Modelling, planning and nonlinear control techniques for autonomous vehicles

Autonomous driving has been an important topic of research in recent years. Autonomous driving is a very challenging research topic that requires from different disciplines such as electronics, computer vision, geolocalization, control or planning. This paper tackles the problem of the vehicle control planning and performs a comparison of two nonlinear model-based control strategies for autonomous cars. These control techniques rely on the so called bicycle model and follow a reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a nonlinear control law that is designed by means of the Lyapunov direct approach. The second strategy is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high-order sliding mode control. To test and compare the proposed control strategies a quintic path planner has been implemented in order to provide the desired temporal variables to the control block. Different scenarios have been used to prove such control techniques. First both methods were proved in simulation (Matlab/Simulink and Unity1 ) and finally they were used in scenarios with a real car

Desarrollo e implementación de un cuadricóptero

Elaboración de un prototipo de cuadricóptero con un sistema de control de estabilidad automático y comunicado vía Wi-Fi con el punto de control. Se utiliza como plataforma de control la tarjeta Raspberry Pi. La aplicación de control se ha desarrollado sobre la plataforma COSME la cual trabaja sobre el sistema operativo modificado con características de tiempo real

LPV-MP planning for autonomous racing vehicles considering obstacles

In this paper, we present an effective online planning solution for autonomous vehicles that aims at improving the computational load while preserving high levels of performance in racing scenarios. The method follows the structure of the model predictive (MP) optimal strategy where the main objective is to maximize the velocity while smoothing the dynamic behavior and fulfilling varying constraints. We focus on reformulating the non-linear original problem into a pseudo-linear problem by convexifying the objective function and reformulating the non-linear vehicle equations to be expressed in a Linear Parameter Varying (LPV) form. In addition, the ability of avoiding obstacles is introduced in a simple way and with reduced computational cost. We test and compare the performance of the proposed strategy against its non-linear approach through simulations. We focus on testing the performance of the trajectory planning approach in a racing scenario. First, the case of free obstacles track and afterwards a scenario including static obstacles. Simulation results show the effectiveness of the proposed strategy by reducing the algorithm elapsed time while finding appropriate trajectories under several input/state constraints.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

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

Gain-scheduling LPV control for autonomous vehicles including friction force estimation and compensation mechanism

© 2018 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.This study presents a solution for the integrated longitudinal and lateral control problem of urban autonomousvehicles. It is based on a gain-scheduling linear parameter-varying (LPV) control approach combined with the use of anUnknown Input Observer (UIO) for estimating the vehicle states and friction force. Two gain-scheduling LPV controllers are usedin cascade configuration that use the kinematic and dynamic vehicle models and the friction and observed states provided bythe 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–LinearQuadratic Regulator problems formulated also in LMI form. The UIO allows to improve the control response in disturbanceaffected scenarios by estimating and compensating the friction force. The proposed scheme has been integrated with atrajectory generation module and tested in a simulated scenario. A comparative study is also presented considering the casesthat the friction force estimation is used or not to show its usefulnessPeer ReviewedPostprint (author's final draft

Autonomous vehicle control using a kinematic Lyapunov-based technique with LQR-LMI tuning

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/This paper presents the control of an autonomous vehicle using a Lyapunov-based technique with a LQR-LMI tuning. Using the kinematic model of the vehicle, a non-linear control strategy based on Lyapunov theory is proposed for solving the control problem of autonomous guidance. To optimally adjust the parameters of the Lyapunov controller, the closed loop system is reformulated in a linear parameter varying (LPV) form. Then, an optimization algorithm that solves the LQR-LMI problem is used to determine the controller parameters. Furthermore, the tuning process is complemented by adding a pole placement constraint that guarantees that the maximum achievable performance of the kinematic loop could be achieved by the dynamic loop. The obtained controller jointly with a trajectory generation module are in charge of the autonomous vehicle guidance. Finally, the paper illustrates the performance of the autonomous guidance system in a virtual reality environment (SYNTHIA) and in a real scenario achieving the proposed goal: to move autonomously from a starting point to a final point in a comfortable way.Peer ReviewedPostprint (author's final draft

