This paper presents a robust feedback control solution for systems with multiple manipulated inputs and a single measurable output. A structure of parallel controllers achieves robust stability and robust disturbance rejection. Each controller uses the least possible amount of feedback at each frequency. The controller design is carried out in the Quantitative Feedback Theory framework. The method pursues a smart load sharing along the frequency spectrum, where each branch must either collaborate in the control task or be inhibited at each frequency. This reduces useless fatigue and saturation risk of actuators. Different examples illustrate the ability to deal with complex control problems that current MISO methodologies cannot solve. Main control challenges arise due to the uncertainty of plant and disturbance models and when a fast‐slow hierarchy of plants cannot be uniquely established.

Unmanned aerial vehicles (UAV) in the civil sector have recently evolved from being devices for recreational uses to becoming aircraft for professional uses. Professional applications of these devices require the aircraft to ship more and more complex sensors for reasons of safety in the event of failures. However, controlling all these systems is a challenge for flight controllers at the hardware and software levels. In this context, this work proposes a new aircraft real-time flight control system. The flight controller hardware is made up of two systems: a core board houses common sensor and flight devices; a flexible unit, isolated from body vibrations, houses redundant sensors to improve accuracy and reliability. The application functions are driven by the microcontroller running a new real-time operating system (RTOS) to better schedule works on limited computation power. To design a simplified RTOS for embedded systems, a hybrid scheduler (first-come-first-serve scheduling and earliest-deadline-first scheduling) with dynamic priority is proposed. The results obtained show the effectiveness of the system using schedulability tests for uni-processor systems. A set of real data supports the performance of the flight controller.

Un cuatrirrotor con todo el equipamiento de vuelo se encuentra fijado a una estructura que permite la rotación en el espacio sin desplazamiento. Además, un conjunto de herramientas software desarrolladas con MATLAB-Simulink® ejecutan la programación de su controladora y gestionan la transmisión en tiempo real de consignas y estados del vuelo pilotado remotamente. Para este banco de pruebas se ofrece un simulador que reproduce fielmente el comportamiento del sistema real con el fin de plantear un benchmark de Ingeniería de Control. El problema propuesto es controlar la orientación del mutirrotor definida por los ángulos de Euler. Para ello, deben generarse las tres acciones de control que atacan al sistema de propulsión, considerando las velocidades y ángulos que estima el sistema de navegacion y las consignas angulares. Para lograr un mayor realismo, en las pruebas de comportamiento se pueden modificar la tensión de alimentación, que simula el nivel de carga de la batería, y una acción de control que emula el control de la altura, lo que da lugar a diferentes puntos de operación. El simulador permite configurar experimentos en lazo abierto o cerrado, para tareas de identificación o para analizar el comportamiento de los controladores en diferentes puntos de operación y ante diferentes entradas. El objetivo final es incorporar una ley de control que mejore el comportamiento dado como referencia para cierto experimento. Tras una simulación, una función de evaluación cuantifica las diferencias en el error de seguimiento y en la acción de control entre el control actual y el de referencia para cada grado de libertad. El principal desafío es optimizar el reducido ancho de banda disponible para controlar un sistema dinámico complejo.

This study focuses on the modeling and control optimization of the pitch–roll angles orientation in a quad-rotor UAV (unmanned aerial vehicle) drone system. To address the control challenge, PID (proportional–integral–derivative) controllers are used, which have been tuned through a model-based optimization approach. A non-linear model of the system is obtained and further validated experimentally. The model is implemented in Simulink to conduct model-based optimization. The controller design uses an IMC (internal model control) model-based approach to establish the initial parameters for a PID controller. In the subsequent design phase, a multiobjective approach is taken, incorporating weighted cost functions that account for IAE (integral absolute error) and ITAE (integral of time-weighted absolute error) forms, along with battery usage. The outcomes of this design are showcased through simulations and real-world platform testing.

The transition from academic training to professional life is not an easy task. In recent years, various learning enhancement methodologies have emerged to reduce this gap, which focuses on both soft and technical skills. Specifically, Competition-Based Learning (CBL) seeks to improve learning and motivation through the competitive component, which has detractors and defenders. In this framework, the present work proposes a national competition, CIC2022, within the field of control engineering that uses a testbed with an Unmanned Aerial Vehicle (UAV) for helping to link theory and practice. The main objective is to analyze the results of CBL on learning. In addition to explaining the characteristics of the competition and the academic results obtained, a survey answered by participants provides conclusions on the effectiveness of the methodology and other issues related to the contest. Greater motivation and teamwork promoted the active learning of students and improved the autonomous acquisition of technical skills.

This paper examines methods to incorporate feedforward loops of known external inputs (output reference) into a multi-input feedback control structure to achieve certain robust performance of its output. Undoubtedly, feedforward can reduce the need for feedback and therefore the amplification of sensor noise at actuators, as occurs in single-input control. Beyond that, since there are several available inputs, a convenient distribution of feedforward and feedback can minimise the control action at each input and offer benefits at all frequencies. The procedure is as follows: because there are rough plant models of the behaviour from each input to the output, it is possible to approximate the individual control demand that will satisfy the performance. Based on this, individual feedforward filters allocate the control bandwidth among the inputs in order to build an equivalent plant that has an equal or greater magnitude than any individual plant at each frequency. Next, the uncertainty of this equivalent plant is addressed by feedback that reduces the closed loop deviation of magnitude frequency responses. The reduction is sufficient to enable a master feedforward to place them, at a second step, around the desired tracking performance model without violating any deviation tolerances. Individual feedback controllers distribute the total feedback among the inputs in order to have the least possible feedback at each frequency. A first example illustrates the method and the relevance of a feedforward orientation to reduce the individual control action, instead of the individual feedback action. Another example proves the superiority of adding feedforward loops to feedback-only schemes and highlights the benefits of robust design methods such as Quantitative Feedback Theory (QFT). This paper also provides the algorithms to employ in response to new robust control specifications in the framework of QFT.