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.

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.

In this paper a control theoretical solution is presented for the direct adaptive visual dynamic control of planar manipulators using a fixed camera, when both camera calibration and robot dynamics are uncertain. The proposed scheme is developed for image-based look-and-move visual systems to allow tracking of a moving target. In order to solve the multivariable parameter adaptive problem, the recently proposed Immersion and Invariance (I&I) method is used. The scheme is then combined with an adaptive controller for the manipulator, taking into account its nonlinear dynamics. This leads to an overall stable adaptive visual servoing system. An extension of the I&I approach for 3D visual servoing is shown to be possible by a convenient control reparametrization associated with a proper image-based depth control scheme.

This paper presents an obstacle avoidance technique for a mobile robot in human-robot collaborative (HRC) tasks. The proposed solution uses fuzzy logic rules and a convolutional neural network (CNN) in an integrated approach to detect objects during vehicle movement. The goal is to improve the robot’s navigation autonomously and ensure the safety of people and equipment in dynamic environments. Using this technique, it is possible to provide important references to the robot’s internal control system, guiding it to continuously adjust its velocity and yaw in order to avoid obstacles (humans and moving objects) while following the path planned for its task. The approach aims to improve operational safety without compromising productivity, addressing critical challenges in collaborative robotics. The system was tested in a simulated environment using the Robot Operating System (ROS) and Gazebo to demonstrate the effectiveness of navigation and obstacle avoidance. The results obtained with the application of the proposed technique indicate that the framework allows real-time adaptation and safe interaction between robot and obstacles in complex and changing industrial workspaces.

This work proposes a mathematical solution for plants with full set of uncertain parameters which is based on the active disturbance rejection control method. This method has been studied for three decades and its main characteristic is the use of an extended state observer to estimate the non‐measurable signals of the plant. However, the application of the basic methodology requires the exact knowledge of the system control gain, which is difficult to measure in the case of parametric uncertainties. Recently, some advances have been discussed in the literature. Some results have revealed the capability of them of rejecting the unknown input disturbance and of ensuring the stability of closed‐loop transfer functions, even in the case of unknown order and/or relative degrees. However, such proposals have not been effective for large level of parametric uncertainties. The idea of the present work is to relax the requirement of exact knowledge of plant parameters by proposing modifications in the control structure. Mathematical analysis show that the proposed modification does not affect the original purpose of output tracking. The performance of the proposed strategy is compared with other schemes by using numerical simulations. In addition, experimental results on a real cart‐pendulum system are presented.

This paper proposes an improved active disturbance rejection control (ADRC) method for output tracking of uncertain plants with unknown control direction. A basic design procedure in the ADRC methodology is to assume the exact knowledge of the system control direction, that is, the sign of its input channel coefficient. Recent works have been proposed to relax such a requirement by performing modifications in its control structure. However, despite considering uncertainties in the input coefficient, many variants of the ADRC method still assume the knowledge of the control direction, which means that the control gain is uncertain in norm, but not in sign. For solving the latter case, and also aiming to generalize the earlier ADRC results for a larger class of systems, the present work incorporates the concept of monitoring functions in the controller design. It is a switching‐based strategy whose main function is to determine the correct sign of the control direction, which is directly related to the sign of the plant input channel coefficient. As a consequence of the resulting new control method, exponential stability with respect to a small residual set is guaranteed for the output tracking problem in closed‐loop. Numerical simulations are performed and discussed initially in an academical example, only for comparing the robustness properties concerning the unknown control direction of the proposed strategy with another ADRC methodology. In the sequence, the proposed ADRC strategy based on monitoring function is applied to the automotive plant system of an Anti‐Lock Braking System for illustrating its performance.