starter Kit)。 This paper explores the practical implementation of the ACC system through use of a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit)。 The ACC algorithm based on fuzzy PID control is deployed on a field programmable gate array (FPGA), included in the robot’s architecture。 This robotic vehicle is programmed using a graphical programming language  (LabVIEW)。 A Kalman filter is used to estimate the unmeasured parameters while implementing the control algorithm in the hardware (the real robot)。 The results obtained are compared for the simulation model and the real robot, respectively。 The experiment demonstrates clear correlation between theoretical expectations and real-life system performance and at the same time offers a novel idea how to deliver this advanced control concept in an applied and visual manner。84379

Keywords: Adaptive cruise control system, Fuzzy control, PID controller, Kalman filter, Robot control, Research Informed Teaching, State estimation

1。INTRODUCTION

Many universities tend to treat research and teaching as two separate entities and there is little integration of research into teaching。 However, developing the links  between research and teaching has been recognised as a priority。 There are different interpretations of the link between teaching and research in universities and how they contribute to the student learning (Brew, 2003; Senaratne and Amaragtunga, 2006)。 Undergraduate research initiatives in Engineering are usually seen as enquiry-based learning。 Enquiry tasks could be designed to explore existing knowledge and build new knowledge through analysis and experimentation。 However, engineers must be able to apply concepts that have  been learnt at university in order to solve problems outside of the experience they had in the course。 Enquiry- and problem- based learning may lead to constructing only subject knowledge (Mills and Treagust, 2003)。 Thus, research methods and skills should not only be progressive: a variety of methods and skills should be delivered。 One interesting example was adopted at Heriot-Watt University, where students contribute to the design of labs experiments, carry them out, acquire the data, analyse it and compare the results at different levels (Jenkins and Zetter, 2003)。 Following that example, a number of our postgraduate students (MSc and PhD levels) were employed to develop research informed teaching materials including hands on  experiments and virtual learning to enhance teaching of control based subjects。 The work presented in this paper has been fully developed by a PhD student and it is informed by his own research (Shakouri et al, 2012)。 The material is to be used in a post

graduate Mechatronics module were students learn robotics, sensing methods, control and artificial intelligence。 In that sense, Adaptive cruise control (ACC) system, cruise control (CC) system and emergency stop are the more common longitudinal applications, which have been studied in  the field of the robotics and intelligent transportation system (ITS) (Gerdes, and Hedrick, 1997) 。 The ACC system is an extension of the cruise control system, which not only controls the velocity of the vehicle but also is it capable of controlling the distance between the leader and follower vehicles to retain a safe distance。 A comprehensive study on the ACC was carried out by (Xiao and Gao, 2010)。 Several model-based control approaches and architectures have been suggested and developed for designing ACC systems from the classical to the nonlinear advanced control methods, such as proportional-integral-derivative (PID) control and linear quadratic control (LQC) taking the gain scheduling approach (Shakouri et al。, 2011; Riis, 2007) model predictive control (MPC) and nonlinear MPC (NMPC) (Shakouri et al, 2012; van den Bleek, 2007) and the sliding mode controller (Zhou and Peng, 2004) among others。 机器人运动模糊逻辑控制英文文献和中文翻译:http://www.youerw.com/fanyi/lunwen_100015.html