Advanced Actuation and Control Technologies for Vehicle Driving Systems—2nd Edition

1. Introduction

Actuators provide a critical link between high-level control decisions and the physical dynamics of a vehicle, translating control commands into steering, braking, suspension, and propulsion actions that directly influence vehicle motion, passenger comfort, and driving efficiency [1,2,3]. In the rapidly evolving landscape of automotive technology, actuation and control systems have become increasingly critical, particularly as vehicles transition toward higher levels of automation and electrification [4,5]. Beyond vehicle motion control, other control systems, such as suspension control, operate within the vehicle to enhance user comfort. Suspension actuators regulate the forces acting between the vehicle body and wheels, enabling the control of body motions and vibrations to improve ride comfort, handling, and overall ride quality [2,6]. Recent advances in computational techniques, optimization methods, and artificial intelligence offer new ways of designing vehicle control systems for enhanced performance in various vehicle applications and operating conditions, with improved safety-critical and energy-efficient system handling [5,7,8,9]. These techniques have been extended to non-traditional vehicle or robotics control, representing emerging research areas for next-generation mobility and industrial applications.

Vehicle control research has combined actuator design with a consideration of various operational aspects. Sinigaglia et al. [10] developed a Model Predictive Control Allocation framework to coordinate multiple chassis motion actuators in heavy vehicles while handling physical limits and actuator dynamics. A framework for simultaneously optimizing vehicle design and control strategies to maximize lap-time performance and energy efficiency [11], dual-actuator shift control for dual-mode coupling drive electric vehicles using a staged fuzzy PID controller to manage mutual interference [12], and an integration of multi-subsystem vehicle dynamics and actuator coordination to enhance overall handling and stability have also been introduced [13].

The second edition of this Special Issue, “Advanced Actuation and Control Technologies for Vehicle Driving Systems,” addresses the pressing need for innovative research in this domain to support sustainable transportation through emerging technologies. The collection showcases cutting-edge developments spanning autonomous vehicle control, electric vehicle powertrain management, advanced suspension systems, and novel actuation mechanisms. As autonomous vehicles are projected to become increasingly significantly in the next decades, the technologies explored in this issue are not merely an academic pursuit but are an essential enabler of our society’s future mobility and economic advancement. The scope of this Special Issue extends beyond conventional passenger vehicles to encompass a diverse range of applications, including non-traditional and industrial vehicles, electric vehicles, vehicle suspension, excavators, and bulldozers, reflecting the broad relevance of advanced actuation and control technologies across the transportation ecosystem. This editorial synthesizes the key contributions of the nine papers published in this Special Issue, organized around three thematic pillars, namely, autonomous vehicle control, suspension and actuation systems, and novel actuation technologies. It also categorizes and lists them by research focus, methods, and application areas.

2. An Overview of Published Articles

2.1. Autonomous Vehicle Control and Motion Systems

The transition toward autonomous driving presents fundamental challenges in vehicle dynamics and motion control. Several contributions to this Special Issue address these challenges through innovative approaches to trajectory tracking, system modeling, and control strategy optimization.

The paper titled “Autonomous Tracked Vehicle Trajectory Tracking Control Based on Disturbance Observation and Sliding Mode Control” examines the path-tracking control problem for tracked mobile robots operating in complex terrain, addressing the critical issue of track slippage, which significantly degrades control accuracy in unstructured environments. Their hierarchical control architecture, which combines kinematic modeling with sliding-mode control strategies, demonstrates robust trajectory tracking across various terrain conditions. The integration of adaptive laws and nonlinear control methods effectively mitigates high-frequency chattering, thereby extending actuator lifespan while maintaining control performance.

The paper titled “Extenics Coordinated Torque Distribution Control for Distributed Drive Electric Vehicles Considering Stability and Energy Efficiency” addresses the dual challenge of driving stability and energy efficiency in distributed-drive electric vehicles through a novel extenics coordinated torque distribution control method. Their contribution is particularly significant for developing a vehicle stability assessment framework grounded in extenics control theory, enabling adaptive weighting between energy optimization and stability objectives. Co-simulation results under NEDC urban driving cycles and double-lane-change conditions show that this approach achieves a practical balance between competing objectives, reducing energy loss by up to 11.2% compared to stability-oriented methods while maintaining satisfactory driving stability.

The paper titled “Optimization Study of Pneumatic–Electric Combined Braking Strategy for 30,000-ton Heavy-Haul Trains” addresses the longitudinal dynamics of 30,000-ton heavy-haul trains, a critical challenge for railway transportation capacity. Their optimization of pneumatic–electric combined braking strategies addresses the excessive longitudinal impulse that occurs when conventional braking is applied on steep gradients. The validated longitudinal dynamic model, combined with systematic analysis of braking parameter effects, yielded strategies that reduced maximum longitudinal forces by up to 47.83% compared to conventional approaches. This work has significant implications for operational safety in heavy-haul rail transport.

