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

Path Following Control of Automated Vehicle Considering Uncertainties and Disturbances with Parametric Varying

Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. Most PFCs did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, and norm-bounded external disturbances, the system states and control matrices are modified

A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.Peer ReviewedPostprint (author's final draft

Producció científica de l'ETSEIB a Futur. Articles de revista i publicacions a congressos dels investigadors de l'ETSEIB durant l'any 2019

Postprint (author's final draft

Performance of Induction Machines

Induction machines are one of the most important technical applications for both the industrial world and private use. Since their invention (achievements of Galileo Ferraris, Nikola Tesla, and Michal Doliwo-Dobrowolski), they have been widely used in different electrical drives and as generators, thanks to their features such as reliability, durability, low price, high efficiency, and resistance to failure. The methods for designing and using induction machines are similar to the methods used in other electric machines but have their own specificity. Many issues discussed here are based on the fundamental achievements of authors such as Nasar, Boldea, Yamamura, Tegopoulos, and Kriezis, who laid the foundations for the development of induction machines, which are still relevant today. The control algorithms are based on the achievements of Blaschke (field vector-oriented control) and Depenbrock or Takahashi (direct torque control), who created standards for the control of induction machines. Today’s induction machines must meet very stringent requirements of reliability, high efficiency, and performance. Thanks to the application of highly efficient numerical algorithms, it is possible to design induction machines faster and at a lower cost. At the same time, progress in materials science and technology enables the development of new machine topologies. The main objective of this book is to contribute to the development of induction machines in all areas of their applications

Space transportation system and associated payloads: Glossary, acronyms, and abbreviations

A collection of some of the acronyms and abbreviations now in everyday use in the shuttle world is presented. It is a combination of lists that were prepared at Marshall Space Flight Center and Kennedy and Johnson Space Centers, places where intensive shuttle activities are being carried out. This list is intended as a guide or reference and should not be considered to have the status and sanction of a dictionary

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