Advances in planning and control for autonomous vehicles

Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Politécnica de Cataluña.--2020-03-24Esta tesis presenta algunos avances en los campos de la estimación de estados, el control automático y la planificación de trayectorias aplicados a vehículos autónomos.Tales contribuciones comparten un particular aspecto a lo largo de la tesis, todas ellas son técnicas basadas en modelos. La teoría de Variación Lineal de Parámetros (VLP) y Takagi-Sugeno (TS) se utilizan para generar modelos orientados al control mediante el uso de enfoques de inclusión no lineal y de no linealidad sectorial. Se proponen diferentes modelos de vehículos según la aplicación y la técnica de estimación-control-planificación . Primero, se presentan los modelos de vehículos en la formulación no lineal. Más tarde, dichos modelos se reformulan como VLP .En el área de control y estimación, la tesis muestra diferentes enfoques para diferentes aplicaciones: modos de conducción normal y de carreras. Primero, para la conducción normal, se desarrollan técnicas de retroalimentación de estado VLP de programación de ganancia (PG). En primera instancia, un diseño de Regulador Cuadrático Lineal (RCL) VLP a través de la formulación de Desigualdad de Matriz Lineal (DML) se establece para el control del vehiculo a bajas velocidades. Más tarde, se presenta un esquema en cascada que incluye capas de control cinemático y dinámico para mejorar el último diseño . Aquí, ambos diseños de controlador se realizan utilizando el diseño VLP-LQR a través de la formulación LMI y un Observador de Entrada Desconocida (OED) VLP está preestablecido para estimar los estados del vehículo , así como la fuerza de fricción que actúa sobre el vehículo . Segundo , para la conducción en carreras, se exploran técnicas óptimas que conducen a introducir la técnica de Control de Modelo Predictivo (CMP) como base para los comportamientos de carrera. En primera instancia, el esquema en cascada se mantiene donde la capa de control externa está gobernada por un controlador TS-CMP . En este punto, se presenta una técnica de estimación avanzada, el TS-Moving Horizon Estimator-UIO (TS-MHE-OED) . Se demuestra que al usar la formulacion TS, tanto el controlador como el estimador óptimos reducen en gran medida el esfuerzo computacional en comparación con su formualción no lineal. Luego, la idea de diseñar un controlador único se explora a través de la técnicaVLP-CMP . En este caso, se muestra el potencial de esta estrategia para poder ejecutarse en tiempo real en pequeñas plataformas integradas para controlar el vehículo en situaciones de carrera. Finalmente, se considera un CMP robusto en línea que tiene como objetivo mejorar la carga computacional utilizando la teoría de zonótopos mientras preserva altos niveles de robustez y rendimiento en escenarios de carreras.En el área de planificación, la tesis se centra en los enfoques de planificación de trayectorias desde el punto de vista óptimo . Primero, el CMP no lineal se formula como un planificador (NL-MPP) en el dominio espacial donde el objetivo es la minimización del tiempo de vuelta total. Más tarde, se explora una solución innovadora en tiempo real que conduce a un VLP-MPP. El método sigue la estructura de la estrategia óptima de modelo predictivo donde el objetivo principal es maximizar la velocidad mientras se cumplen las limitaciones dinámicas del vehiculo. En particular, el objetivo es reformular el problema original no lineal en un problema pseudo-lineal convexificando la función objetivo y haciendo uso de la formulación del vehículo VLP

