Control system design is a core task in industries such as chemical temperature regulation and aero-engine control. Traditional workflows rely heavily on expert knowledge and extensive manual parameter tuning, leading to low efficiency and poor scalability. To address this, the paper introduces the AI Control Scientist (AICS), the first large-language-model-driven agent that can automatically generate optimized controllers from natural-language design requirements.
The system consists of three hierarchical agents:
- The Task Modeling Agent parses user specifications and translates them into engineering constraints (e.g., steady-state error, rise time, robustness metrics).
- The Controller Design Agent searches the constrained space for controller structures (PID, LQR, MPC, etc.) and produces executable code snippets.
- The Parameter Tuning Agent uses closed-loop performance criteria (e.g., $J = \\int_0^T e(t)^2 dt$) to iteratively refine parameters in a simulation environment until the design goals are met.
Experiments cover several representative control tasks, including temperature regulation loops and aero-engine speed control. Results show that AICS automatically generates multiple representative control systems, achieving higher design success rates and optimization efficiency than existing automated baselines.
Significance: This work demonstrates a shift from human-driven to agent-driven control system design, laying the groundwork for automated model predictive control and other advanced strategies.
Blogger's Review: The three-agent architecture of AICS is logically sound, and the tight loop between code generation and parameter tuning is particularly impressive, indicating a new era of AI-enabled industrial control.