Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are two main types of autonomous agents that have awakened academic and commercial interest within the robotics community. They are considered ideal platforms that can work individually and collectively to perform important tasks such as reconnaissance, surveillance, combat, rescue, agriculture, etc. Many of these tasks in which autonomous mobile robots are involved will have to be performed in complex, unknown, and challenging environments using its limited physical and computational resources. Hence, the control system must ensure in real-time that the robot can achieve its tasks despite external events due to payload variations, friction, manufacturing variations, modeling uncertainties, among others. Besides, the energy consumption optimization to increase the flight time is one of the challenges in UAV systems. Therefore, it is important to develop eective control strategies focusing on trajectory control, energy consumption and disturbance rejection. In recent years, the research community has addressed several of these control problems using advanced control techniques based on conventional calculus. However, many of these techniques still present drawbacks of robustness and stability of the system during unknown disturbances and errors in the modeling. On the other hand, the emergence of the fractional calculus has allowed to obtain a better response of closed-loop control systems and a better representation of hidden properties for complex modeling phenomena. Due to the benefits to represent derivatives and integrals to non-integer orders. In view of the opportunity to improve control systems in vehicles, this thesis develops different fractional control strategies for single and multiple autonomous agents in a diversity of test scenarios focused on trajectory control, energy consumption, and disturbance rejection. Most of the controllers implemented in autonomous vehicles are Proportional Integral Derivative (PID) control. Therefore, a ...
Fractional order control strategies for autonomous agents
2021-01-01
Theses
Electronic Resource
English
DDC: | 629 |
Fractional-Order Control: General Aspects
Springer Verlag | 2020
|Path Tracking Using Fractional Order Extremum Seeking Controller for Autonomous Ground Vehicle
British Library Conference Proceedings | 2017
|