Real-time energy management for off-road hybrid electric vehicles (HEVs) poses significant challenges under variable and unknown driving conditions. Spurred by this challenge, this paper proposes a real-time adaptive energy management strategy (EMS) based on a model-based reinforcement learning (MBRL). Within the MBRL framework, a reinforcement learning (RL) oriented environment model is first constructed, consisting of a virtual vehicle model that represents a deterministic powertrain and an online Markov chain (MC) model to reflect dynamic driving conditions. Notably, both models support continuous online updates. Secondly, a novel multi-agent collaborative decision-time planning (DTP) algorithm is introduced. Unlike conventional RL methods that require learning a complete driving cycle, it learns the optimal action for each actual encountered vehicle state within the environment model. Moreover, its internal multi-agent collaborative mechanism combines a series of agent strategies, trained to converge under typical driving cycles, ensuring real-time performance. Simulation results under unknown off-road conditions demonstrate that the proposed strategy incurs only a 2.4% increase in fuel consumption and a 2.5% increase in state of health (SOH) compared to dynamic programming (DP), while maintaining a highly similar state of charge (SOC) trajectory. Simultaneously, the single-step computation time is merely 10.7 ms. It also significantly outperforms model-free Q-learning (MFQL) and rule-based strategies. Moreover, its consistent performance across three additional unknown driving cycles confirms its strong robustness. Finally, the effectiveness of the proposed strategy is validated on a hardware-in-the-loop (HIL) platform.

Internet of Things technology has greatly advanced the development and application of data-driven fault diagnostics and prognostics for industrial equipment. Industrial streaming data processing remains a challenge for intelligent fault diagnosis to continuously learning fault knowledge while retaining strong anti-forgetting ability. Recently, class-incremental learning has gained attention in data-driven fault diagnosis, since it enables to sequentially integrate new fault modes from industrial streaming data while maintaining previously learned knowledge. However, incremental transfer diagnosis in industrial streaming data and time-varying working conditions remain largely unexplored, and challenges such as the stability–plasticity dilemma and limited replay techniques still constrain diagnostic performance. To tackle these issues, we propose a generalized class-incremental lifelong transfer diagnosis (GCILTD) framework. First, a dynamic network expansion strategy is developed to overcome the stability-plasticity dilemma effectively, enabling the incremental model to capture new fault information while preserving prior knowledge. Then, a multi-stage training strategy is proposed to enhance the generalization of the dynamic network, further boosting both knowledge retention and adaptability. Furthermore, a dual-level memory buffer is first designed to enhance the class-incremental transfer diagnosis under time-varying working conditions. Finally, the proposed GCILTD framework is verified on two mechanical fault datasets. Experiment results demonstrate that our proposed GCILTD framework achieves advantageous diagnostic accuracies of 93.88% and 88.51% along with the lowest forgetting rates of 2.41% and 6.09% in different class-incremental transfer scenarios under industrial streaming data and time-varying working conditions, outperforming cutting-edge class-incremental learning approaches.

The jumping motion of wheel-legged robots (WLR) is of great significance to their obstacle-crossing ability. In existing studies, a Vertical Jumping (VJ) scheme that mimics human-like jumping has been realized in WLRs. However, the capacity limit of the actuator severely restricts the height of the wheel off the ground that VJ can reach, that is, the effective jumping height which directly affects the ability to cross obstacles. To enhance the effective jumping height within the actuator’s capacity, this paper proposes the Aerial Leg-Swing Jumping (ALSJ) scheme. By analyzing the take-off and flight phases from an energy perspective, the ALSJ scheme is designed to reduce peak torque and energy consumption. The implementation framework of the proposed scheme includes a vertical reachability map, a phased optimization planning method, and an offset-free whole-body control strategy. Simulation results show that, compared to the VJ scheme, the proposed scheme increases the maximum achievable effective jumping height by about 31.03% within actuator constraints. Additionally, these two schemes are compared in hardware experiments under a test condition with a desired jumping height of 0.15 m, and the flight time constraint of 0.32 s is introduced in ALSJ scheme to enhance the practical significance of the jump. Using the ALSJ scheme, the energy consumption during take-off phase is reduced by 16.23%, the total energy consumption throughout the entire jumping process is decreased by approximately 9.78%, and the peak knee joint torque is decreased by 32.57 Nm, further validating the effectiveness of the proposed scheme.

The connected hybrid vehicles (CHVs) fleet, which consists of multiple inter-CHVs, serves as a significant driver of future intelligent transportation systems. Through vehicle-to-vehicle (V2V) communication, CHVs can greatly enhance driving safety and reduce fuel consumption. However, network exposure and frequent communication make CHVs highly vulnerable to information security attacks, particularly Denial of Service (DoS) attacks, which could potentially lead to communication interruptions and pose a threat to the safety of the fleet. Therefore, designing an advanced vehicular control strategy such that the control performance of CHVs is resilient to DoS attacks has become an urgent issue. To address this challenge, this article proposes an attack-resilient control strategy to ensure the security and cost-effectiveness of the CHVs in the event of a DoS attack. First, heterogeneous and uncertain vehicle dynamics models and DoS attack models are established. Building upon this foundation, a comprehensive resilient model predictive control (CRMPC) strategy is proposed, which ensures queue safety through defined safety functions and resilient MPC while enhancing economy by incorporating vehicle fuel consumption function. Then, a weight adjustment mechanism is employed to balance the relationship between security and cost-effectiveness. Furthermore, an adaptive equivalent consumption minimization strategy (A-ECMS) is adopted, with the equivalent factor being dynamically adjusted in real-time using a Proportional Integral algorithm. Finally, the results of two test scenarios demonstrate that the strategy improves fuel efficiency by 5.32% and 5.44%, respectively, while ensuring fleet safety.

