Level control is one of the most used processes in industries. However, it can present nonlinearities, which can make difficult its project. The PID controller is still a commonly used topology due to the non-necessity to know the full system dynamics, only the modelling that well describes the system behavior. The objective of this work is to identify, control and audit a level tank system from a SMAR ^® didactic plant. Firstly, system identification techniques (Smith, Bröida, Vitečková and Artificial Neural Network) were used to perform the controllers tuning later, approaching it to a First Order plus Dead Time transfer function (FODT). To tuning PI/PID controllers, optimization methods were used, such as Bat Algorithm, Bacterial Foraging Optimization, Genetic Algorithm, Bee Swarm, Bat Algorithm, Ant Colony Optimization, and Shuffled Frog-Leaping. Beyond optimization methods, analytical/classical PI/PID controllers tuning techniques (Cohen-Coon, Hallman, Internal Model Control (IMC), Chien-Hrones-Reswick (CHR), and Integral of Absolute Error (ITAE)) were also introduced to do this parameterization. In order to compare the simulated and experimental results, non-intrusive performance indexes based on integral errors (IAE, ISE, ITAE and ITSE) were introduced to evaluate and choose the best performance. The results were interesting, showing that the classical identification technique Bröida had the best response. For PID controllers tuning, optimization algorithms overcame analytical/classical tuning techniques performance, proving a better execution, performed by Shuffled Frog-Leaping technique.

This work presents the modeling and practical control design of an UAV (Unmanned Aerial Vehicle) based on a quadcopter type. The PID controllers were set for angular position stability (roll and pitch) and yaw speed. The PID gains were designed based on the Saturation Constraints and Performance and Root-Locus technique. The prototype was built with low cost equipment, with satisfactory practical results, producing the expected behavior, allowing safe flight conditions and reliability. The adopted control validation parameters were; peak time (Tp), settling time (Ts) and the percentage overshoot (%UP).

By definition, UAV means Unmanned Aerial Vehicle, which in fact has been growing in the recent years, encouraged by technological developments, especially in the electronics and military automation areas. Regarding that subject, this work aims to develop, simulate and compare two different control techniques in quadcopter altitude position. Low-Level Proportional Integral Derivative (PID) controllers are developed for vehicle angular stabilization (roll, pitch and yaw). Using the Successive Loop Closure technique, two high-level control techniques were designed to track the aircraft vertical position: A nonlinear one, based on Lyapunov functions to develop Integral Backstepping; And another linear, based on the PID method. Finally, the results were simulated and analyzed through non-intrusive performance indexes, based on the integral errors (IAE, ISE and ITSE). The results indicated that the nonlinear controller designed was superior compared to the linear one. However, both techniques were capable of controlling the UAV altitude dynamics.

The research and use of UAVs (Unmanned Aerial Vehicles) have grown in attention and new ideas in the last couple of decades, mainly due to technology improvement of its construction parts, sensors and controllers. When it comes to its autonomous behavior, robust techniques make themselves necessary to assure the security of the aircraft and people around it. This work presents the use of computer vision techniques to improve the positioning of a quadrotor UAV in the landing moment, adding security and precision, overcoming intrinsic errors of other positioning sensors, such as GPS. A landmark was built to be detected by the camera with contour detection, image thresholding and other mathematical techniques in order to obtain fast, simple and robust processing and results. A GoPro camera model was used with a developed algorithm to remove the image distortion. With this set up, the vehicle can be programmed to develop any mission, with this works control algorithm taking part with the vision aided positioning in the landing moment. The quadrotor was conceived and built at the GRIn (Grupo de Robtica Inteligente) laboratory at UFJF (Juiz de Fora Federal University), and uses open source code for its control, with Mavlink communication protocol to exchange messages with the vision-based control algorithm for mission and landing. This algorithm was developed in Python language, and is in charge of image characteristics segmentation, extracting distances and altitude from the data. The control commands are sent to the quadrotor using the mentioned protocol. The system is structured with a remote laptop connected with a serial port to the quadrotor, and a router transmits messages back and forth among the GCS (Ground Control Station, on the laptop) and the high-level control algorithm (mission planning and vision based control) through TCP ports to the quadrotor itself. The camera is connected via Wi-Fi built-in network to the laptop, so all the image processing is done inside the last one. The results obtained in outdoor tests show the efficacy and fast processing of the vision methods and ensure safety in the landing moment. Furthermore, the positioning techniques can be extended to other UAV applications, such as transmission lines and utility equipment inspection and monitoring. Future works look forward to embedding the process in a companion board attached to the UAV, so the process would become automatic and the risk of communication interference and lost heavily reduced.

This work presents a new Active Fault-Tolerant Control (AFTC) method to maintain the Unmanned Aerial Vehicles (UAV) stable and controllable even under a total fault up to one propulsion system actions. The proposed approach is based on the control system re-configuration of the remaining propellers. As the system is no longer controllable in all six degrees of freedom, one dimension should be abandoned. In this case, the control of yaw dynamics was chosen to be ignored because less crash risk is introduced. Than the remaining variables became controllable leading the aircraft to safely land and without shortcoming.