Vehicle dynamics

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

Higher-order sliding mode control for lateral dynamics of autonomous vehicles, with experimental validation

Abstract: This paper presents design and experimental validation of a vehicle lateral controller for autonomous vehicle based on a higher-order sliding mode control. We used the super-twisting algorithm to minimize the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle and the output is the lateral displacement error. The particularity of such a strategy is to take advantage of the robustness of the sliding mode controller against nonlinearities and parametric uncertainties in the model, while reducing chattering, the main drawback of first order sliding mode. To validate the control strategy, the closed-loop system simulated on Matlab-Simulink has been compared to the experimental data acquired on our vehicle DYNA, a Peugeot 308, according to several driving scenarios. The validation shows robustness and good performance of the proposed control approach.

Estimation of lateral tire–road forces and sideslip angle for electric vehicles using interacting multiple model filter approach

Abstract: Vehicle control systems require certain vehicle information (e.g., tire–road forces and vehicle sideslip angle) concerning vehicle-dynamic parameters and vehicle–road interaction, which is difficult to measure directly for both technical and economedic reasons. This paper proposes a novel method to estimate lateral tire–road forces and vehicle sideslip angle by utilizing real-time measurements. The estimation method is based on an interacting multiple model (IMM) filter that integrates in-vehicle sensors of in-wheel-motor-driven electric vehicles to adapt multiple vehicle–road system models to variable driving conditions. Based on a four-wheel nonlinear vehicle dynamics model (NVDM) considering extended roll dynamics and load transfer, the vehicle–road system model set of the IMM filter is consists of a linear tire model based NVDM and a nonlinear Dugoff tire model based NVDM. Therefore, the IMM filter can integrate the estimates from two kinds of different vehicle–road system models to improve estimation accuracy. To address system nonlinearities and un-modeled dynamics, the interacting multiple model-unscented Kalman filter (IMM-UKF) and the interacting multiple model-extended Kalman filter (IMM-EKF) are investigated and compared simultaneously. Simulation using Matlab/Simulink-Carsim is carried out to verify the effectiveness of the proposed estimation methods. The results show that the developed estimation methods can accurately estimate lateral tire–road forces and the vehicle sideslip angle.

Design and Comparison of Robust Nonlinear Controllers for the Lateral Dynamics of Intelligent Vehicles

Abstract: This paper focuses on the lateral control of intelligent vehicles; the aim is to minimize the lateral displacement of the autonomous vehicle with respect to a given reference trajectory. The control input is the steering angle, and the output is the lateral error displacement. We present here an analysis of commonality of three lateral nonlinear adaptive controllers. The first controller is a higher order sliding-mode controller (SMC). The second controller is based on the immersion and invariance $(I \& I)$ principle. The design of this controller led us to prove a very strong stability criterion of the closed-loop system for all controller gains chosen to be positive. Thereafter, some interesting characteristics of passivity of the systems were proved following this development. Hence, the third controller is a passivity-based controller (PBC), an adaptive PI controller based on the feedback of a passive output. To validate our control laws, tests have been performed on SCANeR Studio, a driving simulation engine, according to several real driving scenarios. A comparison of these different controllers is made to highlight the advantages and drawbacks of each control approach in lateral tracking of a reference trajectory.

Data-Driven Design of Braking Control Systems

Abstract: The spread of active braking controllers on vehicles with significant mechanical differences and on low-cost products asks for control design approaches which offer easy and fast calibration and re-tuning capabilities. This task is made difficult by the use of model-based control approaches which heavily rely on specific vehicle dynamics descriptions. To address these issues, this brief paper proposes a data-driven approach to active braking control design, grounded on the virtual reference feedback tuning (VRFT) approach complemented with a data-driven nonlinear compensator. The effectiveness of the proposed approach is assessed both on a full-fledged multibody simulator and on a tire-in-the-loop experimental facility.