Vehicle dynamics

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

Estimation of slip angles using a model based estimator and GPS

Abstract: This paper demonstrates a method for estimating key vehicle states and sensor biases using Global Positioning System (GPS) and an Internal Navigation System (INS). Two Kalman filters, a model based filter and a kinematic filter, are used to integrate the INS sensors with GPS heading and velocity to provide a high update rate of the vehicle states and sensor biases. Additional key vehicle parameters, such as tire-cornering stiffness, are identified and used to correct the model based estimator. The vehicle estimated states compare favorable with values predicted with a theoretical model.

A Model-Free Control Strategy for Vehicle Lateral Stability With Adaptive Dynamic Programming

Abstract: This article presents a nonmodel-based controller design for vehicle dynamic systems to improve lateral stability, where output tracking control and adaptive dynamic programming approaches are employed to track the desired yaw rate and, at the same time, mitigate the sideslip angle, roll angle, and roll rate of the vehicle. Moreover, different from some existing optimization methods in control allocation, the proposed control strategies, which distribute tire forces by learning, are only using the information of states, input, and reference signal instead of the knowledge of the vehicle system. The iterative process repeatedly uses the information about state and input to calculate the feedback gain. It can significantly reduce the learning time and computational burden. The effectiveness of the proposed controller design method is shown by CarSim simulations.

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

Abstract: This paper discusses the derivation and implementation of a nonlinear model predictive control law for tracking reference trajectories and constrained control of a quadrotor platform. The approach uses the state-dependent coefficient form to capture the system nonlinearities into a pseudo-linear system matrix. The state-dependent coefficient form is derived following a rigorous analysis of aerial vehicle dynamics that systematically accounts for the peculiarities of such systems. The same state-dependent coefficient form is exploited for obtaining a nonlinear equivalent of the model predictive control. The nonlinear model predictive control law is derived by first transforming the continuous system into a sampled-data form and and then using a sequential quadratic programming solver while accounting for input, output and state constraints. The boundedness of the tracking errors using the sampled-data implementation is shown explicitly. The performance of the nonlinear controller is illustrated through representative simulations showing the tracking of several aggressive reference trajectories with and without disturbances.

Nonlinear Robust H-Infinity PID Controller for the Multivariable System Quadrotor

Abstract: This paper presents the methodology of design of a nonlinear robust controller for attitude regulation and its implementation in an experimental platform of an unmanned aerial vehicle (UAV) quadrotor. Details on the kinematic and dynamic modeling based on the Euler-Lagrange formalism are provided, as well as the particulars of the design of a nonlinear robust H-infinity PID controller to regulate the rotational moments. The performance and effectiveness of the proposed controller are tested in a simulation and an experimental platform. The performance of the proposed controller is compared with a conventional PID controller by using the integral square error (ISE) as performance parameter. Experimental results help to demonstrate the correct operation of the system for real-time applications in the presence of unmodeled dynamics and the uncertainties of the parameters.