Autotuning PID control using Actor-Critic Deep Reinforcement Learning
This work addresses the specific problem of adaptive control for agricultural robots, but it is incremental as it applies an existing reinforcement learning method to a new application domain.
The paper tackled the problem of tuning PID parameters for a robot arm used in apple harvesting by implementing an Advantage Actor-Critic (A2C) deep reinforcement learning algorithm in simulation. The results showed that the model predicted PID gains that outperformed a baseline, with initial tests indicating it could adapt predictions based on apple locations, making it an adaptive controller.
This work is an exploratory research concerned with determining in what way reinforcement learning can be used to predict optimal PID parameters for a robot designed for apple harvest. To study this, an algorithm called Advantage Actor Critic (A2C) is implemented on a simulated robot arm. The simulation primarily relies on the ROS framework. Experiments for tuning one actuator at a time and two actuators a a time are run, which both show that the model is able to predict PID gains that perform better than the set baseline. In addition, it is studied if the model is able to predict PID parameters based on where an apple is located. Initial tests show that the model is indeed able to adapt its predictions to apple locations, making it an adaptive controller.