Ruochen Wang

A novel energy-regenerative hydraulically interconnected ISD (inerter–spring–damper) suspension system (ER-HIISDS) is proposed by incorporating a fluid inerter into the hydraulic interconnection architecture, with the objective of simultaneously enhancing vehicle ride quality and vehicle stability while recovering suspension vibration energy. A coupled vehicle–hydraulic model is developed by integrating the 14-degree-of-freedom (DOF) full-vehicle dynamics with the ER-HIISDS hydraulic subsystem. On this basis, the key system parameters are optimized using the Electromagnetic Wave Propagation Algorithm (EMWPA). The mechanical characteristics and energy-regeneration performance of the optimized ER-HIISDS are subsequently investigated under sinusoidal excitations and stochastic road inputs. Comparative simulations are further conducted under representative transient maneuvers, including single-wheel bump excitation, emergency braking, and double-lane-change conditions, with a conventional passive suspension and an energy-regenerative hydraulically interconnected suspension (ER-HIS) employed as benchmark systems. The results indicate that the proposed ER-HIISDS can attenuate vehicle-body vibrations and improve body attitude stability while maintaining favorable dynamic performance and energy-regeneration capability over a range of stochastic road excitations. Under the representative transient maneuvers, the hydraulic interconnection generates additional coupling forces and corresponding anti-roll and anti-pitch moments, thereby effectively suppressing vehicle-body roll and pitch motions. Overall, the proposed ER-HIISDS achieves a favorable balance among ride comfort, handling stability, attitude control, and vibration-energy regeneration, demonstrating its potential for advanced vehicle suspension applications.

As modern agriculture expands into complex unstructured environments, including hilly terrain, paddy fields, and wetlands, conventional wheeled and tracked chassis increasingly face poor terrain adaptability, limited obstacle crossing capability, and severe soil compaction. Wheel-legged agricultural chassis combine efficient wheeled mobility with flexible legged obstacle crossing, offering a new approach to balancing operational efficiency with terrain adaptability. This review systematically summarizes the key technologies and recent advances in wheel-legged agricultural machinery chassis. It first analyzes the limitations of conventional mobile platforms and the technical insights provided by transformable wheels and biomimetic legged robots. Existing research is then reviewed in four areas: configuration and lightweight design, motion and posture control, obstacle crossing performance, and multi-sensor perception and autonomous navigation. The application potential and engineering limitations of wheel-legged chassis in hilly farming, protected agriculture, paddy fields and wetlands, and material transport are further examined. Because terrain adaptability relies on additional joints, actuators, sensors, and control systems, a fundamental conflict exists between mobility gains and structural complexity, manufacturing cost, energy consumption, and field reliability over prolonged operation. Future research should prioritize modular structures with fewer degrees of freedom, robust adaptive control, whole-machine energy management, protection against environmental exposure, and prolonged field testing to balance terrain adaptability with engineering practicality.

Agricultural machinery is rapidly developing toward electrification, intelligence, and autonomy, and the electric drive chassis has become a key technology for improving power transmission performance, operational efficiency, and energy utilization. This paper presents a review of research on electric drive chassis for agricultural machinery, focusing on four major aspects: electric drive systems, anti-slip and stability control of electric drive chassis, autonomous navigation system control technologies, and energy management strategies. The electric drive system is reviewed from the perspectives of drive motor technologies and drive architectures. Chassis control technologies are mainly discussed in terms of longitudinal anti-slip control and lateral stability control under complex terrain conditions. Autonomous navigation systems are summarized with respect to multi-source environmental perception, path planning, and path tracking control. Energy management strategies are classified into rule-based, optimization-based, and learning-based approaches according to their control principles, and the characteristics and applicable scenarios of each approach are analyzed. On this basis, the collaborative relationships among drive architecture, chassis control, autonomous navigation, and energy management are further discussed. Finally, future research directions are proposed, including highly integrated electric drive systems, vehicle-level collaborative control, multi-source sensor fusion, hybrid model-driven and data-driven control, global energy optimization, and multi-machine cooperative operation. This review provides a reference for the design and development of intelligent electric drive chassis for agricultural machinery.

With the aim of addressing the actuator delay issue in magnetorheological (MR) semi-active suspension systems, this paper investigates and designs an efficient control strategy to enhance the vehicle vertical dynamic performance. First, by establishing forward and inverse models that accurately describe the response characteristics of MR dampers, a quarter-vehicle suspension system model incorporating time-delay elements was constructed. The control objectives and performance constraints for the system were clearly defined. Building upon this foundation, to balance system robustness and comprehensive performance while accounting for the limitations of actual measurable signals, a static output-feedback controller synthesis method based on H _∞ / GH ₂ theory is proposed. Finally, through simulations and experiments under two typical road conditions, namely, transient impact road and steady-state random road, the effectiveness and superiority of the proposed control strategy were validated. The results demonstrate that compared to passive suspension and traditional time-delay independent H _∞ control methods, the proposed control strategy not only effectively suppresses body vibration and significantly enhances ride comfort, but also stabilizes suspension rattle space (SRS) and dynamic tire load (DTL) within safe limits, thereby comprehensively optimizing vehicle dynamic performance. It provides a complete and feasible technical solution for addressing suspension control problems involving actuator delays.

