AI Control Design Solution (Optimization through Reinforcement Learning)

basic information

Learning Method: Reinforcement Learning Algorithm Used: SAC (Soft Actor Critic) Network Structure: Actor Network, Critic Network (both are multi-layer neural networks) Tools: MATLAB/Simulink Reinforcement Learning Toolbox, Python (TensorFlow/Keras, etc.) Control Target: Electric throttle valve position control Reward Design: Minimization of error to target position + control amount penalty, etc. (estimated) Learning Settings: Number of episodes: over 1,000, batch size: 32, etc. (from graphs)

Price range

Delivery Time

Applications/Examples of results

Control design using reinforcement learning can be applied to industrial actuators, electric throttle valves, electric motor control, inverter control, and more. In particular, it may achieve more flexible responses than conventional design methods for systems that include dynamic characteristics, friction, and nonlinearity. In the application example shown, autonomous positioning is achieved using an Actor-Critic network in relation to the target value of throttle opening. In the future, applications in fields such as robotics, autonomous driving, and HVAC control are expected.