Comparison of Adaptive Control Architectures for Flutter Suppression

A study is conducted to derive and implement a state feedback model reference adaptive control (MRAC) solutions for a 2-D aeroelastic nonlinear system and in evaluating the robustness of different control strategies to damage leading to the deterioration of the structural stiffness characteristics. The standard MRAC, a modified MRAC and the adaptive controller are the three model reference adaptive control solutions analyzed. The standard direct MRAC solution serves as the threshold to assess whether or not the more complex algorithms are an effective improvement to it

ℒ1 adaptive control of quadrotor UAVs in case of inversion of the torque direction

This paper presents a method for fault tolerant control of quadrotor UAVs in case of inversion of the torque direction, a situation that might occur due to structural, hardware or software issues. The proposed design is based on multiple-model ℒ1 adaptive control. The controller is composed of a nominal reference model and a set of degraded reference models. The nominal model is that with desired dynamics that are optimal regarding some specific criteria. In a degraded model, the performance criteria are reduced. It is designed to ensure system robustness in the presence of critical failures. The controller is tested in simulations and it is shown that the multiple model ℒ1 adaptive controller stabilizes the system in case of inversion of the control input, while the ℒ1 adaptive controller with a single nominal model fails

Design of Flight Control Laws for a Novel Stratospheric Dual-Aircraft Platform

Dual-aircraft platform (DAP) is a novel concept that features two glider-like unmanned aerial systems (UAS) tethered via a thin adjustable cable allowing them to sail back-and-forth, without propulsion, using vertical wind shear. DAP offers the potential of a low-cost atmospheric satellite. This thesis presents the results of an initiative to demonstrate this novel flight concept through modeling, simulation, and flight testing at Embry-Riddle Aeronautical University (ERAU). A realistic simulation environment, described herein, was developed to support the development and testing of flight control systems. This environment includes nonlinear aerodynamic models for the aircraft, a multi-element cable dynamics model, propeller-motor thrust model, control surface actuator models, and permits time-varying wind profiles. This simulator offers both pilot-in-the-loop control and autonomous sailing flight control, and X-Plane interface to provide visualization cues. An intensive flight test program, described herein, was conducted to support the validation of the DAP concept. MAXA Pro 4m gliders were assembled, instrumented, and flight tested in an effort to physically demonstrate the sailing mode of flight. The flight test program described here focuses on the capability to sail with one aircraft (i.e., fly without propulsion) while towing (i.e., pulling) a moving truck as an intermediate step towards the more complex scenario of sailing with two connected aircraft. Two vital elements of the flight software are implemented and analyzed herein. The accuracy of wind estimation techniques is evaluated using flight testing. The robustness of an L1 adaptive controller is evaluated within the flight simulation environment by comparing its performance with a conventional controller

Learning-based Nonlinear MPC for Quadrotor Control

openThis work aims at investigate the application of different learning based techniques for the enhancement of the Nonlinear Model Predictive Control (NMPC) framework, in the context of trajectory control for a quadrotor unmanned aerial vehicle (UAV). In particular, a gaussian process regression technique and a neural network approach are both taken into account in order to improve the knowledge of the model that constitutes the basis of the effectiveness of the NMPC.This work aims at investigate the application of different learning based techniques for the enhancement of the Nonlinear Model Predictive Control (NMPC) framework, in the context of trajectory control for a quadrotor unmanned aerial vehicle (UAV). In particular, a gaussian process regression technique and a neural network approach are both taken into account in order to improve the knowledge of the model that constitutes the basis of the effectiveness of the NMPC

Event sampled optimal adaptive regulation of linear and a class of nonlinear systems

