This paper addresses the issue of obstacles encountered by intelligent vehicles during their movement in agricultural fields. The traditional artificial potential field (APF) method for obstacle avoidance often results in problems such as unreachable target points and the vehicle getting trapped in local minima, preventing it from moving. To overcome these issues, we propose dynamically adjusting the size of the potential field by incorporating the relative distance between the vehicle s real-time position and the target point as a criterion. Additionally, we apply the simulated annealing (SA) method, which uses its inherent search probability to escape local minima. Finally, path smoothing is performed to compensate for the shortcomings of the traditional APF algorithm. MATLAB simulations of the improved APF-based obstacle avoidance path planning confirm the feasibility of the proposed method.

This paper proposes an event-triggered control method for the CAN-network delayed human-machine shared steering system. The uncertain model parameter of vehicle speed is handled by the polytypic technology, and then represented by a new state with fewer vertices. Thus, a driver-vehicle path-tracking model is built. After that, the communication model of the shared control scheme considering the CAN network delays is redefined by the event-triggered system (ETS). Instead of employing the periodic communication from the sensor to the controller, it only occurs when the triggered condition of ETS is satisfied. Such a design can reduce the communication load and improve the usage of network resources. Finally, a robust state-feedback controller with the parallel distribution method is adopted to guarantee vehicle path-tracking accuracy, while reducing driver steering efforts. Through constructing a Lyapunov-Krasovskii function, the asymptotic stability of the system is proved. Some essential conditions are derived to obtain the controller and triggered parameters. The hardware-in-the-loop (HIL) tests by Carsim/ Matlab joint platform are further used to validate the proposed shared assistance control strategy. The results illustrate the effectiveness to ensure the prescribed system performance while using fewer CAN network resources.Note to Practitioners—The driver assistance steering system plays an important role to enhance driving performance. Almost all road vehicles have been equipped with the Electrical Power Steering (EPS) function. Based on the information feedback from the onboard sensors, it can generate an extra steering input to assist the driver in real time. The advanced steering control technology, such as Servolectric of ZF and ESTEERTM of Delphi, can significantly reduce traffic accidents and improve the driving experience. Recently, the Original Equipment Manufacturers (OEMs) bring various advanced driver assistance systems to further guarantee the vehicle safety. This is usually accompanied by the communication load of the in-vehicle network CAN (Controller Area Network). Meanwhile, the x-by-wire technique can also induce the possibility of CAN delays. It would deteriorate the control performance. Hence, this paper introduces the event-triggered system to develop the steering assistance controller. The CAN network delays are also integrated into the human-machine shared steering model. The communication only occurs when the triggered condition of ETS is satisfied. A robust control method is further presented to ensure the system prescribed performance. This technology aims to be a paradigm to design active safety control units. Note that some poor driver states, such as fatigued driving and distracted driving have not been considered in this study for the design of the assistance controller. Future research will focus on developing a shared controller to accommodate time-varying driver characteristics.

Aiming at the problems of low tracking accuracy and more steering control times of the existing unilateral braking tracked vehicle tracking control algorithm, an adaptive path tracking algorithm for unilateral braking tracked vehicle based on Particle swarm optimization (PSO) is proposed. Based on the preview tracking model, the track vehicle path tracking method is studied; In order to improve the adaptive ability of the preview tracking model, a fitness function is constructed based on the tracking accuracy and steering control times. The lateral error is used as the main decision parameter, and the forward-looking distance in the preview tracking model is determined in real time by particle swarm optimization algorithm; In order to reduce the calculation time of particle swarm optimization and carry out local search as soon as possible, the inertia weight coefficient and particle state update strategy in PSO algorithm are improved, and chaos factor is introduced. In this paper, the tracking accuracy and steering control times of the algorithm are comprehensively evaluated through simulation and actual tests on the test platform of the modified 3b55 tracked transport vehicle. Compared with SSA algorithm, the improved PSO algorithm has faster convergence speed, higher tracking accuracy and fewer steering control times. The research results can provide innovative ideas and technical support for the automatic navigation technology of unilateral braking tracked vehicles.

