Architecture for Smart Buildings Based on Fuzzy Logic and the OpenFog Standard

The combination of Artificial Intelligence and IoT technologies, the so-called AIoT, is expected to contribute to the sustainability of public and private buildings, particularly in terms of energy management, indoor comfort, as well as in safety and security for the occupants. However, IoT systems deployed on modern buildings may generate big amounts of data that cannot be efficiently analyzed and stored in the Cloud. Fog computing has proven to be a suitable paradigm for distributing computing, storage control, and networking functions closer to the edge of the network along the Cloud-to-Things continuum, improving the efficiency of the IoT applications. Unfortunately, it can be complex to integrate all components to create interoperable AIoT applications. For this reason, it is necessary to introduce interoperable architectures, based on standard and universal frameworks, to distribute consistently the resources and the services of AIoT applications for smart buildings. Thus, the rationale for this study stems from the pressing need to introduce complex computing algorithms aimed at improving indoor comfort, safety, and environmental conditions while optimizing energy consumption in public and private buildings. This article proposes an open multi-layer architecture aimed at smart buildings based on a standard framework, the OpenFog Reference Architecture (IEEE 1934–2018 standard). The proposed architecture was validated experimentally at the Faculty of Engineering of Vitoria-Gasteiz to improve indoor environmental quality using Fuzzy logic. Experimental results proved the viability and scalability of the proposed architecture.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ II; to the Diputación Foral de Álava (DFA), through the project CONAVANTER; to the UPV/EHU, through the projects GIU20/063 and CBL 22APIN; and to the MobilityLab Foundation (CONV23/12), for supporting this work

Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin

Photovoltaic (PV) energy, representing a renewable source of energy, plays a key role in the reduction of greenhouse gas emissions and the achievement of a sustainable mix of energy generation. To achieve the maximum solar energy harvest, PV power systems require the implementation of Maximum Power Point Tracking (MPPT). Traditional MPPT controllers, such as P&O, are easy to implement, but they are by nature slow and oscillate around the MPP losing efficiency. This work presents a Reinforcement learning (RL)-based control to increase the speed and the efficiency of the controller. Deep Deterministic Policy Gradient (DDPG), the selected RL algorithm, works with continuous actions and space state to achieve a stable output at MPP. A Digital Twin (DT) enables simulation training, which accelerates the process and allows it to operate independent of weather conditions. In addition, we use the maximum power achieved in the DT to adjust the reward function, making the training more efficient. The RL control is compared with a traditional P&O controller to validate the speed and efficiency increase both in simulations and real implementations. The results show an improvement of 10.45% in total power output and a settling time 24.54 times faster in simulations. Moreover, in real-time tests, an improvement of 51.45% in total power output and a 0.25 s settling time of the DDPG compared with 4.26 s of the P&O is obtained

Wireless Technologies for Industry 4.0 Applications

Wireless technologies are increasingly used in industrial applications. These technologies reduce cabling, which is costly and troublesome, and introduce several benefits for their application in terms of flexibility to modify the layout of the nodes and scaling of the number of connected devices. They may also introduce new functionalities since they ease the connections to mobile devices or parts. Although they have some drawbacks, they are increasingly accepted in industrial applications, especially for monitoring and supervision tasks. Recently, they are starting to be accepted even for time-critical tasks, for example, in closed-loop control systems involving slow dynamic processes. However, wireless technologies have been evolving very quickly during the last few years, since several relevant technologies are available in the market. For this reason, it may become difficult to select the best alternative. This perspective article intends to guide application designers to choose the most appropriate technology in each case. For this purpose, this article discusses the most relevant wireless technologies in the industry and shows different examples of applications

Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control

Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change