Um método robusto aplicado no controle de formação e rastreamento de trajetória de um conjunto de robôs móveis não-holonômicos com dinâmica incerta/ A robust method applied to the formation and trajectory tracking control of a set of nonholonomic mobile robots with uncertain dynamics

Este artigo propõe a implementação do método de Controle com Rejeição Ativa de Distúrbios (ADRC) com planta modificada em uma estratégia de controle em cascata. O objetivo é realizar a formação e o controle de rastreamento de uma equipe de robôs móveis não-holonômicos com parâmetros dinâmicos incertos. Ao contrário do esquema ADRC padrão que requer um ganho de controle conhecido, o controlador ADRC modificado proposto neste artigo usa uma nova descrição entrada/saída da planta para a estrutura de cada modelo dinâmico de robô. Assim, ao introduzir essa modificação, é possível projetar um controlador robusto sem exigir o conhecimento exato sobre o ganho de controle do sistema. Resultados de simulações computacionais são apresentados para mostrar a eficiência da estratégia proposta

Fuzzy Gain-Scheduling PID for UAV Position and Altitude Controllers

Unmanned aerial vehicle (UAV) applications have evolved to a wide range of fields in the last decade. One of the main challenges in autonomous tasks is the UAV stability during maneuvers. Thus, attitude and position control play a crucial role in stabilizing the vehicle in the desired orientation and path. Many control techniques have been developed for this. However, proportional integral derivative (PID) controllers are often used due their structure and efficiency. Despite PID’s good performance, different requirements may be present at different mission stages. The main contribution of this research work is the development of a novel strategy based on a fuzzy-gain scheduling mechanism to adjust the PID controller to stabilize both position and altitude. This control strategy must be effective, simple, and robust to uncertainties and external disturbances. The Robot Operating System (ROS) integrates the proposed system and the flight control unit. The obtained results showed that the proposed approach was successfully applied to the trajectory tracking and revealed a good performance compared to conventional PID and in the presence of noises. In the tests, the position controller was only affected when the altitude error was higher, with an error of 2% lower.publishedVersio

Development of a Solar Panel Control Strategy for Tracking Maximum Power Generation / Desenvolvimento de uma estratégia de controlo de painéis solares para rastrear a produção máxima de energia

The solar panel is an essential energy conversion component of photovoltaic (PV) systems, an indispensable key for converting clean and sustainable solar energy into electricity. Over the last few years, there has been a growing demand for renewable sources due to sustainable development and global warming.  Therefore, this work  describes  the  prototype  of an electronic supervision and control system  for  the  orientation  of  a  bench  solar  panel. The developed tracker prototype has as its core an electronic circuit based on a commercial microcontroller model Tennsy 3.0, within which the control algorithm is embedded. In addition to the controller, a supervisory software was developed to monitor solar cells’ status in real-time. The supervisory showed the angle of the solar plate and values of luminosity and acquired power. Simulations results were presented to show that the amount of energy generated can   reach 37 %.

Hybrid PID-Fuzzy controller for autonomous UAV stabilization

In the last two decades, there have been great advances in using Unmanned Aerial Vehicles (UAVs) in several applications. This rapid growth is due to their ability to carry out several types of tasks with high flexibility aligned with reduced risks to human life and cost-effectiveness. The increasing demands for more complex assignments have improved UAVs' capabilities, propelling the rise of platforms with a high degree of autonomy for performing simultaneous tasks with less human intervention. One of the great challenges of maneuvering a UAV for a stabilized flight consists of a highly-coupled nonlinear system with fast dynamics. Thus, attitude control is an essential issue for stabilizing the vehicle or for keeping it in the desired orientation. The PID controller is often used due to its simple structure, good stability properties, and less dependence on the exact system model. However, its tuning process may become complicated in the face of the system nonlinearities. In this scenario, an adjustment error can cause it to lose its flight stability. Therefore, the main contribution of this work is the development of a Fuzzy-PID hybrid controller to control a quadrotor UAV's height stability. The work also considers a classic PID controller for comparison purposes with the proposed hybrid controller. The ROS/Gazebo platform was used to carry out the experiments with both control structures. From the results, it was possible to verify that both PID and Fuzzy-PID controllers could perform the attitude control of the UAV. However, the hybrid control strategy obtained some advantages, such as self-adjustment through system variations

Unmanned Aerial Vehicles Motion Control with Fuzzy Tuning of Cascaded-PID Gains

One of the main challenges of maneuvering an Unmanned Aerial Vehicle (UAV) to keep a stabilized flight is dealing with its fast and highly coupled nonlinear dynamics. There are several solutions in the literature, but most of them require fine-tuning of the parameters. In order to avoid the exhaustive tuning procedures, this work employs a Fuzzy Logic strategy for online tuning of the PID gains of the UAV motion controller. A Cascaded-PID scheme is proposed, in which velocity commands are calculated and sent to the flight control unit from a given target desired position (waypoint). Therefore, the flight control unit is responsible for the lower control loop. The main advantage of the proposed method is that it can be applied to any UAV without the need of its formal mathematical model. Robot Operating System (ROS) is used to integrate the proposed system and the flight control unit. The solution was evaluated through flight tests and simulations, which were conducted using Unreal Engine 4 with the Microsoft AirSim plugin. In the simulations, the proposed method is compared with the traditional Ziegler-Nichols tuning method, another Fuzzy Logic approach, and the ArduPilot built-in PID controller. The simulation results show that the proposed method, compared to the ArduPilot controller, drives the UAV to reach the desired setpoint faster. When compared to Ziegler-Nichols and another different Fuzzy Logic approach, the proposed method demonstrates to provide a faster accommodation and yield smaller errors amplitudes