ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise

TL;DR

ControlAgent mimics the design processes used by (human) practicing engineers, but removes all the human efforts and can be run in a fully automated way to give end-to-end solutions for control system design with user-specified requirements.

Abstract

Control system design is a crucial aspect of modern engineering with far-reaching applications across diverse sectors including aerospace, automotive systems, power grids, and robotics. Despite advances made by Large Language Models (LLMs) in various domains, their application in control system design remains limited due to the complexity and specificity of control theory. To bridge this gap, we introduce ControlAgent, a new paradigm that automates control system design via novel integration of LLM agents and control-oriented domain expertise. ControlAgent encodes expert control knowledge and emulates human iterative design processes by gradually tuning controller parameters to meet user-specified requirements for stability, performance, and robustness. ControlAgent integrates multiple collaborative LLM agents, including a central agent responsible for task distribution and task-specific agents dedicated to detailed controller design for various types of systems and requirements. ControlAgent also employs a Python computation agent that performs complex calculations and controller evaluations based on standard design information provided by task-specified LLM agents. Combined with a history and feedback module, the task-specific LLM agents iteratively refine controller parameters based on real-time feedback from prior designs. Overall, ControlAgent mimics the design processes used by (human) practicing engineers, but removes all the human efforts and can be run in a fully automated way to give end-to-end solutions for control system design with user-specified requirements. To validate ControlAgent's effectiveness, we develop ControlEval, an evaluation dataset that comprises 500 control tasks with various specific design goals. The effectiveness of ControlAgent is demonstrated via extensive comparative evaluations between LLM-based and traditional human-involved toolbox-based baselines.

Figures (12)

• Figure 1: General ControlAgent framework.
• Figure 2: A feedback control system illustrating the reference $r$, measured output $y$, disturbance $d$, and noise $n$. The dynamical model $G(s)$ provides a mathematical approximation of the real physical system. The inherent mismatch between the real system and its mathematical model underscores the need for a robust controller $C(s)$ to ensure reliable performance despite modeling inaccuracies.
• Figure 3: The controller design process of ControlAgent, showcasing interactions between the User, Central agent, Python agent, History and Feedback module, and Task-Specific Agents to design a controller that meets stability, phase margin, and settling time requirements.
• Figure 4: Workflow of the task-specific agent in ControlAgent. The design history and feedback are dynamically updated based on previous iterations. ControlAgent refines its designs iteratively, incorporating user instructions and feedback at each step. By the third iteration, ControlAgent achieves a final design that satisfies the user's requirements, achieving a settling time of less than 0.3 seconds (as shown in the time response plot) and maintaining a phase margin consistently greater than $70^\circ$ (as depicted in the Bode plot).
• Figure 5: ASR and AgSR for first-order stable systems (averaged across fast, moderate, and slow modes) and higher-order system.