2.2. Advanced Suspension and Actuation Systems

Active suspension systems represent a frontier in vehicle comfort and handling performance. Several papers in this Special Issue focus on the control challenges inherent in electro-hydraulic servo actuators (EHSAs) for active suspensions, addressing time delays, internal leakage faults, and system nonlinearities.

The paper titled “Theoretical Analysis of IGAO-Fuzzy PID Fault-Tolerant Control and Performance Optimization for Electro-Hydraulic Active Suspensions Under Internal Leakage Faults” addresses the performance degradation and control instability in electro-hydraulic active suspension systems caused by internal leakage faults, e.g., due to component wear and aging. To address this, the authors propose an innovative fuzzy PID fault-tolerant controller optimized using the Improved Giant Armadillo Optimization (IGAO) algorithm. The IGAO algorithm introduces a nonlinear dynamic inertia weight mechanism and a random reflection strategy to prevent getting trapped in local optima. Using a quarter-car simulation model, the study shows that the proposed method effectively suppresses body vibrations, reduces shock amplitude, and maintains strong dynamic recovery and control robustness under varying degrees of internal leakage faults, outperforming standard optimization methods such as Particle Swarm Optimization (PSO) and standard GAO.

The paper titled “Dynamic Error Improved Model-Free Adaptive Control Method for Electro-Hydraulic Servo Actuators in Active Suspensions with Time Delay and Data Disturbances” examines the performance degradation caused by time delays and data disturbances in electro-hydraulic servo actuators for active suspensions. Their Dynamic Error Improved Model-Free Adaptive Control (DE-IMFAC) method represents a significant advancement by reconfiguring the time-delay term from an explicit form in the control law to implicit management. This reconceptualization substantially mitigates the influence of time delays on system control performance without requiring an accurate mathematical model of the actuator system. Rigorous theoretical analysis establishing BIBO stability and monotonic convergence of tracking error is complemented by simulation and experimental results demonstrating superior performance over PID and traditional MFAC methods across multiple metrics.

The paper titled “IPSO-Optimized DE-MFAC Strategy for Suspension Servo Actuators Under Compound-Degradation Faults” further extends this line of inquiry to address compound-degradation faults, specifically internal leakage combined with time delays. Their collaborative control framework, which combines DE-MFAC with an Improved Particle Swarm Optimization (IPSO) algorithm, demonstrates the power of hybrid approaches for managing complex fault scenarios. Validation on the AMESim-Simulink co-simulation platform shows that the IPSO-tuned DE-MFAC delivers superior position-tracking accuracy, faster response time, and stronger overshoot suppression under various compound-fault conditions. Notably, the Integral of Absolute Cubic Error (IACE) function is particularly effective at suppressing overshoot, offering practical guidance for engineering applications with stringent dynamic performance requirements.

The paper titled “Model-Free Multi-Parameter Optimization Control for Electro-Hydraulic Servo Actuators with Time Delay Compensation” further validates the DE-MFAC approach through comprehensive co-simulation studies under two time-delay scenarios. Their work demonstrates the remarkable advantages of the proposed control strategy in reducing tracking errors while balancing settling time and overshoot. The development of a quarter-vehicle active suspension electro-hydraulic actuation system model in AMESim 2021, coupled with Simulink 2023b co-simulations, provides a robust validation platform for future research.

The paper titled “Performance Improvement in a Vehicle Suspension System with FLQG and LQG Control Methods” investigates the effect of active control on quarter-vehicle suspension systems using Linear Quadratic Gaussian (LQG) and Fuzzy Linear Quadratic Gaussian (FLQG) control methods optimized with the grey wolf optimization algorithm. Their results demonstrate significant improvements across all vehicle parameters: vehicle body movement improved by approximately 88.2%, vehicle acceleration by 91.5%, suspension deflection by 88%, and tire deflection by 89.4%. These substantial gains in vehicle driving comfort highlight the potential of intelligent control methods in suspension systems.

2.3. Novel Actuation Technologies

The final thematic area of this Special Issue explores emerging actuation paradigms that promise to transform vehicle control systems through bio-inspired design and novel materials.

The paper titled “Review of Bio-Inspired Actuators and Their Potential for Adaptive Vehicle Control” provides a comprehensive review of bio-inspired actuators and their potential for adaptive vehicle control. The paper systematically categorizes actuators by mechanism, including shape memory alloys, dielectric elastomers, ionic polymer–metal composites, polyvinylidene fluoride-based electrostrictive actuators, and soft pneumatic actuators. The paper evaluates each mechanism’s properties, operating principles, and potential applications in automotive systems.

