Nowadays, the concept of Industry 4.0 aims to improve factories’ competitiveness. Usually, manufacturing production is guided by standards to segment and distribute its processes and implementations. However, industry 4.0 requires innovative proposals for disruptive technologies that engage the entire production process in factories, not just a partial improvement. One of these disruptive technologies is the Digital Twin (DT). This advanced virtual model runs in real-time and can predict, detect, and classify normal and abnormal operating conditions in factory processes. The Automation Pyramid (AP) is a conceptual element that enables the efficient distribution and connection of different actuators in enterprises, from the shop floor to the decision-making levels. When a DT is deployed into a manufacturing system, generally, the DT focuses on the low-level that is named field level, which includes the physical devices such as controllers, sensors, and so on. Thus, the partial automation based on the DT is accomplished, and the information between all manufacturing stages could be decremented. Hence, to achieve a complete improvement of the manufacturing system, all the automation pyramid levels must be included in the DT concept. An artificial intelligent management system could create an interconnection between them that can manage the information. As a result, this paper proposed a complete DT structure covering all automation pyramid stages using Artificial Intelligence (AI) to model each stage of the AP based on the Digital Twin concept. This work proposes a virtual model for each level of the traditional AP and the interactions among them to flow and control information efficiently. Therefore, the proposed model is a valuable tool in improving all levels of an industrial process. In addition, It is presented a case study where the DT concept for modular workstations underpins the development of technologies within the framework of the Automation Pyramid model is implemented into a didactic manufacturing system.

Nowadays, process automation and smart systems have gained increasing importance in a wide variety of sectors, and robotics have a fundamental role in it. Therefore, it has attracted greater research interests; among them, Underactuated Mechanical Systems (UMS) have been the subject of many studies, due to their application capabilities in different disciplines. Nevertheless, control of UMS is remarkably more difficult compared to other mechanical systems, owing to their non-linearities caused by the presence of fewer independent control actuators with respect to the degrees of freedom of the mechanism (which characterizes the UMS). Among them, the Furuta Pendulum has been frequently listed as an ideal showcase for different controller models, controlled often through non-lineal controllers like Sliding-Mode and Model Reference Adaptive controllers (SMC and MRAC respectively). In the case of SMC the chattering is the price to be paid, meanwhile issues regarding the coupling between control and the adaptation loops are the main drawbacks for MRAC approaches; coupled with the obvious complexity of implementation of both controllers. Hence, recovering the best features of the MRAC, an Artificial Neural Network (ANN) is implemented in this work, in order to take advantage of their classification capabilities for non-linear systems, their low computational cost and therefore, their suitability for simple implementations. The proposal in this work, shows an improved behavior for the stabilization of the system in the upright position, compared to a typical MRAC-PID structure, managing to keep the pendulum in the desired position with reduced oscillations. This work, is oriented to the real implementation of the embedded controller system for the Furuta pendulum, through a Microcontroller Unit (MCU). Results in this work, shows an average 58.39% improvement regarding the error through time and the effort from the controller.

Nowadays, owing to the growing interest in renewable energy, Photovoltaic systems (PV) are responsible of supplying more than 500,000 GW of the electrical energy consumed around the world. Therefore, different converters topologies, control algorithms, and techniques have been studied and developed in order to maximize the energy harvested by PV sources. Maximum Power Point Tracking (MPPT) methods are usually employed with DC/DC converters, which together are responsible for varying the impedance at the output of photovoltaic arrays, leading to a change in the current and voltage supplied in order to achieve a dynamic optimization of the transferred energy. MPPT algorithms such as, Perturb and Observe (P&O) guarantee correct tracking behavior with low calibration parameter dependence, but with a compromised relation between the settling time and steady-state oscillations, leading to a trade off between them. Nevertheless, proposed methods like Particle Swarm Optimization- (PSO) based techniques have improved the settling time with the addition of lower steady-state oscillations. Yet, such a proposal performance is highly susceptible and dependent to correct and precise parameter calibration, which may not always ensure the expected behavior. Therefore, this work presents a novel alternative for MPPT, based on the Earthquake Optimization Algorithm (EA) that enables a solution with an easy parameters calibration and an improved dynamic behavior. Hence, a boost converter case study is proposed to verify the suitability of the proposed technique through Simscape Power Systems™ simulations, regarding the dynamic model fidelity capabilities of the software. Results show that the proposed structure can easily be suited into different power applications. The proposed solution, reduced between 12% and 36% the energy wasted in the simulation compared to the P&O and PSO based proposals.

Nowadays, photovoltaic (PV) systems are responsible for over 994 TWH of the worldwide energy supply, which highlights their relevance and also explains why so much research has arisen to enhance their implementation; among this research, different optimization techniques have been widely studied to maximize the energy harvested under different environmental conditions (maximum power point tracking) and to optimize the efficiency of the required power electronics for the implementation of MPPT algorithms. On the one hand, an earthquake optimization algorithm (EA) was introduced as a multi-objective optimization tool for DC–DC converter design, mostly to overcome component shortages by optimal replacement, but it had never been tested (until now) for PV applications. On the other hand, the original EA was also taken as inspiration for a promising EA-based MPPT, which presumably enabled a solution with simple parametric calibration and improved dynamic behavior; yet prior to this research, the EA-MPPT had never been experimentally validated. Hence, this work fills the gap and provides the first implementation of the EA-based MPPT, validating its performance and suitability under real physical conditions, where the experimental testbed was optimized through the EA design methodology for DC–DC converters and implemented for the first time for PV applications. The results present energy waste reduction between 12 and 36% compared to MPPTs based on perturb and observe and particle swarm optimization; meanwhile, the designed converter achieved 7.3% current ripple, which is between 2.7 and 12.7% less than some industrial converters, and it had almost 90% efficiency at nominal operation. Finally, the EA-MPPT proved simple enough to be implemented even through an 8-bit MCU (ATmega328P from Arduino UNO).

It is undeniable that the energy consumption will continue to grow and the negative impact of using fossil fuels to produce it will favor the use of alternative energies. There has been an intensive research to develop clean energies from some decades now. As a result electric energy can now be generated using photovoltaic cells, wind power, fuel cells among other clean sources. Each source has advantages and disadvantages, hence, it is generally accepted that a combination of them is the best. Power electronics specialists have started to develop schemes to allow sources of different characteristics to cooperate to power a load. The device that achieve this goal has been called "energy router" by some researchers. In this work, it is shown that common multi-input power converter can function as energy routers if they are properly controlled. A control algorithm is proposed for these converters.