In networked control systems (NCS), wherein a communication network is used to close the feedback loop, the transmission of feedback signals and execution of the controller is currently carried out at periodic sampling instants. Thus, this scheme requires a significant computational power and network bandwidth. In contrast, the event-based aperiodic sampling and control, which is introduced recently, appears to relieve the computational burden and high network resource utilization. Therefore, in this dissertation, a suite of novel event sampled adaptive regulation schemes in both discrete and continuous time domain for uncertain linear and nonlinear systems are designed. Event sampled Q-learning and adaptive/neuro dynamic programming (ADP) schemes without value and policy iterations are utilized for the linear and nonlinear systems, respectively, in both the time domains. Neural networks (NN) are employed as approximators for nonlinear systems and, hence, the universal approximation property of NN in the event-sampled framework is introduced. The tuning of the parameters and the NN weights are carried out in an aperiodic manner at the event sampled instants leading to a further saving in computation when compared to traditional NN based control. The adaptive regulator when applied on a linear NCS with time-varying network delays and packet losses shows a 30% and 56% reduction in computation and network bandwidth usage, respectively. In case of nonlinear NCS with event sampled ADP based regulator, a reduction of 27% and 66% is observed when compared to periodic sampled schemes. The sampling and transmission instants are determined through adaptive event sampling conditions derived using Lyapunov technique by viewing the closed-loop event sampled linear and nonlinear systems as switched and/or impulsive dynamical systems. --Abstract, page iii

Data-driven methods for statistical verification of uncertain nonlinear systems

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 277-290).Due to the increasing complexity of autonomous, adaptive, and nonlinear systems, engineers commonly rely upon statistical techniques to verify that the closed-loop system satisfies specified performance requirements at all possible operating conditions. However, these techniques require a large number of simulations or experiments to exhaustively search the set of possible parametric uncertainties for conditions that lead to failure. This work focuses on resource-constrained applications, such as preliminary control system design or experimental testing, which cannot rely upon exhaustive search to analyze the robustness of the closed-loop system to those requirements. This thesis develops novel statistical verification frameworks that combine data-driven statistical learning techniques and control system verification. First, two frameworks are introduced for verification of deterministic systems with binary and non-binary evaluations of each trajectory's robustness. These frameworks implement machine learning models to learn and predict the satisfaction of the requirements over the entire set of possible parameters from a small set of simulations or experiments. In order to maximize prediction accuracy, closed-loop verification techniques are developed to iteratively select parameter settings for subsequent tests according to their expected improvement of the predictions. Second, extensions of the deterministic verification frameworks redevelop these procedures for stochastic systems and these new stochastic frameworks achieve similar improvements. Lastly, the thesis details a method for transferring information between simulators or from simulators to experiments. Moreover, this method is introduced as part of a new failure-adverse closed-loop verification framework, which is shown to successfully minimize the number of failures during experimental verification without undue conservativeness. Ultimately, these data-driven verification frameworks provide principled approaches for efficient verification of nonlinear systems at all stages in the control system development cycle.by John Francis Quindlen.Ph. D

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Control colaborativo de dinámicas múltiples