To improve the braking pressure tracking performance of the electro-hydraulic braking (EHB) system, this paper proposes a high-precision brake pressure control strategy. Considering the effects of nonlinear friction and external disturbances on pressure control, a master cylinder pressure dynamic model is established, and a hierarchical control architecture is constructed, consisting of an upper-level pressure servo loop and a lower-level motor current loop. An improved PINN–LuGre friction model is introduced to enhance the accuracy of nonlinear friction characterization, while an improved linear extended state observer (ILESO) is employed to estimate and compensate for external disturbances in real time. Based on this, the upper-level pressure servo loop adopts an improved super-twisting sliding mode control (ISTSMC) scheme to achieve robust pressure tracking, and the lower-level motor current loop achieves precise actuation based on feedforward decoupling. Hardware-in-the-loop (HiL) experimental results show that the proposed strategy improves pressure tracking accuracy under ramp and step conditions. For low- and high-frequency sinusoidal conditions, the RMS error is significantly reduced, validating its effectiveness.

This paper addresses the impact of complex mining environments and the nonlinear dynamics of hinged mining trucks on reverse path tracking control for autonomous mining trucks. We propose a neural-network-based sliding mode control (NN-SMC)-based control strategy for reverse motion to improve tracking accuracy and robustness. First, a tractor–trailer dynamic model is built, and the force characteristics at the coupling joint are analyzed to derive the reverse interaction forces, which simplifies trailer modeling and avoids the influence of uncertain tractor parameters. Next, a control scheme matching the simplified model is developed, where an optimized sliding surface is designed and a neural network adaptively tunes control parameters to reduce chattering and improve adaptability to challenging conditions. Finally, hardware-in-the-loop tests validate the simulation results. Both simulation and experiments show that, compared with conventional SMC, the proposed method reduces lateral displacement error by 13.98% and heading error by 18.96%, demonstrating the effectiveness of the control approach.

To address the issue of vehicle instability and veering during braking when a single wheel fails in an electric vehicle’s electromechanical braking (EMB) system, an integrated application-oriented control framework based on adaptive sliding mode control (ASMC) is proposed. To address the shortcomings of SMC—such as difficulty in suppressing oscillations and the high workload associated with parameter tuning—a novel composite reaching law function was designed, and the TD3 algorithm was employed to optimize the sliding mode control parameters. When a failure in the EMB system is detected, the upper-layer control uses an improved ASMC algorithm to calculate the vehicle’s additional yaw moment. The lower-layer control employs an optimal control algorithm to distribute braking force, taking into account braking intensity, yaw moment, and tire utilization. This approach is integrated with sliding mode steering control to enhance vehicle stability during braking. To meet the driver’s braking requirements, a backpropagation (BP) neural network is first employed to identify braking intent. Based on this, the additional yaw moment is calculated by the upper-layer controller, and the brake force distribution is optimized through the lower-layer controller, thereby improving the vehicle’s stability. Through co-simulation analysis using Simulink-2024a and CarSim-2019.1, the results show that, compared to traditional algorithms, the proposed hierarchical control strategy reduced the maximum sideslip angle by 51.4%, decreased the maximum yaw rate by 47.2%, and reduced the maximum lateral offset by 45.6%. This control strategy enables enhanced stability across various braking intensity conditions.

To address the issues of poor operational stability and insufficient omnidirectional leveling capability of tracked electric agricultural machinery in hilly and mountainous areas, this paper presents an electromechanical omnidirectional leveling chassis architecture based on a dual-layer independently driven architecture. Utilizing servo electric cylinders as actuators, a leveling mechanism with physically decoupled upper lateral and lower longitudinal layers was constructed. Based on this structure, a mathematical model relating the electric cylinder displacement to the platform posture was established. Furthermore, an ADAMS dynamics simulation platform was built to conduct simulation analysis and prototype experiments. The results indicate that the designed dual-layer independently driven chassis can achieve a theoretical leveling range of ±28.6° laterally and ±27.7° longitudinally, operating smoothly under the rated 25° slope condition. Dynamic tests demonstrate that when the prototype travels at 3 km/h, the residual inclination angle of the platform can be controlled within ±0.9° in 3 s. The simulation and experimental results are in high agreement, comprehensively revealing the dynamic coupling relationship among the electric cylinder displacement, platform posture, and driving thrust. The experiments verify that the electromechanical omnidirectional leveling system can accomplish adaptive leveling under slope conditions, exhibiting superior performance regarding response speed, control accuracy, and disturbance rejection, with the thrust deviation rate between simulation and experiment within 6.71%.