Artificial Intelligence Needs Control Theory: Towards Stable, Adaptive, and Autonomous AI Systems

Abstract Artificial intelligence systems are increasingly capable of reasoning, planning, and interacting with external tools. However, capability alone does not guarantee stable behavior, reliable execution, or successful task completion under uncertainty and disturbances. This paper investigates how control-theoretic principles can inform the design of more stable, adaptive, and autonomous AI systems. It develops a formal model of the closed cognitive control loop, examines practical stability and actuator gain uncertainty, and introduces the Unified Cognitive Control Framework (UCCF), a proposed architecture integrating state estimation, feedback, adaptation, verification, and safety mechanisms. The framework is evaluated through a simulation-based proof of concept using a controlled navigation task. Across 500 trials per configuration, the full UCCF configuration achieved an 83.8% task success rate, compared with 61.4% for a raw-feedback baseline. These results provide preliminary evidence that combining feedback with additional control and verification mechanisms can improve task performance in the simulated environment. The study also discusses limitations concerning metric validation, model assumptions, computational overhead, and generalization to deployed AI agents. The simulation does not establish equivalent performance improvements in real-world large language model (LLM) agents. This work contributes a control-oriented perspective on autonomous AI system design and outlines a research direction for integrating stability, adaptation, and runtime verification into increasingly capable AI architectures. Keywords: Artificial Intelligence; Control Theory; Autonomous Systems; LLM Agents; Feedback Control; Runtime Verification; Stability; Adaptive Systems. Author: Mohamed EidAffiliation: OpenQCore AI Research