Aiming at the problems of low tracking accuracy and more steering control times of the existing unilateral braking tracked vehicle tracking control algorithm, an adaptive path tracking algorithm for unilateral braking tracked vehicle based on Particle swarm optimization (PSO) is proposed. Based on the preview tracking model, the track vehicle path tracking method is studied; In order to improve the adaptive ability of the preview tracking model, a fitness function is constructed based on the tracking accuracy and steering control times. The lateral error is used as the main decision parameter, and the forward-looking distance in the preview tracking model is determined in real time by particle swarm optimization algorithm; In order to reduce the calculation time of particle swarm optimization and carry out local search as soon as possible, the inertia weight coefficient and particle state update strategy in PSO algorithm are improved, and chaos factor is introduced. In this paper, the tracking accuracy and steering control times of the algorithm are comprehensively evaluated through simulation and actual tests on the test platform of the modified 3b55 tracked transport vehicle. Compared with SSA algorithm, the improved PSO algorithm has faster convergence speed, higher tracking accuracy and fewer steering control times. The research results can provide innovative ideas and technical support for the automatic navigation technology of unilateral braking tracked vehicles.

The paper proposes a tracked vehicle slope path track control algorithm based on MPC hilly mountainous area motor differential control. First, a tracked vehicle slope driving slip model is established to achieve the accurate estimation slope position. Second, the desired acceleration and desired angular acceleration are obtained as inputs by incorporating the intended linear and angular velocities, and the actual motivation required of the tracked vehicle when steering the vehicle on the slope is estimated by the MPC, and then the driving force is obtained as the actual control signal through the road surface response and kinematics solving to achieve the slope control of tracked vehicles. The actual control signal is obtained from the driving force through the road surface response and kinematics solution to realize the tracked vehicle slope path tracking control. The test outcomes demonstrate that our path tracking algorithm exhibits superior performance compared to the proportion integral differential (PID) control method during the vehicle's travel on a slope.

The paper concerns the contemporary approaches of Distributed Drive Electric Vehicles (DDEVs) to the development process and their problems. It starts with describing the complete independent control of a vehicle's throttling and braking forces through in-wheel motors. The study then goes further down the line and addresses vehicle reaction rate, eco-friendliness, and road-controlling precision. The paper uses the model of complete vehicle dynamics to develop a controller that would collect the specified characteristics of the given vehicle. It will make it more stable and safe on the road and explain the controller design and optimization procedure in this way. Furthermore, simulation methods are used to model the performance of different control strategies and algorithms and how they react to specific driving situations. The research findings, imply that DDEVs enable the exploitation of different kinds of torque management values, loss of unstopped energy, and stability enhancement, which are crucial in the effort toward shedding the negative emission detriments from our transportation systems.

Distributed drive electric vehicles (DDEVs) eliminate the complex drivetrain. The independently driven in- wheel motors also endow the vehicle with more ability for improving maneuverability. To this end, this paper proposes an integrated control framework of torque vectoring (TV) and active front-wheel steering system (AFS) to ensure the vehicle lateral motion stability performance. First, the polytope method with finite vertices is employed to deal with the system uncertainties and simplify the modeling structure, based on which a distributed model predictive control is adopted to construct a dynamic interactive model between agents. Then, through introducing the game theory, a distributed parallel control scheme is developed to obtain the cooperative strategy of agents. Such a design can also satisfy the modular and scalable requirement for integrated chassis control. To ensure the system asymptotic stability, the terminal input combined with the terminal cost function are treated as the constraints in the game paradigm and then transformed as the linear matrix inequalities. Furthermore, a robust$\text{H}\infty $compensation method is used to suppress the system disturbance. Finally, the hardware-in-the-loop (HIL) tests are conducted to assess the control performance. The results verify the proposed integrated control scheme is effective to enhance the vehicle handling stability.

Aggressive driving behavior has greatly endangered vehicle safety and posed challenges to the design of advanced driver-assistance systems (ADASs). A novel driver–automation cooperative shared control system is proposed in this article to make steering assistance actions better cooperate with aggressive drivers. Based on investigating shared control modes, a driving activity parameter for drivers is introduced, which aims to modulate the shared control authority and mitigate the conflicts between aggressive drivers and ADAS. A polytope represented by finite vertices is employed to handle uncertain parameters, including driving activity and longitudinal velocity. Then, an H$\infty $robust output-feedback control method satisfying the regional pole assignment is presented to provide robustness and stability of the polytope space while simplifying the control structure through reducing vertices. The driver-in-the-loop simulator experiments are carried out to verify the proposed controller, in which the driver model parameters are identified. The results demonstrate that the developed assistance controller can effectively ensure path-tracking accuracy and simultaneously improve aggressive drivers’ comfort.