253 research outputs found

    Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots

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    This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative. Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set. Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance. Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference. Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles

    Control multiagente de vehículos autónomos en presencia de agentes no cooperativos utilizando teoría de juegos

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    ilustraciones, fotografías a colorThis thesis proposes a solution to the problem of autonomous vehicle driving in a road environment, specifically in the presence of agent-driven vehicles with selfish decisions and aggressive maneuvers. The controller tries to solve the optimization problem using a \textit{Model Predictive Control}(MPC). Taking advantage of the previous technique for trajectory prediction, the controller uses this to better predict the neighbors' position and plan its trajectory. In addition, the predictive model can solve the \textit{Optimal Control Problem} by complying with security restrictions, avoiding obstacles, and achieving its primary objective. The \textit{Optimal Control Problem} has non-convex constraints due to its based on mixed-integer variables. By creating non-linear MPC that can deal with the problem of hybrid variables, it is sought to solve the problem of driving vehicles against aggressive and non-cooperative decisions for the network. Furthermore, all agents in the system can be controlled by creating local controllers based on \textit{Game Theory}. We analyzed two methods to find an optimal solution: centralized and decentralized. The most effective and viable controller is chosen after objective research and comparison of all others. Since the centralized MPC provides the best solution for the entire plant, it is used as a benchmark. The first decomposed algorithm is centralized MPC, in which the neighboring subsystems give the information to the central node, calculate the new routes and transmit in each iteration of the MPC. The second approach is based on optimal distributed decentralized MPC. The cars are based on the \textit{Generalized Potential Game theory} in both cases. Each agent solves its problem sequentially and shares its next move with neighbors, looking for a ϵ\epsilon-Nash equilibrium. Both drivers can feasibly calculate their trajectory by relying on additional constraints while avoiding other vehicles. Distributed controllers are evaluated in three different scenarios, using three criteria: the efficiency of the global controller, the time it takes for each controller to find an answer, and the feasibility of the controller with the increase in steps that the controller must predict. The first scenario gives an idea of the controller's behavior against agents with unknown maneuvers; the second shows the controller's behavior against increased constraints and connections with neighbors, and the third tests the controller by reducing its environmental variables. (Texto tomado de la fuente)Esta tesis propone una solución al problema de la conducción autónoma de vehículos en un entorno vial, concretamente en presencia de vehículos conducidos por agentes con decisiones egoístas y maniobras agresivas. El controlador trata de resolver el problema de optimización basado en un Control Predictivo de Modelo (MPC). Aprovechando la técnica anterior de predicción de trayectoria, el controlador la utiliza para predecir mejor la posición de los vecinos y planificar su trayectoria. Además, el modelo predictivo puede resolver el problema de control óptimo al cumplir con las restricciones de seguridad, evitar obstáculos y lograr su objetivo principal. El problema de control óptimo tiene restricciones no convexas debido a las variables enteras mixtas en las que se basa. Mediante la creación de MPC no lineales que puedan lidiar con el problema de las variables híbridas, se busca resolver el problema de conducción de vehículos frente a decisiones agresivas y no cooperativas para la red. Además, todos los agentes del sistema pueden controlarse mediante la creación de controladores locales basados en \textit{Teoría de Juegos}. Analizamos dos métodos para encontrar una solución óptima: centralizado y descentralizado. El controlador más eficaz y viable se elige después de una investigación objetiva y la comparación de todos los demás. Dado que el MPC proporciona la mejor solución para toda la planta, se utiliza como punto de referencia. El primer algoritmo descompuesto es MPC centralizado, en el que los subsistemas vecinos entregan la información al nodo central, calculan las nuevas rutas y transmiten en cada iteración del controlador por MPC. El segundo enfoque se basa en MPC descentralizado distribuido óptimo. Los coches se basan en la teoría del Juego de Potencial Generalizado en ambos casos. Cada agente resuelve su problema secuencialmente y comparte su próximo movimiento con los vecinos, buscando un equilibrio ϵ\epsilon-Nash. Ambos conductores pueden calcular su trayectoria de manera factible confiando en restricciones adicionales mientras evitan otros vehículos. Los controladores distribuidos se evalúan en tres escenarios diferentes, utilizando tres criterios: la eficiencia del controlador global, el tiempo que tarda cada controlador en encontrar una respuesta y la viabilidad del controlador con el aumento de pasos que el controlador debe predecir. El primer escenario da una idea del comportamiento del controlador frente a agentes con maniobras desconocidas; el segundo muestra el comportamiento del controlador frente a mayores restricciones y conexiones con vecinos, y el tercero prueba el controlador reduciendo sus variables ambientales.MaestríaMagíster en Ingeniería - Automatización IndustrialControlRobotic

    Analysis and design of model predictive control frameworks for dynamic operation -- An overview