Este trabajo introduce un método de control de procesos multivariable para ser aplicado a sistemas donde coexisten varias dinámicas acopladas que deben ser controladas basándose en la medición de las entradas y salidas de cada subsistema, así como la estabilidad global del sistema. Se calculan trayectorias individuales para las consignas de modo de que esas trayectorias ayuden a rechazar en buena parte perturbaciones que afectan el comportamiento deseado. Se presenta una técnica de control recursivo no lineal basado en Lyapunov para encontrar una ley de control que ayude a resolver el problema de seguimiento. El desempeño de seguimiento es evaluado por alguna norma. Esta tesis plantea una solución al problema de las perturbaciones por un método de control que enfoca el esfuerzo en rechazar la perturbación en un solo lazo mientras desintoniza otros lazos. Al proponer movimientos de consigna como estrategia es fundamental garantizar que esas consignas modificadas sean las apropiadas. Su cálculo puede ser muy complejo si el grado de incertidumbre en la perturbación es elevado. Para cumplir con estas garantías se encuentra un conjunto admisible de consignas que rechaza de forma óptima la perturbación y al mismo tiempo no viola las restricciones ni desestabiliza el sistema. Esta tesis se fundamenta en las desigualdades lineales matriciales, LMI, y el trabajo permitió dar respuestas que por otros métodos eran muy difíciles de probar. Las LMI permiten hacer complejos planteamientos multivariable, con algunas modificaciones describen espacios no lineales. Como herramienta de optimización su eficacia es muy buena, ya que trata problemas convexos y no convexos. Se propone una modificación al esquema de control descentralizado de sistemas multivariable con un procedimiento poco invasivo, que sin retirar los controladores PID mejore su desempeño. Al ser esos procesos muy difíciles de controlar se propone un cambio en el paradigma que actualmente se aplica en la teoría de control. El control tradicional de dinámica múltiple controla la integridad de todas las variables de un proceso. Esta conducta rígida obliga a hacer un enorme esfuerzo, que es innecesario si se considera que la dinámica del proceso tolera variaciones en otras variables menos importantes. Este es un hecho que se evidencia en la práctica: Es suficiente controlar la variable que se relaciona directamente con la calidad del producto que se factura. Otro cambio de paradigma en esta propuesta consiste en evitar que una dinámica entre en conflicto con el resto del proceso, ya que esa situación origina inestabilidad en un sistema. Para atender situaciones conflictivas es acertado resolverlas por colaboración de los agentes involucrados. Se propone entonces el control colaborativo de procesos de dinámica múltiple en sistemas que inicialmente operan con unidades de control del tipo PID y atienden de forma aislada las dinámicas más representativas de un proceso. El método propuesto se inspira en una observación que se da en control de procesos complejos de múltiples dinámicas acopladas, donde los operadores logran mejorar el desempeño de un proceso haciendo pequeños retoques manuales en las consignas de los controladores. En esta propuesta un exosistema, llamado control colaborativo, mueve las consignas de forma óptima sin intervención humana, procurando un buen desempeño y logrando una solución con valor agregado. Con esta propuesta se mejora el desempeño y no es necesario remplazar los controladores PID que han demostrado que trabajan aceptablemente bienAbstract : This work introduces a multivariable process control method to be applied to systems where several coupled dynamics coexist that must be controlled based on the measurement of the inputs and outputs of each subsystem as well as the overall stability of the system. Individual trajectories are calculated for the setpoints so that those trajectories will largely reject disturbances that affect the desired behavior. We present a nonlinear recursive control technique based on Lyapunov to find a control law to help solve the tracking problem. Follow-up performance is assessed by some standard. This thesis proposes a solution to the problem of disturbances by a control method that focuses the effort in rejecting the disturbance in a single loop while it detunes other loops. When proposing slogan movements as a strategy, it is essential to ensure that these modified slogans are appropriate. Its calculation can be very complex if the degree of uncertainty in the disturbance is high. To comply with these guarantees is an admissible set of slogans that optimally rejects the disturbance and at the same time does not violate the restrictions or destabilize the system. This thesis is based on linear matrix inequalities, LMI, and the work allowed to give answers that by other methods were very difficult to prove. The LMI allow complex multivariate approaches, with some modifications describing nonlinear spaces. As an optimization tool, its effectiveness is very good, since it treats convex and non-convex problems. It is proposed a modification to the decentralized control scheme of multivariate systems with a non-invasive procedure, which without removing the PID controllers improves its performance. Since these processes are very difficult to control, we propose a change in the paradigm that is currently applied in control theory. Traditional multi-dynamics control controls the integrity of all variables in a process. This rigid behavior requires an enormous effort, which is unnecessary if one considers that the dynamics of the process tolerate variations in other less important variables. This is a fact that is evidenced in practice: It is sufficient to control the variable that is directly related to the quality of the product that is invoiced. Another paradigm shift in this proposal is to avoid that a dynamic is in conflict with the rest of the process, since that situation causes instability in a system. In order to deal with conflicting situations, it is wise to resolve them through the collaboration of the agents involved. We then propose the collaborative control of multiple dynamics processes in systems that initially operate with control units of the PID type and attend in isolation the most representative dynamics of a process. The proposed method is inspired by an observation that occurs in control of complex processes of multiple coupled dynamics, where the operators manage to improve the performance of a process by making small manual adjustments in the controller's instructions. In this proposal an exosystem, called collaborative control, moves the slogans optimally without human intervention, striving for a good performance and achieving a solution with added value. With this proposal performance is improved and it is not necessary to replace the PID controllers that have proven to work acceptably wellDoctorad