Two-wheeled self-balancing robots exhibit nonlinear and inherently unstable dynamics due to their inverted-pendulum structure, making control design challenging under terrain variations and external disturbances. This paper proposes a hybrid master–slave fuzzy cascade controller with an additional wheel-synchronization loop to improve tracking performance and robustness. The architecture combines a master velocity PI loop with fuzzy-tuned integral action and a slave balance PD loop with fuzzy proportional control, while a differential synchronization mechanism compensates for motor mismatches without affecting the global balance dynamics. Local stability is analyzed through linearization and equivalent gain approximation within a sector-bounded framework. Experimental validation was conducted on an ESP32-based TWSBR under flat, uphill, and downhill conditions at reference velocities of $0.15$, $0.20$, and $0.30{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, including payload tests with additional masses of $0.279$ and $0.375\mathrm{kg}$. For each scenario, 30 independent trials were performed to compute the reported metrics. Compared with a conventional PID controller, the proposed strategy reduced the flat-terrain velocity RMSE from $0.0108$ to $0.0057{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, while also improving angular stabilization and robustness under slope and payload disturbances.

Adaptive cruise control (ACC) is a key component of advanced driver assistance systems, as it maintains a safe distance from preceding vehicles by regulating speed and spacing. However, vehicle dynamics, measurement uncertainty, and traffic variability pose significant challenges for conventional control methods. In this context, fuzzy logic (FL) has been widely explored for its ability to handle uncertainty and incorporate expert knowledge via linguistic rules. This article presents a systematic literature review on the application of FL in ACC systems, proposing a functional taxonomy based on the role of the fuzzy system within the control architecture. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 103 initial records were identified, of which 87 studies were included in the final analysis. Four main categories are defined: Direct Fuzzy Control/Learning-Based, Fuzzy Supervisory Decision Control, Fuzzy Adaptive Robust Control, and Fuzzy Model-Based Control. Results indicate that Direct Fuzzy Control/Learning-Based and Fuzzy Supervisory Decision Control dominate the literature, accounting for 35.6% and 28%, respectively, while Fuzzy Adaptive Robust Control and Fuzzy Model-Based Control represent 20.7% and 14.9%. Mamdani-type systems predominate (78.16%), followed by Takagi-Sugeno (T–S) systems (17.24%), while type-2 fuzzy systems remain limited (4.60%) due to higher computational complexity. Recent trends highlight growing interest in adaptive and robust FL-based strategies.

Background: Learning-based, bio-inspired, and hybrid methods report routing improvements, but differences in experimental design complicate comparison. This critical review examines the evidence supporting these claims. Methods: Structured screening and critical synthesis covered 240 journal studies from 2022 to 2026: 43 learning-based, 135 bio-inspired and evolutionary, and 62 hybrid. Full-text assessment using category-specific thresholds yielded 1243 primary decisions, complemented by resource-stage and bio-inspired evaluation analyses. Results: Learning generalization and controlled ablation were Supported in 14/43 and 17/43 studies; none met complete joint resource-accounting or live operational-validation thresholds. Bio-inspired studies reported repeated runs in 104/135 cases and public code with inputs in 9/135; complementary feasibility, fairness, and generalization thresholds were Supported in 10/135, 4/135, and 2/135. Among 56 hybrids with comparative routing outcomes, 39 met neither the matched-budget nor the integration-cost threshold. Conclusions: This review contributes a category-specific framework linking routing improvements to feasibility, experimental controls, computational resources, and validation scope. Under its operational thresholds, these reporting frequencies characterize evidence rather than overall study quality. The framework helps researchers and logistics practitioners distinguish Supported claims from those requiring additional evidence, without pooling incompatible performance results or ranking methodological families.