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During patrol and surveillance tasks, attitude control is crucial for improving the terrain adaptability of unmanned wheel-legged hybrid vehicles. This paper proposes an attitude control strategy for unmanned wheel-legged hybrid vehicles, considering the contact of the wheels and ground. The proposed method can naturally achieve torque control efficiently of each joint actuator and wheel-side actuator and avoid the discrepancy between off-road terrain and stability. First, an inverse kinematics model is established to resolve the body and each joint rotation angle, and the dynamic model is built based on the multi rigid body theory, considering the contact points planning of wheel and ground. Considering the nonholonomic constraint of the structure scheme, a hierarchical real-time attitude controller for a wheel-legged vehicle is proposed. The upper layer calculates the contact points of each wheel and the ground through the quadratic programming algorithm, and the lower layer is divided into a legged motion generator and a wheel motion generator by a mathematical analysis method. Finally, the proposed method is applied to achieve the tracking and control of the whole-body trajectory. The proposed strategy can achieve the decoupling of wheeled motion generator and legged motion generator, and improve control efficiency.

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.

The paper proposes a motion control framework for the unmanned wheel-legged hybrid vehicle to track the motion trajectory considering uncertain disturbances. The whole-body dynamic model is built with the contact force of each rolling wheel, which serves as the foundation to obtain trajectory tracking. The angular momentum and linear momentum are optimized by the robust model predictive control algorithm considering the soft constraint of the relaxation variable. The contact force between wheel and ground is solved by the quadratic programming algorithm to efficiently obtain the flexion/extension joint and wheel motion planning. Then, the explicit algorithm to calculate the torque commands of the flexion/extension joint considering the feed-forward torque and feedback torque to improve the control accuracy. Simulation results validate that the control framework based on the robust model predictive control algorithm can solve the uncertain disturbances in process of the vehicle running on the rough road.

In this paper, a novel algorithm is proposed for the motion planning and path following automated cars with the incorporation of a collision avoidance strategy. This approach is aligned with an optimal reinforcement learning (RL) coupled with a new risk assessment approach. For this purpose, a probabilistic function-based collision avoidance strategy is developed, and the proposed RL approach learns the probability distributions of the adjacent and leading vehicles. Subsequently, the nonlinear model predictive control (NMPC) algorithm approximates the optimal steering input and the required yaw moment to follow the safest and shortest path through the optimal RL-based probabilistic risk function framework. Additionally, it is attempted to maintain the travel speed for the ego vehicle stable such that the ride comfort is also offered for the vehicle occupants. For this purpose, the steering system dynamics are also incorporated to provide a thorough understanding of the vehicle dynamics characteristic. Different driving scenarios are employed in the present paper to evaluate the proposed algorithm’s effectiveness.

To improve tracked vehicle ride comfort and minimize weapon's vibration, a coordinated control strategy is developed for tracked vehicles' semi-active suspension systems. A model with eight degrees-of-freedom for a tracked vehicle equipped with magnetorheological dampers is established, and is followed by the formulation of a sliding mode controller. The proposed control algorithm is a localized-based controller that can change its target location in the tracked vehicle to where it is needed most. A co-simulation system model including a six-wheel tracked vehicle multi-body dynamics model, coordinated control strategy, and magnetorheological damper force allocator is developed to analyze the ride performance under bump and random road excitations. The simulation results demonstrate that the proposed strategy is very effective in improving the vehicle's ride performance and is much better than the traditional skyhook controllers. The innovation of this paper can be concluded as the coordinated control strategy can simultaneously improve vertical acceleration and pitch acceleration for the hull, which is of great importance for combat situations.

Vehicle performance is largely affected by the properties of the suspension system, where semi-active suspension has been widely used in mass production of vehicles owing to its characteristics such as internal stability and low energy consumption. To solve the contradiction between ride comfort and road handling, road estimation based semi-active suspension has received considerable attention in recent years. In order to provide accurate estimation for advanced control strategies applications, this paper aims to develop a new method that can provide precise road class estimation based on measurable suspension system response (i.e. sprung mass acceleration, unsprung mass acceleration and rattle space). The response signal is first decomposed using wavelet packet analysis, and features in both time and frequency domains are subsequently extracted. Then, minimum redundancy maximum relevance (mRMR) is utilized to select superior features. Finally, a probabilistic neural network (PNN) classifier is applied to determine road classification output. The most representative semi-active control strategy, i.e. skyhook control, is used to validate this method, and simulation results with varying conditions including different control parameters and sprung mass are compared. The results show that unsprung mass acceleration is most suitable for road classification, and more robust to varying conditions in comparison to other responses.

The camera always suffers from image instability on the moving vehicle due to unintentional vibrations caused by road roughness. This article presents an adaptive neural network approach mixed with linear quadratic regulator control for a quarter-car active suspension system to stabilize the image captured area of the camera. An active suspension system provides extra force through the actuator which allows it to suppress vertical vibration of sprung mass. First, to deal with the road disturbance and the system uncertainties, radial basis function neural network is proposed to construct the map between the state error and the compensation component, which can correct the optimal state-feedback control law. The weights matrix of radial basis function neural network is adaptively tuned online. Then, the closed-loop stability and asymptotic convergence performance is guaranteed by Lyapunov analysis. Finally, the simulation results demonstrate that the proposed controller effectively suppresses the vibration of the camera and enhances the stabilization of the entire camera, where different excitations are considered to validate the system performance.