AI Control Design Solution (Optimization through Reinforcement Learning)
How about designing AI control to replace PID using reinforcement learning?
This solution supports the design of next-generation control systems utilizing reinforcement learning. It is an approach that automatically acquires optimal control strategies through learning for nonlinear, higher-order delay systems and multi-degree-of-freedom control targets, which are difficult to handle with conventional PID control. The agent selects actions based on rewards, allowing for the construction of controllers that maximize performance. It is compatible with model-based design using MATLAB/Simulink's Reinforcement Learning Toolbox and Python environments, enabling the utilization of existing model assets. In addition to traditional mathematical model-driven design, it achieves integration with data-driven AI.
- Company：ネオリウム・テクノロジー
- Price：Other
