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.
翻译:双轮自我平衡机器人是反弹钟的一个例子,本质上是一个非线性不稳定的系统。拟议框架“Epersister”的基本概念是,通过提供稳健的控制机制、比例综合衍生工具(PID)和强化学习(RL)来克服对准最初不稳定系统的挑战。此外,微盘控制器NodeMCUESP32和Epersister的惯性传感器采用较少的计算程序,对发动机驱动器的轮子旋转给予准确指示,这有助于控制轮子和平衡机器人。这个框架还包括PID控制器的数学模型和作为RL代理的新型自我训练优势的演员-弧算法。经过几次实验,控制变量校准作为达到静平衡角度的基准值。这个“Epersic”框架提出了PID和RL辅助功能原型和模拟,以便更好使用。