Epersist: A Self Balancing Robot Using PID Controller And Deep Reinforcement Learning

TL;DR

Epersist combines PID control and deep reinforcement learning to develop a robust, self-balancing two-wheeled robot with efficient computation and accurate balancing capabilities.

Contribution

The paper introduces a novel framework integrating PID and a self-trained RL algorithm for improved self-balancing in robots.

Findings

Effective balance achieved with combined PID and RL control

Reduced computational load with NodeMCU ESP32 and sensors

Benchmark calibration improves stability accuracy

Abstract

A two-wheeled self-balancing robot is an example of an inverse pendulum and is an inherently non-linear, unstable system. The fundamental concept of the proposed framework "Epersist" is to overcome the challenge of counterbalancing an initially unstable system by delivering robust control mechanisms, Proportional Integral Derivative(PID), and Reinforcement Learning (RL). Moreover, the micro-controller NodeMCUESP32 and inertial sensor in the Epersist employ fewer computational procedures to give accurate instruction regarding the spin of wheels to the motor driver, which helps control the wheels and balance the robot. This framework also consists of the mathematical model of the PID controller and a novel self-trained advantage actor-critic algorithm as the RL agent. After several experiments, control variable calibrations are made as the benchmark values to attain the angle of static equilibrium. This "Epersist" framework proposes PID and RL-assisted functional prototypes and simulations for better utility.