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In modern applied research concerning human-machine interaction, the possibility of increasing the degree of intelligence and dexterity of the controlled plant by imitating human control dynamics has been a primary objective. To reach this goal, we propose a novel nonlinear modeling technique able to predict human force generated during a cooperative task with a controlled robot. The proposed Narmax model was constructed using an artificial neural network as a nonlinear functional approximator and was firstly trained offline with data acquired from ten subjects, performing a manipulation task on a small collaborative robot. Then, given the complexity of the system and the characteristics of human response, the exploitation of Peak to Peak Dynamics allowed the development of a reduced-order model that could reliably forecast the peak of human response. Ultimately, our human model was tested online on an industrial high-payload robot, showing its general applicability and how it can be used to let the robot anticipate human intention during collaborative manipulation.

In Human–Machine interaction, the possibility of increasing the intelligence and adaptability of the controlled plant by imitating human control behavior has been an objective of many research efforts in the last decades. From classical control-theory human control models to modern machine learning, neural networks, and reinforcement learning paradigms, the common denominator is the effort to model complex nonlinear dynamics typical of human activity. However, these approaches are very different, and finding a guiding line is challenging. This review investigates state-of-the-art techniques from the perspective of human control modeling, considering the different physiological districts involved as the starting point. The focus is mainly directed toward nonlinear dynamical system modeling, which constitutes the main challenge in this field. In the end, transport systems are presented as a technological scenario in which the discussed techniques are mainly applied.

The aim of the Special Issue on Automatic Control and System Theory and Advanced Applications, the second volume of a previous paper selection, is to emphasize the role of new inventions in the area of automatic control applications [...]

Real-time frequency analysis is a task commonly implemented in last generation signal processing devices. Even if the reliability of these systems is widely recognized, detecting the presence of drifts in the frequency of analog signals is still an open problem. Besides being simple in principle, it needs a suitably long-term memory in order to determine not only in which direction the frequency drifted but also along which specific path. In this paper, we propose a novel strategy based on the concept of jump resonance in nonlinear forced oscillators to determine a digital signal processing paradigm hinged on a lookup table able to provide reliable and real-time information on complex paths of frequency drifts.

The idea of using model identification techniques in order to validate the design of printed circuit boards (PCBs) is proposed in this communication. With the term identification, it is intended to obtain the mathematical model of a system from input/output data. Actually, the quality requirements of PCB systems working with power devices are highly demanded by costumers of semiconductor companies. Due to the increasing market of power devices, the topic is of wide interest. In this note, a new approach is presented. It is based on both CAD techniques, used to simulate the board, and identification techniques, mainly based on linear models, to validate the board performance in the design phase. The main results regarding the analysis of the PCB design allow to establish if the PCB CAD procedures can be improved in order to elicit the parasitic effects aiming at compensating possible nonlinearity in the systems, thus providing the customers with reliable and simple models.

In this paper, the general concept of multidimensional discrete maps is presented. Moreover, new and fundamental results show the invariance of the bifurcation points from periodic to chaotic behavior. Numerical examples regarding the multidimensional cases of the logistic map, the complex-valued Ikeda map, and the multivariable Henon map are reported.

The realization of integrated devices determines unavoidable imperfections which are linked to the manufacturing process, thus making them imperfect systems. Imperfection is not always detrimental, as it can lead to unexpected complex and organized behaviors. In this paper we explore the possibility of designing imperfect electronic circuits producing a chaotic flow with bandwidth up to the order to several megahertz thanks to the hidden dynamics induced by construction imperfections, and whose characteristics can be tuned by means of a single variable resistor, acting as bifurcation parameter. Moreover, a strategy to estimate the parameters of the hidden dynamics is devised and the synchronization of imperfect chaotic circuits is shown. The paper further remarks that imperfection can play an important role in the realization of robust chaos generators based on simple circuits.

In this paper, a new application of active chaos is presented. It will be shown how chaotic behavior allows to address the energy transport in networks of linear oscillators. The paper is focused on RLC oscillators coupled in a network through links implemented by capacitors. The capacitances are subjected to an external input which leads to fluctuations in their values. This creates the conditions for the onset of decoherence, a situation in which a phenomenon called environment-assisted quantum transport occurs in quantum mechanics. From numerical and experimental observations, the use of active chaos to alter the value of the coupling capacitances reveals to be more effective than the use of random fluctuations. This implies that an energy control strategy for linear systems based on chaos can be outlined.

The features of the main models of spiking neurons are discussed in this review. We focus on the dynamical behaviors of five paradigmatic spiking neuron models and present recent literature studies on the topic, classifying the contributions based on the most-studied items. The aim of this review is to provide the reader with fundamental details related to spiking neurons from a dynamical systems point-of-view.

