Intelligent Control and Actuation for High-Performance Autonomous Vehicle Dynamics
A special issue of Actuators (ISSN 2076-0825). This special issue belongs to the section "Actuators for Surface Vehicles".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 75
Special Issue Editor
Interests: human driver behavior analysis for autonomous and assisted driving systems; vehicle dynamics; tire behavior, and control under extreme driving conditions; driving simulation, HMI/UX, and experimental validation; motorsport-inspired vehicle dynamics and high-performance driving analysis; safety-oriented vehicle control and intelligent transportation systems
Special Issue Information
Dear Colleagues,
The rapid advancement of autonomous driving technologies has significantly increased the demand for high-performance vehicle dynamics control and intelligent actuation systems. Modern autonomous vehicles must operate safely and efficiently under highly dynamic and uncertain environments, requiring seamless integration of perception, decision-making, and control.
In this context, intelligent control and actuation have emerged as key enablers for enhancing vehicle performance, safety, and robustness. Recent developments in data-driven methods, artificial intelligence, and advanced optimization techniques are transforming conventional control frameworks into more adaptive, predictive, and human-aware systems.
This Special Issue focuses on recent research in control, estimation, actuation, and dynamic system analysis for autonomous vehicle dynamics. We welcome contributions that address both theoretical developments and practical implementations, including simulation, experimental validation, and real-world applications. Topics of interest include, but are not limited to:
- Vehicle dynamics modeling and control under extreme or uncertain conditions;
- Integrated chassis control (steering, braking, suspension, and torque vectoring);
- Motion control and trajectory tracking for autonomous vehicles;
- Robust, adaptive, and model predictive control (MPC);
- Data-driven and learning-based control approaches (e.g., reinforcement learning, imitation learning);
- Human-inspired and driver-in-the-loop control strategies;
- Fault-tolerant control and safety-critical systems;
- Actuator design, control allocation, and redundancy management;
- State estimation and sensor fusion for vehicle dynamics;
- Interaction between vehicle dynamics and environment (road friction, disturbances, etc.);
- Tire–road interaction, tire modeling, and contact dynamics;
- Suspension system dynamics, ride and handling analysis, and multibody system modeling;
- Coupled tire–suspension dynamics analysis and optimization;
- Design optimization of chassis and suspension systems for performance and robustness;
- Real-time implementation and embedded control systems;
- Validation through simulation, hardware-in-the-loop (HIL), and experimental platforms.
Dr. Jaewon Nah
Guest Editor
Manuscript Submission Information
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Keywords
- autonomous vehicle dynamics
- intelligent actuation systems
- integrated chassis control
- model predictive control (MPC)
- data-driven control
- fault-tolerant control/safety-critical systems
- tire–road interaction
- real-time embedded control
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