In driver-operated hydraulic luffing crane tasks, achieving precise motion control and effective load stabilization is essential for safe and efficient operation. This study develops a variable-weight nonlinear model predictive control (VW-NMPC) approach for driver-in-the-loop trajectory tracking and anti-sway regulation. A coupled nonlinear dynamic model integrating hydraulic, luffing, and payload dynamics is established. A virtual reference feedback mechanism is introduced to reduce feedback delays and suppress instability arising from strong nonlinearities and structural coupling. A state-dependent weighting mechanism dynamically adjusts control priorities according to driver commands and operating conditions, enabling balanced optimization in human–machine cooperation. Hardware-in-the-loop experiments show a velocity deviation of less than 2.53×10⁻² m/s and payload swing angles within 2.95×10⁻² rad under typical working scenarios. The proposed method significantly reduces operator dependence and enhances robustness, safety, and intelligence in crane operations across varying environments. These results provide a solid theoretical and practical foundation for next-generation intelligent crane control systems.

Muddy terrains present a particularly challenging environment for robotic applications. To address this, we developed a bioinspired ostrich foot (BiF), designed as a deformable framework informed by detailed anatomical and skeletal features of ostriches. The load-bearing capacity and withdrawal resistance of the BiF were examined through experimental validation, demonstrating superior performance relative to flat and cylindrical foot. Soil pressure distribution and particle image velocimetry analyses conducted for all three-foot types revealed that variations in pressure and velocity field distributions arise from the nonuniform distribution of compressed and flowing soil, which plays a critical role in mechanical performance. Building on these insights, we established a mapping between foot geometry and load-bearing characteristics. By introducing a dual-modal mechanism that accounts for both long-term flow and short-term relaxation, and incorporating foot shape as a variable parameter within a cumulation resistance framework, we developed a foot–mud rheological model tailored to muddy terrains. Validation through foot intrusion experiments confirmed the model’s accuracy.

With the complexity of automobile body forming and the improvement of process quality requirements, more efficient and accurate metal forming simulation methods are urgently needed. In the frame of one step positive forming algorithm, the improved perturbation method is introduced in Newton-Raphson iterative calculation. Along the tangent plane of the node on the spatial initial solution, a small displacement perturbation is applied to make the unbalance force on the spatial initial solution changed. The simplified method to process the perturbation of the node normal vector is applied to calculate the unbalance force gradient in the node tangent plane, which can be transformed into the one in the space coordinate system through the coordinate transformation matrix. The node coordinates of the spatial initial solution can be modified according to the unbalance force gradient descent direction. When calculating the unbalance force gradient, the region dependent on the perturbation can be found. Then the unbalanced force vector is calculated only in the region dependent on the perturbation, and the repetitive calculation of the unbalanced force vector in uncorrelated nodes is avoided. Because the method proposed does not involve solving of the system of equations, the ill condition of stiffness matrix is avoided, which is caused by local material yield deformation destabilization, and lead to instability or non-convergence of the computation process. Thus, the solving process is more stable. The validity of the algorithm proposed is proved by comparing the measurement of the practical drawn parts with the simulation results obtained by one step positive forming algorithm based on improved perturbation method, one step positive forming algorithm based on solving system of equations and one step inverse forming algorithm of DYNAFORM on the calculation accuracy and computational efficiency.

Powered knee prosthesis can enhance mobility by allowing voluntary control of knee flexion, thereby improving foot clearance for crossing obstacle. However, prior controllers have typically focused on crossing obstacle with the prosthesis serving as the trailing leg, while the scenario where the prosthesis leads has been overlooked. This paper introduces a self-adaptive volitional control method utilizing mechanical signals, enabling amputee to cross an obstacle with the prosthesis either as the leading or trailing leg, and even negotiate multiple continuous obstacles. In volitional controller, we partition the swing flexion phase into four conditions and calculate the desired maximum knee angle in real-time accordingly. We recruited amputees to participate in experiments, which included three distinct tasks: single obstacle crossing with the prosthesis leading, single obstacle crossing with the prosthesis trailing, and dual obstacles crossing. This work offers amputees capability to cross obstacle freely and naturally, a functionality that is currently unavailable.