Advances in planning and control for autonomous vehicles

Aplicat embargament des de la data de defensa fins al 30 de juny de 2021This thesis presents some advances to the state of the art of state estimation, automatic control and trajectory planning fields applied to autonomous vehicles. Such contributions have a common aspect throughout the thesis, all of them are model-based techniques. The Linear Parameter Varying (LPV) Takagi-Sugeno (TS) theory are used to generate control-oriented models by using the non-linear embedding approach. Several vehicle models are proposed depending on lhe application and estimation -control -planning technique. First, non-linear vehicle formulations are presented. Later, the same models are represented in the LPV form. In the area of control and estimation, the thesis shows different approaches for diferent applications: normal and racing driving modes. First, for normal driving, gain scheduling (GS) LPV state feedback techniques are developed. In the first instance, an LPV-Linear Quadratic Regulator (LQA) design via Linear Matrix lnequality (LMI) formulation is stated for control at low velocities. Later, a cascade scheme including kinematic and dynamic control layers is presented to improve the last design. Here, both controller designs are set up using the LPV-LQR design via LMI formulation and a LPV-Unknown Input Observer (UIO) is presented for estimating vehicle states and exogenous friction force. Second, for racing driving, optimal techniques are explored leading to introduce the Model Predictive Control (MPC) technique as a basis for racing behaviours. In the first instance, the cascade scheme is maintained where the outer control layer is governed by a TS -MPC controller. At this point, an advanced estimation technique is presented, the TS-Moving Horizon Estimator-UIO (TS-MHE-UIO). lt is shown that by using the TS formulation both optimal-based controller and estimator reduce greatly the computational effort in comparison to their non-linear formulation. Then, the idea of designing a unique controller is explored through the LPV-MPC technique. In this case, it is shown the potential of this strategy being able to be executed in real time in small embedded platforms for controlling the vehicle in racing situations. Finally, an online robust MPC is considered that aims at improving the computational load using zonotope theory while preservin high levels of robustness and performance in racing scenarios. In the area or planning, the thesis focuses on trajectory planning approaches from the optimal point of view. First, the non-linear MPC is formulated as a planner (NL-MPP) in space domain where the goal is the minimization of the total lap time.Later, an innovative real time solution is explored leading to a LPV-MPP. The method follows the structure of the model predictive optimal strategy where the main objective is to maximize the velocity while fulfilling varying constraints. In particular, the aim is on reformulating the non-linear original problem into a pseudo-linear problem by convexifying the objective function and making use of the LPV vehicle formulation.Esta tesis presenta algunos avances en los campos de la estimación de estados, el control automático y la planificación de trayectorias aplicados a vehículos autónomos. Tales contribuciones comparten un particular aspecto a lo largo de la tesis, todas ellas son técnicas basadas en modelos. La teoría de Variación Lineal de Parámetros (VLP) y Takagi-Sugeno (TS) se utilizan para generar modelos orientados al control mediante el uso de enfoques de inclusión no lineal y de no linealidad sectorial. Se proponen diferentes modelos de vehículos según la aplicación y la técnica de estimación-control-planificación . Primero, se presentan los modelos de vehículos en la formulación no lineal. Más tarde, dichos modelos se reformulan como VLP . En el área de control y estimación, la tesis muestra diferentes enfoques para diferentes aplicaciones: modos de conducción normal y de carreras. Primero, para la conducción normal, se desarrollan técnicas de retroalimentación de estado VLP de programación de ganancia (PG). En primera instancia, un diseño de Regulador Cuadrático Lineal (RCL) VLP a través de la formulación de Desigualdad de Matriz Lineal (DML) se establece para el control del vehiculo a bajas velocidades. Más tarde, se presenta un esquema en cascada que incluye capas de control cinemático y dinámico para mejorar el último diseño . Aquí, ambos diseños de controlador se realizan utilizando el diseño VLP-LQR a través de la formulación LMI y un Observador de Entrada Desconocida (OED) VLP está preestablecido para estimar los estados del vehículo , así como la fuerza de fricción que actúa sobre el vehículo . Segundo , para la conducción en carreras, se exploran técnicas óptimas que conducen a introducir la técnica de Control de Modelo Predictivo (CMP) como base para los comportamientos de carrera. En primera instancia, el esquema en cascada se mantiene donde la capa de control externa está gobernada por un controlador TS-CMP . En este punto, se presenta una técnica de estimación avanzada, el TS-Moving Horizon Estimator-UIO (TS-MHE-OED) . Se demuestra que al usar la formulacion TS, tanto el controlador como el estimador óptimos reducen en gran medida el esfuerzo computacional en comparación con su formualción no lineal. Luego, la idea de diseñar un controlador único se explora a través de la técnica VLP-CMP . En este caso, se muestra el potencial de esta estrategia para poder ejecutarse en tiempo real en pequeñas plataformas integradas para controlar el vehículo en situaciones de carrera. Finalmente, se considera un CMP robusto en línea que tiene como objetivo mejorar la carga computacional utilizando la teoría de zonótopos mientras preserva altos niveles de robustez y rendimiento en escenarios de carreras. En el área de planificación, la tesis se centra en los enfoques de planificación de trayectorias desde el punto de vista óptimo . Primero, el CMP no lineal se formula como un planificador (NL-MPP) en el dominio espacial donde el objetivo es la minimización del tiempo de vuelta total. Más tarde, se explora una solución innovadora en tiempo real que conduce a un VLP-MPP. El método sigue la estructura de la estrategia óptima de modelo predictivo donde el objetivo principal es maximizar la velocidad mientras se cumplen las limitaciones dinámicas del vehiculo. En particular, el objetivo es reformular el problema original no lineal en un problema pseudo-lineal convexificando la función objetivo y haciendo uso de la formulación del vehículo VLP.Postprint (published version

Modelling, planning and nonlinear control techniques for autonomous vehicles

Autonomous driving has been an important topic of research in recent years. Autonomous driving is a very challenging research topic that requires from different disciplines such as electronics, computer vision, geolocalization, control or planning. This paper tackles the problem of the vehicle control planning and performs a comparison of two nonlinear model-based control strategies for autonomous cars. These control techniques rely on the so called bicycle model and follow a reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a nonlinear control law that is designed by means of the Lyapunov direct approach. The second strategy is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high-order sliding mode control. To test and compare the proposed control strategies a quintic path planner has been implemented in order to provide the desired temporal variables to the control block. Different scenarios have been used to prove such control techniques. First both methods were proved in simulation (Matlab/Simulink and Unity1 ) and finally they were used in scenarios with a real car