This study presents a solution for the integrated longitudinal and lateral control problem of urban autonomous vehicles. It is based on a gain‐scheduling linear parameter‐varying (LPV) control approach combined with the use of an Unknown Input Observer (UIO) for estimating the vehicle states and friction force. Two gain‐scheduling LPV controllers are used in cascade configuration that use the kinematic and dynamic vehicle models and the friction and observed states provided by the Unknown Input Observer (UIO). The LPV–UIO is designed in an optimal manner by solving a set of linear matrix inequalities (LMIs). On the other hand, the design of the kinematic and dynamic controllers lead to solve separately two LPV–Linear Quadratic Regulator problems formulated also in LMI form. The UIO allows to improve the control response in disturbance affected scenarios by estimating and compensating the friction force. The proposed scheme has been integrated with a trajectory generation module and tested in a simulated scenario. A comparative study is also presented considering the cases that the friction force estimation is used or not to show its usefulness.

In this study, the authors present an effective online tube-based model predictive control (T-MPC) solution for autonomous driving that aims at improving the computational load while ensuring robust stability and performance in fast and disturbed scenarios. They focus on reformulating the non-linear original problem into a pseudo-linear problem by transforming the non-linear vehicle equations to be expressed in a linear parameter varying (LPV) form. An scheme composed by a nominal controller and a corrective local controller is proposed. First, the local controller is designed as a polytopic LPV-H ${}_{\mathrm{\infty }}$ controller able to reject external disturbances. Moreover, a finite number of accurate reachable sets, also called tube, are computed online using zonotopes taking into account the system dynamics, the local controller and the disturbance-uncertainty bounds considered. Second, the nominal controller is designed as an MPC where the LPV vehicle model is used to speed up the computational time while keeping accurate vehicle representation. They test the presented scheme and compared the local controller performance against the LQR design as state-of-the-art approach. They demonstrate its effectiveness in a disturbed fast driving scenario being able to reject strong exogenous disturbances and fulfilling imposed constraints at a very reduced computational cost.

In this work, 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 non-linear MPC formulation (NL-MPC) with compensated friction force, using the TS-MPC approach with compensated friction force and using TS-MPC without compensated friction force.

This article presents an approach to address the problem of localisation within the autonomous driving framework. In particular, this work takes advantage of the properties of polytopic Linear Parameter Varying (LPV) systems and set-based methodologies applied to Kalman filters to precisely locate both a set of landmarks and the vehicle itself. Using these techniques, we present an alternative approach to localisation algorithms that relies on the use of zonotopes to provide a guaranteed estimation of the states of the vehicle and its surroundings, which does not depend on any assumption of the noise nature other than its limits. LPV theory is used to model the dynamics of the vehicle and implement both an LPV-model predictive controller and a Zonotopic Kalman filter that allow localisation and navigation of the robot. The control and estimation scheme is validated in simulation using the Robotic Operating System (ROS) framework, where its effectiveness is demonstrated.