Vehicle dynamics

About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.

Potential Cyberattacks on Automated Vehicles

Abstract: Vehicle automation has been one of the fundamental applications within the field of intelligent transportation systems (ITS) since the start of ITS research in the mid-1980s. For most of this time, it has been generally viewed as a futuristic concept that is not close to being ready for deployment. However, recent development of “self-driving” cars and the announcement by car manufacturers of their deployment by 2020 show that this is becoming a reality. The ITS industry has already been focusing much of its attention on the concepts of “connected vehicles” (United States) or “cooperative ITS” (Europe). These concepts are based on communication of data among vehicles (V2V) and/or between vehicles and the infrastructure (V2I/I2V) to provide the information needed to implement ITS applications. The separate threads of automated vehicles and cooperative ITS have not yet been thoroughly woven together, but this will be a necessary step in the near future because the cooperative exchange of data will provide vital inputs to improve the performance and safety of the automation systems. Thus, it is important to start thinking about the cybersecurity implications of cooperative automated vehicle systems. In this paper, we investigate the potential cyberattacks specific to automated vehicles, with their special needs and vulnerabilities. We analyze the threats on autonomous automated vehicles and cooperative automated vehicles. This analysis shows the need for considerably more redundancy than many have been expecting. We also raise awareness to generate discussion about these threats at this early stage in the development of vehicle automation systems.

Nonlinear Robust Adaptive Control of Flexible Air-Breathing Hypersonic Vehicles

Abstract: This paper describes the design of a nonlinear robust adaptive controller for a flexible air-breathing hypersonic vehicle model. Because of the complexity of a first-principle model of the vehicle dynamics, a control-oriented model is adopted for design and stability analysis. This simplified model retains the dominant features of the higher-fidelity model, including the nonminimum phase behavior of the flight-path angle dynamics, the flexibility effects, and the strong coupling between the engine and flight dynamics. A combination of nonlinear sequential loop closure and adaptive dynamic inversion is adopted for the design of a dynamic state-feedback controller that provides stable tracking of the velocity and altitude reference trajectories and imposes a desired set point for the angle of attack. A complete characterization of the internal dynamics of the model is derived for a Lyapunov-based stability analysis of the closed-loop system, which includes the structural dynamics. The proposed methodology addresses the issue of stability robustness with respect to both parametric model uncertainty, which naturally arises when adopting reduced-complexity models for control design, and dynamic perturbations due to the flexible dynamics. Simulation results from the full nonlinear model show the effectiveness of the controller.

Output Feedback Control of a Quadrotor UAV Using Neural Networks

Abstract: In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.

Propulsion system design of electric and hybrid vehicles

Abstract: There is a growing interest in electric and hybrid-electric vehicles due to environmental concerns. Efforts are directed toward developing an improved propulsion system for electric and hybrid-electric vehicles applications. This paper is aimed at developing the system design philosophies of electric and hybrid vehicle propulsion systems. The vehicles' dynamics are studied in an attempt to find an optimal torque-speed profile for the electric propulsion system. This study reveals that the vehicles' operational constraints, such as initial acceleration and grade, can be met with minimum power rating if the power train can be operated mostly in the constant power region. Several examples are presented to demonstrate the importance of the constant power operation. Operation of several candidate motors in the constant power region are also examined. Their behaviors are compared and conclusions are made.

Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics

Abstract: In this brief, we consider the formation control problem of underactuated autonomous surface vehicles (ASVs) moving in a leader-follower formation, in the presence of uncertainties and ocean disturbances. A robust adaptive formation controller is developed by employing neural network and dynamic surface control technique. The stability of the design is proven via Lyapunov analysis where semiglobal uniform ultimate boundedness of the closed-loop signals is guaranteed. The advantages of the proposed formation controller are that: first, the proposed method only uses the measurements of line-of-sight range and angle by local sensors, no other information about the leader is required for control implementation; second, the developed neural formation controller is able to capture the vehicle dynamics without exact information of coriolis and centripetal force, hydrodynamic damping and disturbances from the environment. Comparative analysis with a model-based approach is given to demonstrate the effectiveness of the proposed method.