Ciencias Exactas y Ciencias de la Salud

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A comparison between fuzzy logic, and PID controllers to guidance their application in a DC-DC boost converter system with PV source
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-12-01) Aguirre Damián, Bernabé; Ponce Cruz, Pedro; emiggomez, emipsanchez; Molina Gutiérrez, Arturo; School of Engineering and Sciences; Campus Ciudad de México; Sánchez García, José Manuel
There is a need to integrate alternative sources of energy due to the expected lack of conventional sources, in addition to this energy consumption is expected to grow. Solar photovoltaic (PV) energy has emerged as a highly abundant source, and its use has increased in recent years, but it is important to consider that, as this source produces direct DC power, the voltage regulation is a crucial part in PV systems. To achieve this task PID control is a widely used control technique, however intelligent control strategies have been developed in recent years, among these strategies, Fuzzy Logic Control (FLC) use has been increasing, as it aims to be a simpler control technique. PID and FLC have both been used to control the voltage in PV systems, each of these techniques being capable of regulating the output voltage in DC-DC converters, but both controllers behave differently, and each have their own tuning mechanism, so, in order to prove the performance of both control methodologies, a comparison to differentiate the main features that these controllers can bring in PV systems is required. In this work it is developed a DC-DC boost converter model in MATLAB/SIMULINK, and with the application of optimization algorithms, such as particleswarm, patternsearch, simulannealbnd and genetic algorithms it is tuned the PID gains, and it is developed a PID controller tuned by each algorithm and are compared with fuzzy controllers whose membership functions are tuned with the information obtained by the PID controllers performance. In total 8 different controllers are compared, even though the PID controllers presented lower error values, the FLC’s presented advantages in rise time and settling time reduction, overshoot minimization and ripple limitation in the response. In addition, a robustness analysis of both controller methodologies is carried out, changing the temperature and irradiance from the PV source, and a sensitivity analysis of the controllers changing the voltage reference.
Reinforcement learning for an attitude control algorithm for racing quadcopters
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06-15) Nakasone Nakamurakari, Shun Mauricio; BUSTAMANTE BELLO, MARTIN ROGELIO; 58810; Bustamante Bello, Martín Rogelio; puemcuervo; Navarro Durán, David; School of Engineering and Sciences; Campus Ciudad de México; Galuzzi Aguilera, Renato
From its first conception to its wide commercial distribution, Unmanned Aerial Vehicle (UAV)’s have always presented an interesting control problem as their dynamics are not as simple to model and present a non-linear behavior. These vehicles have improved as the technology in these devices has been developed reaching commercial and leisure use in everyday life. Out of the many applications for these vehicles, one that has been rising in popularity is drone racing. As technology improves, racing quadcopters have also improved reaching capabilities never seen before in flying vehicles. Though hardware and performance have improved throughout the drone racing industry, something that has been lacking, in a way, is better and more robust control algorithms. In this thesis, a new control strategy based on Reinforcment Learning (RL) is presented in order to achieve better performance in attitude control for racing quadcopters. For this process, two different plants were developed to fulfill, a) the training process needs with a simplified dynamics model and b) a higher fidelity Multibody model to validate the resulting controller. By using Proximal Policy Optimization (PPO), the agent is trained via a reward function and interaction with the environment. This dissertation presents a different approach on how to determine a reward function such that the agent trained learns in a more effective and faster way. The control algorithm obtained from the training process is simulated and tested against the most common attitude control algorithm used in drone races (Proportional Integral Derivative (PID) control), as well as its ability to reject noise in the state signals and external disturbances from the environment. Results from agents trained with and without these disturbances are also presented. The resulting control policies were comparable to the PID controller and even outperformed this control strategy in noise rejection and robustness to external disturbances.

Ciencias Exactas y Ciencias de la Salud

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