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    This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems. Dynamic operation is often an integral part of the control objective, ranging from tracking of reference signals to the general economic operation of a plant under online changing time-varying operating conditions. We focus on the particular challenges that arise when dealing with such more general control goals and present methods that have emerged in the literature to address these issues. The goal of this article is to present an overview of the state-of-the-art techniques, providing a diverse toolkit to apply and further develop MPC formulations that can handle the challenges intrinsic to dynamic operation. We also critically assess the applicability of the different research directions, discussing limitations and opportunities for further researc

    Multiple Waypoint Navigation in Unknown Indoor Environments

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    Indoor motion planning focuses on solving the problem of navigating an agent through a cluttered environment. To date, quite a lot of work has been done in this field, but these methods often fail to find the optimal balance between computationally inexpensive online path planning, and optimality of the path. Along with this, these works often prove optimality for single-start single-goal worlds. To address these challenges, we present a multiple waypoint path planner and controller stack for navigation in unknown indoor environments where waypoints include the goal along with the intermediary points that the robot must traverse before reaching the goal. Our approach makes use of a global planner (to find the next best waypoint at any instant), a local planner (to plan the path to a specific waypoint), and an adaptive Model Predictive Control strategy (for robust system control and faster maneuvers). We evaluate our algorithm on a set of randomly generated obstacle maps, intermediate waypoints, and start-goal pairs, with results indicating a significant reduction in computational costs, with high accuracies and robust control.Comment: Accepted at ICCR 202

    Advanced Sensing and Control for Connected and Automated Vehicles

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    Connected and automated vehicles (CAVs) are a transformative technology that is expected to change and improve the safety and efficiency of mobility. As the main functional components of CAVs, advanced sensing technologies and control algorithms, which gather environmental information, process data, and control vehicle motion, are of great importance. The development of novel sensing technologies for CAVs has become a hotspot in recent years. Thanks to improved sensing technologies, CAVs are able to interpret sensory information to further detect obstacles, localize their positions, navigate themselves, and interact with other surrounding vehicles in the dynamic environment. Furthermore, leveraging computer vision and other sensing methods, in-cabin humans’ body activities, facial emotions, and even mental states can also be recognized. Therefore, the aim of this Special Issue has been to gather contributions that illustrate the interest in the sensing and control of CAVs

    Design, testing and validation of model predictive control for an unmanned ground vehicle

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    The rapid increase in designing, manufacturing, and using autonomous robots has attracted numerous researchers and industries in recent decades. The logical motivation behind this interest is the wide range of applications. For instance, perimeter surveillance, search and rescue missions, agriculture, and construction. In this thesis, motion planning and control based on model predictive control (MPC) for unmanned ground vehicles (UGVs) is tackled. In addition, different variants of MPC are designed, analysed, and implemented for such non-holonomic systems. It is imperative to focus on the ability of MPC to handle constraints as one of the motivations. Furthermore, the proliferation of computer processing enables these systems to work in a real-time scenario. The controller's responsibility is to guarantee an accurate trajectory tracking process to deal with other specifications usually not considered or solved by the planner. However, the separation between planner and controller is not necessarily defined uniquely, even though it can be a hybrid process, as seen in part of this thesis. Firstly, a robust MPC is designed and implemented for a small-scale autonomous bulldozer in the presence of uncertainties, which uses an optimal control action and a feed-forward controller to suppress these uncertainties. More precisely, a linearised variant of MPC is deployed to solve the trajectory tracking problem of the vehicle. Afterwards, a nonlinear MPC is designed and implemented to solve the path-following problem of the UGV for masonry in a construction context, where longitudinal velocity and yaw rate are employed as control inputs to the platform. For both the control techniques, several experiments are performed to validate the robustness and accuracy of the proposed scheme. Those experiments are performed under realistic localisation accuracy, provided by a typical localiser. Most conspicuously, a novel proximal planning and control strategy is implemented in the presence of skid-slip and dynamic and static collision avoidance for the posture control and tracking control problems. The ability to operate in moving objects is critical for UGVs to function well. The approach offers specific planning capabilities, able to deal at high frequency with context characteristics, which the higher-level planner may not well solve. Those context characteristics are related to dynamic objects and other terrain details detected by the platform's onboard perception capabilities. In the control context, proximal and interior-point optimisation methods are used for MPC. Relevant attention is given to the processing time required by the MPC process to obtain the control actions at each actual control time. This concern is due to the need to optimise each control action, which must be calculated and applied in real-time. Because the length of a prediction horizon is critical in practical applications, it is worth looking into in further detail. In another study, the accuracies of robust and nonlinear model predictive controllers are compared. Finally, a hybrid controller is proposed and implemented. This approach exploits the availability of a simplified cost-to-go function (which is provided by a higher-level planner); thus, the hybrid approach fuses, in real-time, the nominal CTG function (nominal terrain map) with the rest of the critical constraints, which the planner usually ignores. The conducted research fills necessary gaps in the application areas of MPC and UGVs. Both theoretical and practical contributions have been made in this thesis. Moreover, extensive simulations and experiments are performed to test and verify the working of MPC with a reasonable processing capability of the onboard process

    System identification and nonlinear model predictive control with collision avoidance applied in Hexacopters UAVs

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    Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller

    Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

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    According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies
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