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

EFFECT OF SENSOR ERRORS ON AUTONOMOUS STEERING CONTROL AND APPLICATION OF SENSOR FUSION FOR ROBUST NAVIGATION

Autonomous steering control is one the most important features in autonomous vehicle navigation. The nature and tuning of the controller decides how well the vehicle follows a defined trajectory. A poorly tuned controller can cause the vehicle to oversteer or understeer at turns leading to deviation from a defined path. However, controller performance also depends on the state–feedback system. If the states used for controller input are noisy or has bias / systematic error, the navigation performance of the vehicle is affected irrespective of the control law and controller tuning. In this report, autonomous steering controller analysis is done for different kinds of sensor errors and the application of sensor fusion using Kalman Filters is discussed. Model-in-the-loop (MIL) simulation provides an efficient way for developing and performing controller analysis and implementing various fusion algorithms. Matlab/Simulink was used for this Model Based Development. Firstly, through experimentation the path tracking performance of the controller was analyzed followed by data collection for sensor, actuator and vehicle modelling. Then, the plant, actuator and controllers were modelled followed by the comparison of the results for ideal and non-ideal sensors. After analyzing the effects of sensor error on controller and vehicle performance, a solution was proposed using 1D-Kalman Filter (KF) based sensor fusion technique. It is seen that the waypoint tracking under 1D condition is improved to centimeter level and the steering response is also smoothened due to less noisy vehicle heading estimation

A trajectory-tracking controller for improving the safety and stability of four-wheel steering autonomous vehicles

To achieve anti-crosswind, anti-sideslip, and anti-rollover in trajectory-tracking for Four-Wheel Steering (4WS) autonomous vehicles, a trajectory-tracking controller based on a four-channel Active Disturbance Rejection Control (ADRC) was used to track the desired lateral displacement, longitudinal displacement, yaw angle, and roll angle, and minimize the tracking errors between the actual output values and the desired values through static decoupling steering and braking systems. In addition, the anti-crosswind, anti-sideslip, and anti-rollover simulations were implemented with CarSim®. Finally, the simulation results showed that the 4WS autonomous vehicle with the controller still has good anti-crosswind, anti-sideslip, and anti-rollover performance in path tracking, even under a small turning radius or lowadhesion curved roads. First published online 12 March 202