Fuzzy Game-Theoretic Predictive Control for Integrated Vehicle Stability and Energy-Efficient Motion Management

Abstract

The increasing intelligence of vehicles transforms motion management from a mechanical regulation problem into a complex cyber-physical coordination task. This paper examines a class of control architectures that combine fuzzy inference, game-theoretic decision making, and predictive control for integrated vehicle stability and energy-efficient motion management. Rather than presenting numerical algorithms, the discussion focuses on system-level structural trade-offs among actuation authority, trajectory tracking, energy consumption, robustness, and policy compliance. The paper argues that advanced vehicle control should be interpreted as a multi-objective negotiation process in which stability requirements and energy performance objectives operate as partially conflicting players under uncertainty. Fuzzy rule bases provide an interpretable mechanism for representing uncertain tire-road interactions, driver intent, and environmental disturbances. Game-theoretic concepts allow the control architecture to balance competing objectives without assigning fixed priority to either stability or energy efficiency. Predictive control supplies anticipatory constraint handling over a finite horizon. The article analyzes how these components can be integrated into layered automotive control infrastructure, including sensing, estimation, arbitration, actuation, and cloud-assisted learning. It discusses deployment constraints, real-time computational loads, safety certification, robustness degradation, fairness in shared infrastructure, and sustainability implications. Cross-domain comparisons with energy management in hybrid vehicles and cooperative autonomous fleets are provided. The analysis identifies unresolved tensions between interpretability and learning, centralization and edge autonomy, and short-term energy optimization and long-term dependability. The paper concludes that the value of fuzzy game-theoretic predictive control lies less in replacing conventional vehicle stability controllers than in creating a governance layer that makes trade-offs explicit, auditable, and adaptable across heterogeneous operating contexts.