Aug 2026· International journal of intelligent engineering and systems
Control and Dynamics of Mobile Robots
Abstract
This paper presents a two-wheeled mobile robot trajectory-tracking controller combining a particle swarm optimization (PSO)-tuned fuzzy logic controller (FLC) with a residual reinforcement learning (RL) correction layer.PSO tuning reduces the global distance error by 35% and the integral absolute error by 44% over the initial FLC.The residual RL layer further reduces the global distance error by approximately 2.3% and improves cornering-region tracking by 3.9% in RMSE, 4.7% in IAE, and 5.2% in peak distance error.The proposed controller also reduces the global distance error by 41% and 66% relative to independently tuned PID and fuzzy-PID baselines.Trained across four trajectory families with a held-out test split, the generalized agent reduces the average test distance error by 18% relative to the tuned FLC baseline.These results show that a lightweight residual correction improves both accuracy and generalization while preserving the fuzzy controller's interpretability.
Model-based life-cycle evaluation indicates that AI-optimized PPP contracts reduce bridges reaching emergency condition by 30%–40% over a 30-year horizon while lowering life-cycle costs by 8%–12% compared with rule-based policies, providing infrastructure agencies and private concessionaires with an integrated AI-driven life-cycle management platform.
Ali Shehadeh, Odey Alshboul· Journal of Legal Affairs and...· 0 citations
Reinforcement learning (RL) has achieved remarkable success in various domains, but its "black box" nature poses a significant challenge for real-world deployment. Understanding the rationale behind an RL agent's decisions is crucial for trust, debugging, and improving performance. This paper proposes a novel approach to explainable AI (XAI) within reinforcement learning by leveraging causal reasoning. We model the environment and the agent's policy using a causal Bayesian network. By performing inference through this network, we trace the causal chain of events leading to a specific action, providing a transparent explanation. This method moves beyond simply observing the agent's behavior to understanding the underlying reasons for its choices. The core of our approach lies in identifying and representing the causal relationships within the RL system, enabling us to dissect the decision-making process and ultimately build more robust and reliable RL agents. The proposed framework offers a significant step toward interpretable RL and addresses a critical limitation of current techniques. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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