Nonlinear model predictive control of autonomous vehicle steering

Rafaila, Razvan C.; Livint, Gheorghe

doi:10.1109/icstcc.2015.7321337

Cited by 27 publications

(17 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gao et al [14] applied nonlinear model predictive control (NMPC) to steering and braking control to develop obstacle avoidance logic. Rafaila et al [15] considered nonlinear tire models and implemented steering control by NMPC.…”

Section: Of 21mentioning

confidence: 99%

“…In this paper, two additional aspects were considered when upgrading the desired yaw rate from the one formulated in Equation (15). First, the upper limit of the yaw rate due to the road friction limit was considered.…”

Section: Upper Chassis Controllermentioning

confidence: 99%

“…Equation (16b) provides the upper limit that is imposed on the steady-state yaw rate γ ss in Equation (15). This was added to account for the road friction effect that Equation (15) does not include; the stability of a vehicle can be degraded when the desired yaw rate is controlled with only Equation ( 15) on a low friction road. The constant 0.85 was added to compensate for ignoring the effect of lateral acceleration in the derivation of the yaw rate limit.…”

Section: Upper Chassis Controllermentioning

confidence: 99%

“…Secondly, there are methods based on dynamic vehicle models that can overcome the stability problems of kinematic methods. Various control schemes such as model predictive control (MPC) and linear quadratic regulator (LQR); and H-infinity, fuzzy, and genetic algorithms have been adopted to achieve reliable path tracking capabilities [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

2021

View full text Add to dashboard Cite

This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and path tracking performance. The vehicle is assumed to employ an IWM independently at each wheel. The AD controller implements longitudinal/lateral path tracking using proportional-integral(PI) control and adaptive model predictive control. The chassis controller is composed of two lateral control units: the active front steering (AFS) control and the torque vectoring (TV) control. Jointly, they find the yaw moment to maintain vehicle stability using sliding mode control; AFS is prioritized over TV to enhance safety margin and energy saving. Then, the command yaw moment is optimally distributed to each wheel by solving a constrained least-squares problem. Validation was performed using simulation in a double lane change scenario. The simulation results show that the integrated AD control system of this paper significantly improves the path tracking capability and vehicle stability in comparison with other control systems.

show abstract

Section: Of 21mentioning

confidence: 99%

Section: Upper Chassis Controllermentioning

confidence: 99%

Section: Upper Chassis Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

2021

View full text Add to dashboard Cite

show abstract

“…But with the development of integrated chips and optimized methods, which have accelerated computing speeds, an increasing number of researchers are considering NMPCs for autonomous trajectory tracking. In [23,24], researchers compared NMPC autonomous steering to manual steering in simulations of lane change and return maneuvers, and verified the advantage of NMPCs. In [25] a method of varying discrete steps is adopted in NMPCs to compatible with path tracking and obstacle avoidance better.…”

Section: Introductionmentioning

confidence: 99%

Research for Nonlinear Model Predictive Controls to Laterally Control Unmanned Vehicle Trajectory Tracking

et al. 2020

View full text Add to dashboard Cite

The autonomous driving is rapid developing recently and model predictive controls (MPCs) have been widely used in unmanned vehicle trajectory tracking. MPCs are advantageous because of their predictive modeling, rolling optimization, and feedback correction. In recent years, most studies on unmanned vehicle trajectory tracking have used only linear model predictive controls to solve MPC algorithm shortcomings in real time. Previous studies have not investigated problems under conditions where speeds are too fast or trajectory curvatures change rapidly, because of the poor accuracy of approximate linearization. A nonlinear model predictive control optimization algorithm based on the collocation method is proposed, which can reduce calculation load. The algorithm aims to reduce trajectory tracking errors while ensuring real-time performance. Monte Carlo simulations of the uncertain systems are carried out to analyze the robustness of the algorithm. Hardware-in-the-loop simulation and actual vehicle experiments were also conducted. Experiment results show that under i7-8700, the calculation time is less than 100 ms, and the mean square error of the lateral deviation is maintained at 10−3 m2, which proves the proposed algorithm can meet the requirement of real time and accuracy in some particular situations. The unmanned vehicle trajectory tracking method provided in this article can meet the needs of real-time control.

show abstract

Nonlinear Model Predictive Control using Lyapunov Functions for Vehicle Lateral Dynamics

Rafaila¹,

Caruntu²,

Livint

2016

IFAC-PapersOnLine

View full text Add to dashboard Cite

Nonlinear model predictive control of autonomous vehicle steering

Cited by 27 publications

References 4 publications

Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

Design of Integrated Autonomous Driving Control System That Incorporates Chassis Controllers for Improving Path Tracking Performance and Vehicle Stability

Research for Nonlinear Model Predictive Controls to Laterally Control Unmanned Vehicle Trajectory Tracking

Nonlinear Model Predictive Control using Lyapunov Functions for Vehicle Lateral Dynamics

Contact Info

Product

Resources

About