In the frame of the one-step inverse forming algorithm, the improved gradient method is proposed for Newton-Raphson iterative computations, which replaces the previous iteration algorithm based on solving equations. This method commences from the element's unbalanced force, which is generated from variations of element shape in the finite element analysis. It continuously optimizes the node position coordinates to reduce the unbalanced force of the sheet metal. In each iteration step, the unbalanced force gradients of nodes, induced by small disturbances, are first calculated. The range normalization method is applied to calculate the node coordinates modifier. Along the descending direction of the unbalanced force gradient, the coordinates of the node on the initial blank are modified with the modification amount as the step length. Since the algorithm does not need to generate the overall stiffness matrix, nor does it need to solve the finite element equations, it avoids the local material yield instability leading to the ill-conditioned stiffness matrix, resulting in instability or non-convergence of the solution, which effectively improves the stability of the algorithm. Finally, the validity of the proposed algorithm is demonstrated by comparing the simulation results obtained by the method with those obtained through the module of the one-step inverse forming method in DYNAFORM and practical stamping parts.

Earthmoving equipment automation is limited by interactions with complex media, unstructured terrain and highly variable environmental conditions. Although research in this field has continued for nearly thirty years, a truly fully autonomous system remains unrealized. This paper examines automation technologies for earthmoving equipment in typical repetitive short-cycle loading operations and presents a systematic survey of core literature spanning over thirty years. Key techniques are grouped into four categories, environmental perception, path planning and tracking, autonomous loading and unloading and safety and risk assessment. The analysis indicates that current research primarily addresses improvements to individual stages or subsystems while system level optimization of the entire workflow is still lacking. Moreover, data physics hybrid methods show strong potential in earthmoving equipment automation applications by compensating for errors in physical models and improving the generalization of data driven models. Finally, the major challenges are summarized and directions for future research are proposed.

This study proposes a biomass microgeneration system based on porous combustion. We also investigated the efficient heat exchange between biomass syngas and free-piston Stirling engine generator (FPSEG) was conducted to achieve high-efficiency conversion from biomass to electrical energy. The porous media arrangement was optimised based on the power-generation and heat-transfer characteristics of the FPSEG in different porous media. Furthermore, the FPSEG power-generation characteristics under different air intake volumes were investigated. Specifically, we found the highest electrical efficiency was achieved when the porous medium comprised 20 mm alumina ceramic particles. The highest cold-start speed was observed for the 10 PPI Si-C foam ceramics. Moreover, through the optimised arrangement of these two porous media, a high thermal-electric conversion efficiency was achieved along with a rapid cold start-up. Additionally, with an increase in air intake, the rate of increase in the power generation of the system first increased and then decreased. The maximum rate of change in the power generation, reaching 0.74 W/s, occurred at an air intake of 8.8 m3/h. This study integrates porous media combustion technology with biomass gasification technology to promote the efficient utilisation of biomass energy. It offers new perspectives on electricity supply for remote villages, islands, and temporary facilities such as mobile hospitals, where access to conventional fossil fuels is chal-lenging, and energy supply economics are unfavorable.

The container automated guided vehicle (AGV) is gaining prominence owing to its efficient operation. This paper presents an electro-hydraulic proportional steering system and cascade proportional-integral-derivative control strategy suitable for container AGVs. Mathematical models of key components and mechanical mechanisms were constructed, and parameters affecting response speed and stability were analyzed. A linear active disturbance rejection control (LADRC) strategy was proposed to improve the anti-jamming capability of the system. The effects of the two control strategies were compared based on multi-software co-equivalent simulation. Results show that in the 12 s period sinusoidal tracking experiment, LADRC improved tracking accuracy by 53.85% and reduced deviation amplitude by 85%, and the anti-interference ability was stronger. The proposed system and control strategy were tested on a bench and prototype vehicle. The passing ability and operational efficiency of the container AGV under complex road conditions were improved by the proposed method.

Syngas produced from the gasification of organic feedstocks from biomass is one of the clean and sustainable sources of energy. The advantages of simple access and renewability of biomass energy can meet the energy needs of temporary power supply. This study presents a biomass power-generation system for vehicular applications. Using biomass and a free-piston Stirling engine generator (FPSEG) as the primary material and prime mover, respectively, biomass energy is converted into electricity by combusting the syngas to heat the FPSEG. Matching and key parameter design for biomass gasification and thermoelectric conversion systems within a power generation system were performed. A porous medium area was constructed using Si-C foam ceramics to obtain an energy-conversion experimental platform. The effects of bed height, porosity, porous-region diameter, and air-intake conditions on the power-generation performance were investigated, and optimisations were performed for the thermoelectric conversion system. The rate of increase during FPSEG power generation first increased and then decreased with increasing bed height, peaking at a bed height of 40 mm. An increasing porous-region diameter accelerated FPSEG power generation, whereas porosity changes in the porous media did not significantly affect the rate of change during FPSEG power generation. With increasing air intake, the rate of increase during power generation first increased and then decreased. The maximum change rate and the highest thermoelectric conversion efficiency of the power-generation system occurred at 9.5 m3/h and 6.5 m3/h (∼45.1

