Explainable Control Framework (XCF) based on Fuzzy Model-Agnostic Explanation and LLM Agent-Supported Interface

Researchers propose an Explainable Control Framework (XCF) that uses fuzzy logic and large language models to make complex automated controllers transparent and understandable to humans. The system generates natural language explanations of controller decisions across multiple levels of abstraction, demonstrated through robotic control applications like inverted pendulums and obstacle avoidance.

This paper addresses a critical gap in modern control systems: as controllers become more sophisticated through machine learning and data-driven approaches, understanding how they make decisions becomes increasingly difficult. The XCF bridges this transparency gap by creating human-interpretable explanations of controller behavior without requiring access to the underlying model architecture—a model-agnostic approach valuable across diverse applications.

The work builds on established explainability research in AI but applies it specifically to control systems, where understanding decision-making carries safety and operational implications. The integration of hierarchical fuzzy logic systems allows the framework to generate explanations at multiple granularity levels—from individual sample decisions to universe-level policy descriptions—providing flexibility for different stakeholder needs. The addition of an LLM-powered interface that automatically translates technical explanations into natural language represents a practical advancement in accessibility.

For industries relying on automated control systems—robotics, autonomous vehicles, industrial automation, and drone applications—this framework could reduce deployment friction by enabling regulators and operators to understand system logic. The ability to quantify state variable contributions through salience values provides actionable insights for debugging and optimization.

The current validation through simulated inverted pendulum and Turtlebot scenarios demonstrates feasibility but leaves questions about scalability to real-world industrial systems with hundreds of variables and safety-critical constraints. Future development should focus on computational efficiency, robustness to adversarial conditions, and integration with existing control pipelines to achieve practical adoption.

• →XCF provides model-agnostic explanations for complex controllers without requiring architecture access, enabling transparency across diverse control systems.
• →Hierarchical fuzzy logic generates multi-level explanations from individual decisions to policy descriptions, supporting various stakeholder needs.
• →LLM-powered interface automates explanation generation and natural language translation, improving accessibility for non-technical users.
• →Framework demonstrates effectiveness on robotic control tasks but requires validation on larger-scale industrial systems for practical deployment.
• →Integration of explainability in control systems could accelerate adoption in safety-critical applications like autonomous vehicles and industrial automation.