Cooperative Control Of Autonomous Vehicles Research Articles

Opposition-based differential evolution for synchronized control of multi-agent systems with uncertain nonlinear dynamics

Multi-agent systems (MAS) have gained increasing research attention in recent years due to their wide applications in cooperative control of autonomous vehicles, sensor networks, robotics, etc. However, designing effective distributed control strategies for MAS with complex dynamics remains challenging. This paper proposes an improved differential evolution (DE) based cooperative control strategy to stabilize and synchronize MAS with uncertain nonlinear dynamics. Specifically, we develop a new mutation strategy inspired by opposition-based learning to enhance the exploration ability and adopt an adaptive parameter mechanism to balance exploration and exploitation. Moreover, a distributed sliding mode term is incorporated in the control input to counteract uncertainties and external disturbances. Rigorous stability analysis shows that the closed-loop MAS can achieve synchronization under the proposed control strategy. Extensive simulations demonstrate that compared to other state-of-the-art DE algorithms and cooperative control methods, the proposed strategy achieves lower synchronization error, control cost, higher convergence speed, and higher accuracy for MAS with different scales, dynamics, and uncertainties.

Cooperative Localization Using the 3D Euler–Lagrange Vehicle Model

Kalman filter-based cooperative localization (CL) algorithms have been shown to significantly improve pose estimations within networks of vehicles but have relied predominantly on two-dimensional kinematic models of the member agents. An inherent deficiency of the commonly employed kinematic vehicle model is the ineffectiveness of CL with only relative position measurements. In this work, we present a singularity-free CL using the full three-dimensional (3D) nonlinear dynamic vehicle model suitable for decentralized control and navigation of heterogeneous networks. We develop the algorithm, present Monte Carlo simulation results with relative pose measurements, and assess the algorithm performance as the number of measurements increases. We further demonstrate that CL with only relative position measurements is effective when using the dynamic model and benefits from increasing number of measurements. We also evaluate the performance of CL with respect to measurement task distribution, which is important in cooperative control of autonomous vehicles.

Connected Cooperative Control of Autonomous Vehicles During Unexpected Road Situations

This article discusses how connected cooperative control of autonomous vehicles (AVs) can help in providing safe and comfortable mobility during unexpected road situations. Driving AVs in urban areas poses a big challenge due to the complexity of the traffic rules as well as unexpected scenarios involved. In these situations, an inter-vehicle communication system can be of great help. Cooperation between multiple AVs is possible with the development of vehicular communication. In particular, state estimation can be improved with multiple sources of information gathered from different vehicles. Cooperative state estimation can also improve robustness against communication failure. With future trajectories shared among nearby vehicles, the motion can be coordinated to make navigation safer and smoother for AVs. For vehicular communication, the IEEE 802.11p standard has been designed to allow information exchange between high-speed cars, and between vehicles and roadside infrastructure. Other wireless communication technologies, such as 3G, 4G, and WiFi, are also suggested.