In this study, the adaptive adjustment technology, based on an Attitude and Heading Reference System, is applied to loaders, aiming to improve the rollover protection performance of articulated engineering vehicles. A dynamic rollover stability index for loaders is developed, coupling the roll angular velocity and the slope and height of the center of gravity. A nonlinear predictive model of the material weight is built, in order to attain the height of the center of gravity. An adaptive attitude adjustment system is proposed, totally based on electro-hydraulic proportional control technology, to adjust the attitude of the working system, so as to achieve a higher threshold value of the vehicle's anti-tip performance. Finally, the rollover stability index and control methods, as proposed in this article, are validated using experiments. The boundary values that trigger the system's operation are reduced to ensure safety, while the validation results prove that the proposed method is effective.

The restoration of a non-stationary composite signal to its original components is a challenging and intriguing task in the field of signal processing. Various methods have been developed to tackle this problem. A traditional method for this problem is Singular Spectrum Analysis(SSA), while it needs to know the component number and costs too much time. Empirical Mode Decomposition(EMD) is also frequently applied, which decomposes the composite signal into a set of components known as Intrinsic Mode Functions(IMFs). However, the EMD method has been criticized for lacking a strong theoretical foundation and being more of a mathematical trick. Another popular method, Empirical Wavelet Transform(EWT), divides the frequency spectrum of the target signal into predefined segments and applies wavelet transforms using specific wavelet bases. Recently, a new method called Variational Mode Decomposition(VMD) has been proposed. This method is based on the variational principle and transforms the decomposition problem into an optimization problem. VMD has a robust mathematical foundation and exhibits excellent performance. However, it does have some limitations, such as the requirement for prior information on the modal number and the existence of a serious deviation in the end regions(named “end effect”).In this paper, we propose a novel method for sequentially separating non-stationary composite signals. Our method is inspired by the variational principle and can accurately and adaptively recover the original modes one by one from the raw mixture, without any prior knowledge or assumptions regarding the modal number. Moreover, our method can determine the modal number during the separation process, providing significant convenience in real-world applications. Additionally, we address the issue of end effects by conducting a new end elongation for the composite signal before the decomposition operation. This approach effectively reduces the end effect to a much lower level compared to the VMD method. To further enhance the accuracy of our method, we introduce a refinement approach after the coarse extraction. Combined these techniques, the final decomposition results demonstrate that our novel method outperforms VMD, EMD, EWT and SSA methods in most cases.

The electro-hydraulic controlled bionic robots suffer from path contour deviation, which is affected by inertia, system lag, and control system accuracy. The focus of this paper is on the study of a data-driven system for the compensation of errors in the robot’s internal arithmetic model and its own motion. A deviation prediction model of the robot motion process is constructed using a machine learning approach, while the spatial error, generated in the transfer process between the input trajectory and the actual output, is de-parameterized and regarded as an attribute value of the robot’s motion process. Furthermore, a data-based adaptive compensation method is proposed. The simulation model and a hardware-in-the-loop simulation platform of the proposed control strategy are constructed, in order to verify the proposed control approach. Simulation and experimental results show that the proposed compensation strategy can significantly reduce system deviation.

Variational Mode Decomposition(VMD) method was proposed to separate non-stationary signal mixture by solving a optimization problem. This method is powerful and can reconstruct the signal components precisely when they are orthogonal(or quasi-orthogonal) in frequency domain. The crucial problem for VMD is that it requires the information of modal number before the decomposition. Also its applications are limited in 1D and 2D signal processing fields, of narrow scope.In this paper, by inheriting and developing the core idea of VMD, we build a general form for this method and extend it to the modal decomposition for common additive mixture, not only limited in signal processing. To overcome the obstacle of modal number, we sequentialize the generalized VMD method, such that the modes can be extracted one by one, without knowing the modal number a priori. After the generalization and sequentialization for the VMD, we apply them in different fields of additive case, such as texture segmentation, Gaussian Mixture Model(GMM), clustering, etc. From the experiments, we conclude that the generalized and sequentialized VMD methods can solve variety classical problems from the view of modal decomposition, which implies that our methods have higher generality and wider applicability. A raw Matlab code for this algorithm is shown in https://github.com/changwangke/SGVMD_additive_Clustering/blob/main/SGVMD_clustering.m.

