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 presence of multiple generator units (GUs) in the hybrid electric propulsion system (HEPS) of flying cars poses higher requirements for the design of energy management strategy (EMS) since the decision made by one GU impacts the state and decisions of others due to the coupling electrical dynamics of the system. In this paper, a data-driven cooperative differential game (CDG) based EMS is proposed to improve the performance of the fuel consumption and exhaust gas temperature (EGT) of those GUs as well as the stability of the state of charge (SOC) of the battery through coordination and cooperation. The energy management problem is first formulated as a general two-player differential game. To improve the computational efficiency as well as the control performance, a novel neural network-based adaptive dynamic programming (ADP) algorithm is proposed to approximate the Nash equilibrium (NE) and Pareto solution (PS) of the non-cooperative differential game (NCDG) and CDG During real-time application, and a comparison mechanism is designed so that the solution with a smaller cost is applied to the system to further improve performance. The simulation results indicate that the proposed CDG-based EMS can not only reduce the equivalent fuel consumption by 2.67% and 6.22% compared with that of NCDG and rule-based EMS, but also obtain a better overall and individual performance of the two GUs simultaneously, demonstrating the effectiveness of the proposed approach in reducing the fuel consumption, EGT as well as maintaining the state of charge (SOC) of the battery.

This study proposes a sequential clustering quadratic programming (SCQP) method for the energy management strategies of plug-in hybrid electric vehicles (PHEVs). In this method, the clustering algorithm is introduced to gather up the points with a smaller iteration step size in the iteration process. The clustering results are utilized to design the termination criterion based on the distance between the cluster centers of various iteration domains. In the case that the distance varies within the preset range, it indicates that the current iteration point is sufficiently close to the optimal point. So that the criterion turns to terminate the computation to reduce unnecessary iteration steps. To analyze the convergence of the method with the designed criterion, the mathematical illustrations are proposed. In the mathematical illustrations, the monotonicity of the clustering objective function is firstly given. Then, the theorem of feasibility for the solution obtained by the designed criterion is proved. On the basis of aforementioned conclusions, the convergence of the SCQP method is obtained. Finally, the performance of the proposed method is validated both in simulation test and hardware-in-loop (HIL) test. The simulation results reveal that the PHEV achieves 8.81% and 7.74% less fuel consumption under two driving cycles. And the average iteration number of the proposed method is obviously reduced compared with the conventional SQP. The HIL results reveal that the proposed strategy exhibits similar performance in both real controller and simulation. The energy saving and real-time performance can be verified.

For automated driving vehicles, trajectory planning is responsible for obtaining feasible trajectories with velocity profiles according to driving environments. From the perspective of trajectory planning, multiple uncertainties of environments and tracking deviations are two significant factors affecting driving safety. The former disturbs the judgment of trajectory planning on the environments, and the latter reduces the tracking accuracy of planned trajectories. To solve these problems, an improved model predictive control (MPC) trajectory planning method is proposed in this paper. Firstly, a Kalman filter fusion method is carried out to predict obstacle trajectory and their uncertainty, which combines model-based and data-based prediction methods. Based on the prediction results, a tube-based MPC trajectory planning method is applied to plan a reference trajectory with a small tracking deviation. The tube-based MPC is composed of two parts. One is the MPC with tightened constraints that is used to plan a feasible trajectory according to a nominal vehicle system and driving environment. The other is a state feedback control that is proposed to adjust the above planned trajectory to reduce the tracking deviations. To our knowledge, this paper proposes Kalman filter fusion and tube-based MPC planning method for the first time to consider the uncertainties of trajectory prediction and tracking control meanwhile in the planning. The planning method is verified by simulations and experiments in multiple scenes. Results show that the method is suitable for both static and dynamic scenes. Compared with applying the basic prediction method, the lateral deviation of the proposed method from the ideal trajectory is decreased by 46.5%. Compared with the nominal MPC method, the lateral tracking deviations of the proposed method are decreased by 77.42%.

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.

In this paper, an all-wheel independently driven and all-wheel independently steered unmanned ground vehicle (UGV) is described. This paper investigates the hierarchical chassis yaw dynamics control (CYDC) and the tyre force control of the UGV in the remote control mode (RCM). The hierarchical CYDC scheme in RCM is proposed. As the key part in the control scheme, a yaw moment controller is proposed to deal with the oversteer problem of the UGV. Through the robust-based pole placement technique, the ideal poles' zones of the lateral UGV dynamics system are able to be tuned to meet different dynamics behavior requirements in different UGV tasks. The robust state feedback yaw dynamics controller is investigated based on the linear matrix inequalities approach. It considers the unavoidable parametric disturbance and uncertainty, such as the variation of the UGV's mass, yaw inertia, and tyre-road characteristics. In addition, in order to improve its performance in off-road conditions, the tyre traction force distribution algorithm and sliding mode wheel slip controller are designed to negotiate uneven terrains. The experiments in paved and off-road conditions are conducted to demonstrate the performance of the proposed controller.

This paper proposes a novel aerial manipulator with tandem ducted fans, which takes both trafficability and effective loading into account. The aerial manipulator is particularly suitable for grasping in complex and narrow environment, in which traditional multi-rotor and helicopter would be inaccessible. The comprehensive integrated dynamic model is established by taking the aerial vehicle dynamics and manipulator dynamics as a whole. On this basis, a multilayer composite controller with feedforward compensation is designed, considering the mutual reactive influence between the aerial vehicle and the manipulator to improve the stability of the system under the motion of the manipulator. The simulation and actual flight tests verify the effectiveness of the design and show good stability and tracking performance of the system.

There has been an increasing interest in the use of correlation filters for visual object tracking due to their impressive tracking performance. However, existing correlation filter based tracking methods, such as Struck and Kernelized Correlation Filter (KCF), cannot always solve tracking problems in complicated conditions such as heavy occlusion and aggressive motion. In this paper, we proposed a real-time visual tracker via a robust KCF. We start by implementing a search window alignment, based on a motion model with uncertainty, which increases the tracking accuracy for fast moving targets and reduces the padding value to accelerate tracking speed. Next, we establish a combined confidence measurement including occlusion information, which is utilized for robust updating. Then we apply an adaptive Kalman filter to improve the tracking accuracy. Qualitative and quantitative experimental results show that the proposed algorithm outperforms the state-of-the-art methods such as KCF and Struck.

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.