  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.
