Implementation of AVCS using Kalman Filter and PID Controller in Autonomous Self Guided Vehicle

Abstract

Advanced Vehicle Control Systems (AVCS) is a key technology for Intelligent Transportation System (ITS) and Intelligent Vehicle Control System (IVCS). AVCS involves automatic steering, acceleration and braking control of fully autonomous vehicles. The unmanned control of the steering wheel is one of the most important challenges faced by the researchers. This paper proposes control architecture for automatic steering, acceleration and braking control of a self guided vehicle. Self guided vehicle is a line follower which tracks a black line on a white surface, through an array of infrared sensors. In line following, the readings that show deviation from the line are considered as lateral error and the proposed algorithm works towards minimizing the lateral error. Calculation of the lateral error and filtering the error values using Kalman filter is the first level of error calculation. Kalman filter protects the steering action from erroneous sensor values. PID control algorithm uses the filtered value and calculates the required steer to achieve the zero lateral error. The proposed adaptive speed control algorithm uses the speed boosting techniques for the longitudinal control of the vehicle. The experimental results show that the combination of Kalman filter with PID for lateral control reduces trajectory error to a minimum level and adaptive speed control algorithm for longitudinal control provides smooth accelerations over the entire track. Thus proposed algorithm gives better performance in both the lateral and longitudinal control.