Ponencias (Ingeniería de Sistemas y Automática)
https://hdl.handle.net/11441/11344
2024-03-29T09:40:07Z
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Neurofuzzy Defocusing strategy for a Fresnel collector
https://hdl.handle.net/11441/156196
Neurofuzzy Defocusing strategy for a Fresnel collector
Concentrating Solar Power systems are widely applied as a means to utilize the sun's abundant renewable energy, but the design of these processes facilitates the occurrence of overheating. This work presents two approaches for calculating the mirror angles of a fresnel collector in order to limit the amount of solar energy collected. The proposed structures are designed to receive a desired focus value from a controller and generate references for the mirror inclination controllers of the collector. The first strategy consists of the use of an optimization problem coupled with a simplified model of the collector. Whereas, the second approach consists of an ANFIS network that is trained with data from a reliable collector model made in SolTrace®. Both approaches are compared with simulations in SolTrace® the results show that the ANFIS solution presented overall better results, taking into account error and computational time.
2023-11-01T00:00:00Z
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Predictive Control of Irrigation Canals Considering Well-being of Operators
https://hdl.handle.net/11441/155870
Predictive Control of Irrigation Canals Considering Well-being of Operators
We propose a model-predictive control (MPC) approach to solve a human-in-the-loop control problem for a non-automatic networked system with uncertain dynamics. There are no sensors or actuators installed in the system and we involve humans in the loop to travel between various nodes in the network and to provide the remote controller with measurements as well as actuating the system according to the control requirements. We compute the time instants at which the measurements and actuations should take place to yield better performance with respect to current control methods. We present simulation results using a numerical model of a real canal, the West-M canal in Arizona, and we demonstrate the superiority of the new method over previously proposed ones for such setting.
© 2023 The Authors. This is an open access article under the CC BY-NC-ND license.
2023-07-01T00:00:00Z
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Real-time monitoring and optimal vessel rescheduling in natural inland waterways
https://hdl.handle.net/11441/155838
Real-time monitoring and optimal vessel rescheduling in natural inland waterways
Despite the efforts made by the port community and the academia to develop Efficient strategies to mitigate the effect of unexpected events on the planning of vessels through natural waterways, most scheduling algorithms developed so far are not against these events unforseen events. These incidents may lead to nonoptimal operation or even to potentially dangerous situations. To tackle this issue, in this paper we propose a real-time monitoring architecture and a series of optimal rescheduling strategies to re-schedule vessels in real time when an unexpected incident is detected. The objective is to reduce the impact of the incident in the overall process while preserving safety. This is done by detecting deviations from the originally scheduled plans and taking the proper measures when incidents are detected, which will depend on the type of anomaly detected. The proposed methodology is applied to the case of the Guadalquivir river, a natural waterway located in the south of Spain.
Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license
2023-07-01T00:00:00Z
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Learning-based NMPC on SoC platforms for real-time applications using parallel Lipschitz interpolation
https://hdl.handle.net/11441/155833
Learning-based NMPC on SoC platforms for real-time applications using parallel Lipschitz interpolation
One of the main problems associated with advanced control strategies is their implementation on embedded and industrial platforms, especially when the target application requires real-time operation. Frequently, the dynamics of the system are totally or partially unknown, and data-driven methods are needed to learn an approximate model of the plant to control. On many occasions, these learning techniques use non-differentiable functions that cannot be handled by most traditional low-level gradient-based optimization methods. In addition, many data-driven techniques require the online processing of a vast amount of data, which may be exceedingly time-consuming for most real-time applications. To solve these two problems at once, we propose a low-cost solution based on a system on a chip (SoC) platform featuring an embedded microprocessor (MP) and a field programmable gate array (FPGA) to implement nonlinear model predictive control strategies. The model employed to make predictions about the future evolution of the system is learnt by means of a data-driven learning method know as parallel Lipschitz interpolation (LI) and implemented in the FPGA part. On the other hand, the optimization problem associated with the model predictive control strategy is solved by software in the MP using an adapted version of the particle swarm optimization method.
2023-07-01T00:00:00Z