Intelligent Control Methods for Wheel-Slip Regulation in Automotive Braking Systems: A Systematic Review and Real-Time Embedded Feasibility Analysis

This paper presents a systematic literature review of intelligent and advanced control methods for automotive wheel-slip regulation in anti-lock braking systems (ABS). Following a PRISMA-based workflow, records from IEEE Xplore, Scopus, and Engineering Village were searched for the 2014–2025 period. The search returned 5034 records; after removal of duplicates, patents, and out-of-range items, 1803 records were screened, 406 reports were assessed at full-text level, and 60 studies met the final eligibility criteria. The included studies were classified using a six-class taxonomy: fuzzy and neuro-fuzzy control, adaptive and self-tuning control, robust nonlinear control, predictive and optimization-based control, learning-assisted control, and hybrid or integrated control. The review shows that robust nonlinear and hybrid/integrated methods dominate the evidence base, while fuzzy and predictive methods remain important recurring families. Validation is still strongly simulation-weighted: many studies report braking-performance gains in slip tracking, stopping distance, chattering reduction, or road-friction robustness, but comparatively few provide hardware-in-the-loop, laboratory, or processor-level evidence. Only a very limited subset reports concrete embedded metrics such as execution time, sampling-period compliance, memory usage, or deadline margin. By combining systematic study selection, braking-specific control equations, primary-method classification, validation coding, embedded-implementation assessment, and comparative-scope analysis, this review identifies a persistent gap between algorithmic ABS performance and deployable real-time embedded feasibility. The findings motivate standardized benchmarking of intelligent braking controllers under common wheel-slip scenarios, common plant models, and common embedded timing metrics.