Hybrid Control Architectures for Autonomous Systems - Analyzing hybrid control architectures combining classical and learning-based approaches for autonomous systems
Keywords:
Hybrid Control Architectures, Autonomous Systems, Classical Control, Learning-Based Approaches, Robustness, Adaptability, Dynamic Environments, Uncertainty, PerformanceAbstract
Hybrid Control Architectures for Autonomous Systems have garnered significant interest due to their potential to combine the robustness of classical control approaches with the adaptability of learning-based methods. This paper presents a comprehensive analysis of various hybrid control architectures used in autonomous systems, focusing on their design principles, advantages, and challenges. The study evaluates how these architectures enhance the overall performance, reliability, and safety of autonomous systems in dynamic and uncertain environments. Through a detailed review and comparison of existing approaches, this paper provides insights into the state-of-the-art techniques and identifies future research directions in the field of hybrid control architectures for autonomous systems.
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