论文标题
主角：使用PID控制器和深度强化学习的自我平衡机器人
Epersist: A Self Balancing Robot Using PID Controller And Deep Reinforcement Learning
论文作者
论文摘要
两轮自动平衡机器人是逆摆的一个示例，是一种固有的非线性，不稳定的系统。提议的框架“主持人”的基本概念是通过提供强大的控制机制，比例积分衍生物（PID）和增强构（RL）来克服最初不稳定系统的挑战。此外，雌激素中的微控制器Nodemcuesp32和惯性传感器采用更少的计算程序来提供有关车轮旋转到电动机驱动器的准确指导，这有助于控制车轮并平衡机器人。该框架还由PID控制器的数学模型和新型的自我训练的Actor-Critic算法作为RL药物。经过多次实验，将控制可变校准作为基准值，以达到静态平衡的角度。这个“主持人”框架提出了PID和RL辅助功能原型和模拟，以更好地实用。
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.
