2022/07/23 by Ghanta Sai Krishna, Krishna, Ghanta Sai, Dyavat Sumith +3
Computer Science · #Adaptive Dynamic Programming Control #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2207.11431
openalex publication_date 2022/07/23 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
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.