Intelligent Control Algorithms Research With Freescale Smart Car

Intelligent car driving will become the trend in the future, two self-balancing intelligent vehicles has become a popular means of transport in recent years. With its lightweight shape, easy to handle, and pure electric drive features, the two self-balancing car effectively alleviated the difficult of people’s traveling, better advocated the idea of low-carbon travel in our social. But because the two self-balancing car only had a single axis, it faced the problem of insufficient stability in the process of moving, this increased the complexity of the control principle, and provided a better verification platform for the theory of intelligent control algorithm.In this thesis, it mainly researched the applications of intelligent control algorithms in two self-balancing upright vehicle, studied and simulated the three control algorithm by modeling, and used the PC for control algorithms debugging online and studying corresponding performance comparison. The main contents are as following:(1)According to the principle of an inverted pendulum, the paper built a microcontroller of Freescale MK60DN512VLQ10 as the core of the intelligent vehicle control system, mainly included six modules of power management, path identification, motor drive, speed detection, the upright control, the wireless communication and so on.(2)The paper designed the control algorithm on the platform. Also it studied the theory and design processes of incremental PID control and fuzzy control. On the basis of PID control algorithm, based on practical experience, the paper established a fuzzy control rule table matching the system in line for the fuzzy control table of three PID parameters Kp、Ki and Kd, so it designed a fuzzy adaptive PID controller for the system.(3)The paper built a mathematical model for the DC motor of the system, and controlled a DC motor respectively and completed corresponding software design with incremental PID control, fuzzy control and fuzzy adaptive PID control algorithm. It selected large S curve and right-angle turn to represent the path, and used ATLAB/Simulink for the three control algorithms to simulate and analyze comparatively, so the paper obtained the optimal control algorithm for two-wheeled self-balancing upright vehicle.Finally, the paper designed the PC architecture based on LABVIEW. It debug the lower machine by Bluetooth, which was an ideal platform for remote control. It debugged the three algorithms online through the platform, the results showed that the steady-state error of the adaptive fuzzy PID system was the minimum. And the adaptive fuzzy PID system had better dynamic performance, the anti-interference ability and traffic adaptability were both better.