Lateral control of an autonomous vehicle

The asymptotic stabilization problem for a class of nonlinear under-actuated systems is studied and solved. Its solution, together with the back-stepping and the forwarding control design methods, is exploited in the control of the nonlinear lateral dynamics of a vehicle. Even though the theoretical studies of the lateral control of autonomous vehicles are traditionally applied to lane keeping cases, the results can be applied to broader range of areas, such as lane changing cases. The comparison between the performances of the closed-loop systems with the given controller and a typical human driver is given and demonstrates the speediness and the effectiveness of the feedback controller

LPV-MPC control of autonomous vehicles

In this work, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This method is based on the use of a cascade control where the external loop solves the position control using a novel Linear Parameter Varying - Model Predictive Control (LPV-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a LPV - Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (LPV-LMI-LQR). Both techniques use an LPV representation of the kinematic and dynamic models of the vehicle. The main contribution of the LPV-MPC technique is its ability to calculate solutions very close to those obtained by the non-linear version but reducing significantly the computational cost and allowing the real-time operation. To demonstrate the potential of the LPV-MPC, we propose a comparison between the non-linear MPC formulation (NL-MPC) and the LPV-MPC approach.This work has been partially funded by the Spanish Governmentand FEDER through the projects CICYT DEOCS and SCAV (refs.MINECO DPI2016-76493, DPI2017-88403-R). This work has alsobeen partially funded by AGAUR of Generalitat de Catalunyathrough the Advanced Control Systems (SAC) group grant (2017SGR 482), and by AGAUR and the Spanish Research Agencythrough the Maria de Maetzu Seal of Excellence to IRI (MDM-2016-0656).Peer ReviewedPostprint (author's final draft

TS-MPC for autonomous vehicles Including a TS-MHE-UIO estimator

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, a novel approach is presented to solve the trajectory tracking problem for autonomous vehicles. This approach is based on the use of a cascade control where the external loop solves the position control using a novel Takagi Sugeno-Model Predictive Control (TS-MPC) approach and the internal loop is in charge of the dynamic control of the vehicle using a Takagi Sugeno-Linear Quadratic Regulator technique designed via Linear Matrix Inequalities (TS-LMI-LQR). Both techniques use a TS representation of the kinematic and dynamic models of the vehicle. In addition, a novel Takagi-Sugeno estimator-Moving Horizon Estimator-Unknown Input Observer (TS-MHE-UIO) is presented. This method estimates the dynamic states of the vehicle optimally as well as the force of friction acting on the vehicle that is used to reduce the control efforts. The innovative contribution of the TS-MPC and TS-MHE-UIO techniques is that using the TS model formulation of the vehicle allows us to solve the nonlinear problem as if it were linear, reducing computation times by 10-20 times. To demonstrate the potential of the TS-MPC, we propose a comparison between three methods of solving the kinematic control problem: Using the nonlinear MPC formulation (NL-MPC) with compensated friction force, the TS-MPC approach with compensated friction force, and TS-MPC without compensated friction force.This work was supported by the Spanish Min-istry of Economy and Competitiveness (MINECO) and FEDER through theProjects SCAV (ref. DPI2017-88403-R) and HARCRICS (ref. DPI2014-58104-R). The corresponding author, Eugenio Alcalá, is supported under FI AGAURGrant (ref 2017 FI B00433).Peer ReviewedPostprint (author's final draft

Adaptive Optimal Dynamic Control for Nonholonomic Systems

In this paper two different control methods are combined for controlling a typical nonholonomic device (a bicycle) the dynamic model and parameters of which are only approximately known. Most of such devices suffer from the problem that the time-derivatives of the coordinates of their location and orientation cannot independently be set so an arbitrarily prescribed trajectory cannot precisely be traced by them. For tackling this difficulty Optimal Control is proposed that can find acceptable compromise between the tracking error of the various coordinates. Further problem is that the solution proposed by the optimal controller cannot exactly be implemented in the lack of precise information on the dynamic model of the system. Based on the decoupled nature of the dynamic model of the longitudinal and lateral behavior of the engine special fixed point transformations are proposed to achieve adaptive tracking. These transformations were formerly successfully applied for the control of holonomic systems. It is the first time that the combined method is checked for various trajectories and dynamic model errors via simulation. It yielded promising results

Robust Adaptive Learning-based Path Tracking Control of Autonomous Vehicles under Uncertain Driving Environments

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the λ-norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods

Lateral and Longitudinal Coordinated Control of Intelligent Vehicle Based on High-Precision Dynamics Model under High-Speed Limit Condition

This study focuses on improving the trajectorytracking control for intelligent vehicles in high-speed and largecurvature limit conditions. To this end, a high-precision fivedegree-of-freedom (5-DOF) dynamics model (HPM) that incorporates suspension characteristics is introduced. Furthermore, acoordinated lateral and longitudinal control system is developed.The lateral model predictive control (MPC) involves two crucialstages: initially, a desired trajectory with associated speed datais generated based on path curvature. Subsequently, using thehigh-precision 5-DOF dynamics model, an objective functionis formulated to minimize the difference between the vehicle’scurrent state and the desired state. This process determines theoptimal front wheel steering angle, taking into account vehiclepositional constraints and steering limitations. Additionally, adouble proportional–integral–derivative (PID) controller for longitudinal control adjusts the throttle and brake pressure basedon real-time position and speed data, ensuring integrated controlover both lateral and longitudinal movements. The effectivenessof this approach is confirmed through real vehicle testing andsimulation. Results show that the high-precision 5-DOF dynamicsmodel markedly enhances the accuracy of vehicle response modeling, and the coordinated control system successfully executesprecise trajectory tracking. In extreme scenarios of high-speedand large curvature, the enhanced model substantially improvestrajectory accuracy and driving stability, thus promoting safevehicle operation