Applying a Kalman filter to control a servo motor : controlling a toy car with Raspberry Pi 4

Abstract

Remote control systems have traditionally relied on radio technology, requiring the control device to be within a certain range to connect to the control station via radio signals. Modern approaches using network or internet connectivity overcome these limitations and enable high-bandwidth services such as camera streaming and remote data transfer via internet protocols. However, these methods introduce trade-offs, particularly increased latency, which can be critical for real-time applications such as self-driving cars. This thesis aimed to research and implement a control system for a radio-controlled toy car with two brushed motors through an internet-based REST API with a Raspberry Pi 4 serving as the central controller. Incoming commands were processed using a Kalman filter to reduce noise before adjusting the servo motors in the steering and transmission systems, ensuring smooth and accurate movement. Experimental results confirm that the servo motors maintain minimal response delay and high accuracy under remote control, demonstrating potential applications in real-world projects such as unmanned vehicles or boats, robotic systems, and self-driving cars.