A critical Review of Robust and Adaptive Control Algorithms for Dynamic Environments

Authors: Derrick Appiah Osei, Abass Aliu

DOI: https://doi.org/10.37082/IJIRMPS.v14.i1.232956

Short DOI: https://doi.org/hbqrbs

Country: United States

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Abstract: Dynamic environments characterized by uncertainty, nonlinearities, disturbances, and time-varying system dynamics present persistent challenges to modern control systems. Robust and adaptive control algorithms have long been developed to address these challenges, each offering distinct advantages but also notable limitations. Robust control provides strong stability and performance guarantees under bounded uncertainties, yet it often leads to conservative designs that limit system efficiency. Adaptive control, on the other hand, enables real-time adjustment to unknown or changing system parameters but may suffer from instability and reduced reliability in the presence of large disturbances or unmodeled dynamics. This study presents a critical review of robust and adaptive control algorithms with a focus on their suitability for dynamic and uncertain environments. The review examines classical and modern robust and adaptive control frameworks, including H-infinity control, sliding mode control, model reference adaptive control, and learning-based adaptive approaches. It further explores hybrid robust adaptive and learning-augmented control architectures that seek to combine stability guarantees with real-time flexibility and improved performance. Applications across robotics, autonomous vehicles, industrial process control, and renewable energy systems are discussed to illustrate how these control strategies perform in practice. Key issues such as stability, robustness, adaptation speed, computational complexity, and implementation constraints are critically compared. The findings indicate that hybrid and learning-integrated control strategies represent a promising pathway for next-generation control systems, offering improved resilience and adaptability without sacrificing safety. However, challenges related to sensing, computation, validation, and regulatory compliance remain significant. The study concludes that future progress in control engineering will depend on effectively integrating robust theoretical foundations with adaptive and data-driven techniques to meet the demands of real-world dynamic environments.

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Paper Id: 232956

Published On: 2026-02-26

Published In: Volume 14, Issue 1, January-February 2026

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