Vehicle dynamics

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

Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results

Abstract: Interest in control of multiple autonomous vehicles continues to grow for applications such as weather monitoring, geographical mapping fauna surveys, and extra-terrestrial exploration. The task of persistent surveillance is of particular significance in that the target area needs to be continuously surveyed, minimizing the time between visitations to the same region. This distinction from one-time coverage does not allow a straightforward application of most exploration techniques to the problem, though ideas from these methods can still be used. The aerial vehicle dynamic and endurance constraints add additional complexity to the autonomous control problem, whereas stochastic environments and vehicle failures introduce uncertainty. In this work, we investigate techniques for high-level control, that are scalable, reliable, efficient, and robust to problem dynamics. Next, we suggest a modification to the control policy to account for aircraft dynamic constraints. We also devise a health monitoring policy and a control policy modification to improve performance under endurance constraints. The Vehicle Swarm Technology Laboratory-a hardware testbed developed at Boeing Research and Technology, Seattle, WA, for evaluating a swarm of unmanned air vehicles-is then described, and these control policies are tested in a realistic scenario.

Optimal Rough Terrain Trajectory Generation for Wheeled Mobile Robots

Abstract: An algorithm is presented for wheeled mobile robot trajectory generation that achieves a high degree of generality and efficiency. The generality derives from numerical linearization and inversion of forward models of propulsion, suspension, and motion for any type of vehicle. Efficiency is achieved by using fast numerical optimization techniques and effective initial guesses for the vehicle controls parameters. This approach can accommodate such effects as rough terrain, vehicle dynamics, models of wheel-terrain interaction, and other effects of interest. It can accommodate boundary and internal constraints while optimizing an objective function that might, for example, involve such criteria as obstacle avoidance, cost, risk, time, or energy consumption in any combination. The algorithm is efficient enough to use in real time due to its use of nonlinear programming techniques that involve searching the space of parameterized vehicle controls. Applications of the presented methods are demonstrated for planetary rovers.

Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning

Abstract: In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in the horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered in our control design. Based on the dynamics model derived in the discrete-time domain, two neural networks (NNs), including a critic and an action NN, are integrated into our adaptive control design. The critic NN is introduced to evaluate the long-time performance of the designed control in the current time step, and the action NN is used to compensate for the unknown dynamics. To eliminate the AUV’s control input nonlinearities, a compensation item is also designed in the adaptive control. Rigorous theoretical analysis is performed to prove the stability and performance of the proposed control law. Moreover, the robustness and effectiveness of the proposed control method are tested and validated through extensive numerical simulation results.

Model Predictive Control for Vehicle Stabilization at the Limits of Handling

Abstract: Recent developments in vehicle steering systems offer new opportunities to measure the steering torque and reliably estimate the vehicle sideslip and the tire-road friction coefficient. This paper presents an approach to vehicle stabilization that leverages these estimates to define state boundaries that exclude unstable vehicle dynamics and utilizes a model predictive envelope controller to bound the vehicle motion within this stable region of the state space. This approach provides a large operating region accessible by the driver and smooth interventions at the stability boundaries. Experimental results obtained with a steer-by-wire vehicle and a proof of envelope invariance demonstrate the efficacy of the envelope controller in controlling the vehicle at the limits of handling.

A Practical Visual Servo Control for an Unmanned Aerial Vehicle

Abstract: An image-based visual servo control is presented for an unmanned aerial vehicle (UAV) capable of stationary or quasi-stationary flight with the camera mounted onboard the vehicle. The target considered consists of a finite set of stationary and disjoint points lying in a plane. Control of the position and orientation dynamics is decoupled using a visual error based on spherical centroid data, along with estimations of the linear velocity and the gravitational inertial direction extracted from image features and an embedded inertial measurement unit. The visual error used compensates for poor conditioning of the image Jacobian matrix by introducing a nonhomogeneous gain term adapted to the visual sensitivity of the error measurements. A nonlinear controller, that ensures exponential convergence of the system considered, is derived for the full dynamics of the system using control Lyapunov function design techniques. Experimental results on a quadrotor UAV, developed by the French Atomic Energy Commission, demonstrate the robustness and performance of the proposed control strategy.