The review identifies several promising application areas: adaptive suspension, active steering, braking systems, and human–machine interfaces for autonomous vehicles. Bio-inspired actuators offer unique advantages, including flexibility, adaptability, and energy efficiency, often mimicking natural mechanisms such as muscle movement and plant tropism. However, the authors candidly address key challenges, including material limitations, response times, and integration with existing automotive control systems.

The review concludes by discussing future directions, emphasizing the integration of bio-inspired actuators with machine learning and advancements in material science as pathways to more efficient and responsive adaptive vehicle control systems. The authors project that bio-inspired actuators will play a significant role in the future automotive industry, offering advantages in weight, power, flexibility, and cost compared to conventional systems.

2.4. Summary of the Contributions

Table 1. Distribution of contributed papers by research area.

Research Area	Papers	Contributed Paper by
Active Suspension Systems	4	Zheng et al. (2026), Xiong et al. (2026),
		Xiong et al. (2026), Zheng et al. (2025)
Autonomous Vehicle Control	2	Yan et al. (2025), Wang et al. (2026)
Electric Vehicle Control	1	Wang et al. (2026)
Heavy-Haul Train Braking	1	Zhang et al. (2025)
Novel Actuation Technologies	1	Mittal et al. (2025)

,

Table 2. Distribution by control methodology.

Methodology	Papers	Contributed Paper by
Model-Free Adaptive	3	Xiong et al. (2026),
Control (MFAC)		Xiong et al. (2026), Zheng et al. (2025)
Fuzzy Logic Control	2	Zheng et al. (2026), Abut et al. (2025)
Sliding Mode Control	1	Yan et al. (2025)
LQG/FLQG Control	1	Abut et al. (2025)
Extenics Control	1	Wang et al. (2026)
Optimization Algorithms	4	Zheng et al. (2026), Xiong et al. (2026),
		Zheng et al. (2025), Abut et al. (2025)

and

Table 3. Distribution by application/vehicle type.

Application/Vehicle Type	Papers	Contributed Paper by
Passenger Vehicles (Suspension)	4	Zheng et al. (2026), Xiong et al. (2026),
		Xiong et al. (2026), Abut et al. (2025)
Distributed-Drive Electric Vehicles	1	Wang et al. (2026)
Tracked Mobile Robots	1	Yan et al. (2025)
Heavy-Haul Trains	1	Zhang et al. (2025)
General Vehicle Systems (Review)	1	Mittal et al. (2025)

provide a categorized summary of the contributions. Specifically, Table 1 categorizes the contributions into five research areas and shows that active suspension systems are the most heavily researched area, with four of nine papers focusing on electro-hydraulic servo actuators and their control challenges.

Table 2 summarizes the distribution of contributed papers across seven control methodologies. Optimization algorithms, model-free adaptive control, and fuzzy logic control are used in multiple contributions. Four papers incorporate metaheuristic optimization algorithms (IGAO, IPSO, PSO, GWO) to tune control parameters, demonstrating the importance of intelligent optimization in complex vehicle systems. Remarkably, a clear trend toward model-free adaptive control (MFAC) methods emerges, as they can handle system uncertainties, time delays, and degradation without requiring exact mathematical models.

Finally, Table 3 categorizes the contributions by specific application or vehicle types. While passenger vehicles dominate with four papers, the Special Issue also covers tracked robots and heavy-haul trains with bio-inspired actuators.

3. Conclusions

This editorial provides an overview of the Special Issue, highlighting contributions that collectively deepen our understanding of emerging vehicle control technologies across various elements. First, the papers indicate a trend toward model-free and data-driven control approaches that aim to address system uncertainties, time delays, and degradation without relying on precise mathematical models. The DE-MFAC framework and its variants represent a significant paradigm shift in this direction. Second, integrating metaheuristic optimization algorithms with classical control strategies has proven highly effective in addressing the multi-constraint parameter optimization problems inherent in complex vehicle systems. The IGAO and IPSO algorithms developed in this issue demonstrate superior performance compared to conventional optimization approaches. Third, the recognition that vehicle control involves multiple competing objectives, including stability, energy efficiency, comfort, and safety, has led to sophisticated multi-objective optimization frameworks. The extenics coordinated control method and the FLQG approach exemplify this trend.

Looking forward, several research directions merit particular attention. Integrating bio-inspired actuators with machine learning offers exciting possibilities for adaptive vehicle control systems. Validating novel control strategies on physical hardware platforms, particularly for electro-hydraulic actuators, is a necessary next step. Additionally, extending these methods to emerging vehicle types, including autonomous delivery vehicles, micro-mobility solutions, and electric vertical takeoff and landing (eVTOL) aircraft, could significantly expand the impact of this research. Finally, the papers collected in this Special Issue are expected to serve as a valuable reference for researchers and practitioners working at the intersection of actuation technology, control systems, and vehicle engineering.