In this article, a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed. The optimization system consists of three parts. First, the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event. Then, the Binary version of the hybrid Gray Wolf Optimization and Particle Swarm Optimization (BGWOPSO) algorithm is used to select features. And, the selected features are optimized and assigned different weights by the Biogeography-Based Optimization (BBO) algorithm. Finally, an improved K-Nearest Neighbor (KNN) classifier is employed for intention recognition. This classifier has the advantages of high accuracy, few parameters as well as low memory burden. Based on data from eight patients with transfemoral amputations, the optimization system is evaluated. The numerical results indicate that the proposed model can recognize nine daily locomotion modes (i.e., low-, mid-, and fast-speed level-ground walking, ramp ascent/decent, stair ascent/descent, and sit/stand) by only seven features, with an accuracy of 96.66% ± 0.68%. As for real-time prediction on a powered knee prosthesis, the shortest prediction time is only 9.8 ms. These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.

The difference in power demand and the driver’s operation in various operation stages make the loader have the problem of low energy utilization. Changeable operating objects and drastically changing loads have exacerbated the difficulty of energy-saving research in different operating stages of loaders. Therefore, based on identifying the operation stage and analyzing the load characteristics under typical working conditions of the loader, this paper proposes a shovel-loading cooperative control strategy. Specifically, the Drag Reduction Insertion (DRI) in the shoveling stage is realized based on learning the driving intention. Based on different operation stages, the load characteristics of different materials for shovel-loading are deeply analyzed, and the prediction research of the power output characteristics of the power unit is carried out. The shovel-loading cooperative control solves the problem of the poor economy caused by different power requirements in different operation stages and significantly reduces the impact of the driver’s operating experience.

This study presents a heat exchange mechanism analysis and an optimization of structural parameters of a builtin series combined microchannel heat dissipation platform, aiming to address the problems of low heat exchange and high pumping power caused by the mismatch in the selection of the structural parameters of the microchannel heat sink (MHS). A numerical calculation model was built in Fluent, and the temperature difference, pressure difference, and their ratio between the inlet and outlet of the MHS were used as the evaluation indicators. Using an orthogonal test, the influence of multiple parameters on the heat exchange effect was investigated. It is found that the shape of the microchannel is the main factor affecting the heat exchange phenomena, and the optimal combination of parameters such as the height, width, and length of the rectangular microchannel is obtained by a genetic algorithm. Physical transformation and heat exchange experiments were carried out. It was highlighted that the comprehensive performance of the optimized cooling system has been improved to varying degrees under different inlet flow conditions. At an inlet flow rate of 2.0 m3 h-1, the pressure difference and pumping power consumption are reduced by 24.9%, and the maximum temperature difference and heat dissipation power are increased by 21.0%. An experimental study on the heat exchange effect of MHSs with different numbers of serial pieces was carried out. With a number of MHSs of 15, the heat exchange is found to be sufficient, and the maximum heat exchange estimated was 9289.52 W.

The traditional cooling system employed in loaders lacks the capability to adjust the fan speed based on the working medium's temperature, resulting in significant power waste. This study introduces a high-power spray cooling system as a solution to this issue. A dedicated spray cooling experimental setup was designed and constructed, and the influence of different factors on the heat transfer performance of the system was studied through multi-factor orthogonal tests with the heat transfer power and energy consumption ratio as evaluation indexes. The results of range and variance analysis demonstrate that the cold water flow rate exerts a substantial impact on both the system's heat transfer power and pump power consumption. Additionally, increasing the nozzle incidence angle can enhance the system's energy consumption ratio. Under the optimal combination of heat transfer power factors, the constructed spray cooling system can achieve a heat dissipation of 44.526 kW at a pump power consumption of 1.184 kW, which meets the heat dissipation requirements of the engine coolant and verifies the feasibility of the proposed scheme. Furthermore, a multiple linear regression model for the power consumption ratio is provided. This investigation yields novel insights for augmenting the performance of loader cooling systems.

With the increasing installed capacity of wind power, higher requirements are put forward for the quality of wind power, but the randomness and intermittency of wind power seriously affect its quality and the stability of the power grid. Hydraulic wind power technology replaces the original gearbox with flexible transmission, which can effectively absorb wind speed pulsation and impact, smooth power transmission, reduce grid impact, as well as have the advantages of reducing cabin weight and construction cost to meet the needs of large-scale wind power development. However, how to further improve the overall efficiency of hydraulic wind turbine and wind power quality needs further research and exploration. This paper comprehensively summarizes the configuration, hydraulic transmission system, pitch control, hydraulic energy storage, etc., as well as analyzes the development of hydraulic wind turbine.