Trajectory tracking control of tractor semi-trailer vehicles poses significant challenges due to inherent off-tracking behavior and roll instability risks. While existing approaches have demonstrated effectiveness, they often rely on computationally intensive numerical solvers and require time-consuming manual tuning of cost function weights. This paper presents an approximate bi-level policy optimization (ABPO) framework that simultaneously optimizes the cost function and synthesizes an explicit control policy to minimize off-tracking while reducing computational complexity. The proposed framework employs a hierarchical structure: the upper level updates cost weights based on the trailer’s stability trajectory, while the lower level derives an approximate optimal policy by solving the tractor’s control problem. By leveraging Pontryagin’s Maximum Principle (PMP), we have developed a novel method to analytically compute cost weight gradients through differentiation of the PMP conditions. This enables the formulation of a related optimal control problem (OCP) whose solutions directly yield gradients for cost parameter updates. The ABPO framework achieves automatic weight coefficient adjustment, enhances trajectory tracking accuracy for both tractor and trailer units, and significantly reduces computational burden. Simulation and experimental validation across 4 classical scenarios demonstrates that the learned policy reduces rearward amplification by 17.82%, lateral tracking errors by 84.15%, and rollover by 64.19%, respectively. Notably, the control policy computation requires less than 10 ms, making it suitable for real-time applications. The source code for the algorithms described in this paper is publicly available at https://github.com/TroyResearch/ABPO.git.

Off-road environments with varied terrain and obstacle types present substantial challenges to the safe maneuvering of unmanned ground vehicles (UGVs). This study addresses the need for personalized path planning by introducing a multi-source off-road potential field (MOPF) method that quantifies risk and impediments in off-road settings based on internal and external characteristics. Specifically, Vehicle capability boundaries are defined by longitudinal dynamics analysis of the ego-vehicle to prevent instability due to insufficient driving force and limited adhesion conditions. A novel Non-Uniform Safety Margin Expression (NSME) is proposed to adjust the MOPF, allowing it to consider the vehicle’s state to enhance travel efficiency and minimize detours. The MOPF can be adapted according to the characteristics of the ego vehicle, drivers, and cargo. To incorporate driving styles, the Driving Style Probabilistic Roadmap (DSPRM) algorithm is developed, leading to smoother and more personalized paths. Comparative tests demonstrate that our method enables personalized path planning, achieving an average reduction of 10.29% in path length and 30.83% in path slope compared to traditional planning methods, while maintaining a safe distance from obstacles.

Inverse optimal control (IOC) seeks to infer a control cost function that captures the underlying goals and preferences of expert demonstrations. While significant progress has been made in finite-horizon IOC, which focuses on learning control cost functions based on rollout trajectories rather than actual trajectories, the application of IOC to receding horizon control, also known as model predictive control (MPC), has been overlooked. MPC is more prevalent in practical settings and poses additional challenges for IOC learning since it is complicated to calculate the gradient of actual trajectories with respect to cost parameters. In light of this, we propose the inverse MPC (IMPC) method to identify the optimal cost function that effectively minimizes the discrepancy between the actual trajectory and its associated demonstration. To compute the gradient of actual trajectories with respect to cost parameters, we first establish two differential Pontryagin's maximum principle (PMP) conditions by differentiating the traditional PMP conditions with respect to cost parameters and initial states, respectively. We then formulate two auxiliary optimal control problems based on the derived differentiated PMP conditions, whose solutions can be directly used to determine the gradient for updating cost parameters. We validate the efficacy of the proposed method through experiments involving five simulation tasks and two real-world mobile robot control tasks. The results consistently demonstrate that IMPC outperforms existing finite-horizon IOC methods across all experiments.

How to improve the trajectory following ability and lateral stability under extreme conditions is an important research problem for distributed drive electric vehicles (DDEVs). This paper proposes a novel integrated control architecture of active front steering control (AFS) system and direct yaw moment control (DYC) system for DDEVs. First, to deal with the future instability problem caused by driver’s misoperation or delayed control, online risk assessment and prediction models, including self-regulating phase plane stability judgment and future driving state prediction of vehicles, is designed to provide decision commands for actuators in advance under extreme conditions. Then, on the basis of comprehensive consideration of system chattering, robustness and control constraint index requirements, an integrated control method based on robust sliding mode predictive control (SMPC) to put forward to solve the multi-objective and multi-constraint optimization problem for multi-subsystem integration. Finally, the simulation and experimental results show that the proposed control architecture can effectively assist drivers improve the trajectory following ability and handling stability of DDEVs, as to ensure the maneuverability and safety of emergency obstacle avoidance under extreme conditions.

Along with the development of hybrid electric vehicle, dual-mode electro-mechanical transmission (EMT) as an innovative power-split transmission technology, is widely applied in heavy-load vehicles. In this paper, a dynamic model for a dual-mode EMT based hybrid electric vehicle is developed which consists of two electrically variable transmission (EVT) modes and provides a platform for performance analysis of vehicle components including electro- mechanical characteristics analysis. Due to drive condition of the powertrain system, the electro- mechanical characteristics of mode switch is evaluated by a simulation model which is designed based on MATLAB/Simulink, and the simulation results are comparatively analyzed. It is expected that the research contents of this paper can be served effectively as a basis for future research in the field of EMT.