Jahan Asgari

Predictive Active Steering Control for Autonomous Vehicle Systems

In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads

MPC-based approach to active steering for autonomous vehicle systems

In this paper a novel approach to autonomous steering systems is presented. A model predictive control (MPC) scheme is designed in order to stabilize a vehicle along a desired path while fulfilling its physical constraints. Simulation results show the benefits of the systematic control methodology used. In particular we show how very effective steering manoeuvres are obtained as a result of the MPC feedback policy. Moreover, we highlight the trade off between the vehicle speed and the required preview on the desired path in order to stabilize the vehicle. The paper concludes with highlights on future research and on the necessary steps for experimental validation of the approach.

MPC-Based Yaw and Lateral Stabilization Via Active Front Steering and Braking

In this paper, we propose a path following Model Predictive Control-based (MPC) scheme utilising steering and braking. The control objective is to track a desired path for obstacle avoidance manoeuvre, by a combined use of braking and steering. The proposed control scheme relies on the Nonlinear MPC (NMPC) formulation we used in [F. Borrelli, et al., MPC-based approach to active steering for autonomous vehicle systems, Int. J. Veh. Autonomous Syst. 3(2/3/4) (2005), pp. 265–291.] and [P. Falcone, et al., Predictive active steering control for autonomous vehicle systems, IEEE Trans. Control Syst. Technol. 15(3) (2007), pp. 566–580.]. In this work, the NMPC formulation will be used in order to derive two different approaches. The first relies on a full tenth-order vehicle model and has high computational burden. The second approach is based on a simplified bicycle model and has a lower computational complexity compared to the first. The effectiveness of the proposed approaches is demonstrated through simulations and experiments.

Predictive control approach to autonomous vehicle steering

A model predictive control (MPC) approach to active steering is presented for autonomous vehicle systems. The controller is designed to stabilize a vehicle along a desired path while rejecting wind gusts and fulfilling its physical constraints. Simulation results of a side wind rejection scenario and a double lane change maneuver on slippery surfaces show the benefits of the systematic control methodology used. A trade-off between the vehicle speed and the required preview on the desired path for vehicle stabilization is highlighted

A linear time varying model predictive control approach to the integrated vehicle dynamics control problem in autonomous systems

A Model Predictive Control (MPC) approach for controlling active front steering, active braking and active differentials in an autonomous vehicle is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle, brakes and traction at the four wheels independently, while fulfilling various physical and design constraints. At each time step a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the system inputs in order to best follow the desired trajectory on slippery roads at a given entry speed. We start from the results presented in [1], [2] and formulate the MPC problem based on successive on-line linearization of the nonlinear vehicle model (LTV MPC). Simulative results are presented, interpreted and compared against LTV MPC schemes which make use only of steering and/or braking.

A 3D Brush-type Dynamic Tire Friction Model

The use of advanced dynamic friction models can improve the brush-type tire friction models. This paper presents a 3D dynamic brush model based on the LuGre friction model. The model describes the dynamics of longitudinal and lateral tire friction forces, as well as the self aligning torque dynamics. It has been originally derived in a distributed-parameter form, and then transformed to a simpler lumped-parameter form with only three internal states. Both uniform and non-uniform normal pressure distributions are considered. The model has analytical solution for steady-state conditions. The steady-state behavior is validated with respect to “magic” formula static model, which served as an “ideal” benchmark. The lumped model dynamic behavior is validated by comparing its time-responses with original distributed model responses. The model parameterization with respect to normal force and other tire/road parameters is considered as well.

A model predictive control approach for combined braking and steering in autonomous vehicles

In this paper we present a Model Predictive Control (MPC) approach for combined braking and steering systems in autonomous vehicles. We start from the result presented in F. Borrelli et al. (2005) and P. Falcone et al. (2006), where a Model Predictive Controller (MPC) for autonomous steering systems has been presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle and the brakes at the four wheels independently, while fulfilling various physical and design constraints.

A hierarchical Model Predictive Control framework for autonomous ground vehicles

A hierarchical framework based on Model Predictive Control (MPC) for autonomous vehicles is presented. We formulate a predictive control problem in order to best follow a given path by controlling the front steering angle while fulfilling various physical and design constraints. We start from the low-level active steering-controller presented in [3], [9] and integrate it with a high level trajectory planner. At both levels MPC design is used. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model. At the low- level a MPC controller computes the vehicle inputs in order to best follow the desired trajectory based on detailed nonlinear vehicle model. This article presents the approach, the method for implementing it, and successful preliminary simulative results on slippery roads at high entry speed.

Modeling and Analysis of Automatic Transmission Engagement Dynamics-Linear Case

A control-oriented model of a typical four-speed automatic transmission is developed by using the bond graph modeling method. The planetary gear set model utilizes the Karnopp friction model for hydraulic and one-way clutches, in order to provide a favorable computing efficiency. The full gear set model is reduced for various phases of the park/ reverse and park/drive engagements. The reduced gear set models and linearized torque converter model are used as a basis for an algebraic analysis of the engagement dynamics. The analysis is originally conducted for the basic case of fully applied brake, and it is then extended by an analysis of the influence of wheel dynamics in the brake-off case. The analysis results are verified by computer simulations and experiments.

Modeling of Wet Clutch Engagement Including a Thorough Experimental Validation

A detailed experimental validation has been carried out to point to limitations of static wet-clutch friction model for typical clutch engagement transients. The model accuracy can be increased by incorporating the fluid film dynamics, as done in the lumped-parameter dynamic clutch model developed at the University of Purdue. That model is extended herein in order to increase its accuracy especially in the case of grooved clutches. The extensions include a description of clutch actuator dynamics and introduction of an empirical scaling factor for the fluid film thickness state equation. More rigorous treatment of fluid dynamics for the grooved clutch is also presented.