Background. This review explores state-of-the-art teleoperated robots for medical ultrasound scan procedures, providing a comprehensive look including the recent trends arising from the COVID-19 pandemic. Methods. Physicians’ experience is included to indicate the importance of their role in the design of improved medical robots. From this perspective, novel classes of equipment for remote diagnostics based on medical robotics are discussed in terms of innovative engineering technologies. Results. Relevant literature is reviewed under the system engineering point of view, organizing the discussion on the basis of the main technological focus of each contribution. Conclusions. This contribution is aimed at stimulating new research to obtain faster results on teleoperated robotics for ultrasound diagnostics in response to the high demand raised by the ongoing pandemic.

In this contribution, the main guidelines that, in the opinion of the authors, will address bioinspired technologies in the next future are discussed. The topics are related to some specific subjects. The presented perspectives could be useful to remark how bioinspired technologies can be applied to solve every day problems in a low cost and sustainable way. Moreover, all the considerations reported hallmark the need of changing the paradigm to design innovative bionspired systems. Efficient and alternative bioinspired systems cannot be designed by only looking at macroscopic scale as observed in nature. The efforts of this paper are oriented towards providing a wide perspective on bioinspired technologies as complex systems where nonlinear phenomena are fundamental elements.

Analog circuits are essential in everyday life and in electrical engineering [...]

The world is currently in the age of energy [...]

The fascinating subject of chaos has been the focus of researchers and scientists from many fields over the last 40 years [...]

The future of engineering systems is based on the capability of integrating sensors, actuators, control systems and materials [...]

Non-Intrusive load monitoring (NILM) represents an emerging strategy based on the application of sevaral multidisciplinary topics [...]

Robotics and micromachines are challenging topics in engineering [...]

This century has been considered the age of Complexity, and therefore that of nonlinear circuits and systems [...]

The study of human–machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human–machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory.

Companies working on semiconductors must currently assure the customers of not only the performance of the semiconductor device per se, but also its performance when it is implemented in a real board, therefore including the role of parasitic effects. It is therefore very important to evaluate, especially during the design phase, not only the single device, but the complete board and their mutual interactions. This consideration opens a new area of investigation in the field of electronic systems engineering. In the current literature, the problem of a software evaluation of parasitic dynamics and electromagnetic effects on printed boards is addressed from the point of view of researchers. Moreover, it is fundamental to have a complete view of the various tools that could be usefully adopted from the perspective of manufacturers. This is the main motivation of this technical note, which performs a comparative analysis of the most prominent software tools for printed circuit boards’ (PCBs) simulation. The main features, the key aspects, and the limitations of the software packages are analyzed in terms of the industrial design of power electronics devices, in order to ensure efficiency and fastness in the semiconductor market.

Lava flow mapping has direct relevance to volcanic hazards once an eruption has begun. Satellite remote sensing techniques are increasingly used to map newly erupted lava, thanks to their capability to survey large areas with frequent revisit time and accurate spatial resolution. Visible and infrared satellite data are routinely used to detect the distributions of volcanic deposits and monitor thermal features, even if clouds are a serious obstacle for optical sensors, since they cannot be penetrated by optical radiation. On the other hand, radar satellite data have been playing an important role in surface change detection and image classification, being able to operate in all weather conditions, although their use is hampered by the special imaging geometry, the complicated scattering process, and the presence of speckle noise. Thus, optical and radar data are complementary data sources that can be used to map lava flows effectively, in addition to alleviating cloud obstruction and improving change detection performance. Here, we propose a machine learning approach based on the Google Earth Engine (GEE) platform to analyze simultaneously the images acquired by the synthetic aperture radar (SAR) sensor, on board of Sentinel-1 mission, and by optical and multispectral sensors of Landsat-8 missions and Multi-Spectral Imager (MSI), on board of Sentinel-2 mission. Machine learning classifiers, including K-means algorithm (K-means) and support vector machine (SVM), are used to map lava flows automatically from a combination of optical and SAR images. We describe the operation of this approach by using a retrospective analysis of two recent lava flow-forming eruptions at Mount Etna (Italy) and Fogo Island (Cape Verde). We found that combining both radar and optical imagery improved the accuracy and reliability of lava flow mapping. The results highlight the need to fully exploit the extraordinary potential of complementary satellite sensors to provide time-critical hazard information during volcanic eruptions.

Leonardo da Vinci inventions and projects represent an intriguing starting point to remark the concept that innovation must be considered as a continuous route towards evolution in history. Some of the particular ideas and innovations presented by Leonardo da Vinci led us to formulate a link with automatic control. Selected models of the Leonardo da Vinci machines are presented in this paper, taking strictly into account the original mechanical schemes and working principles, but introducing modern low-cost control equipment, emphasizing the role of automatic control and that of electronic control devices, such as microcontrollers, sensors, and communication devices, to completely automate the Leonardo da Vinci machines. The approach outlined in the paper can be applied not only to other Leonardo machines but also to other mechanical equipment not necessarily designed by Leonardo da Vinci. Moreover, it is useful to remark that the approach followed in this paper can be very important also to introduce students, leading by example, to concepts typical of automation and for assisting in learning, keeping in mind the practical applications of advanced automation principles. The main research task of this paper is proving the efficacy of modern digital control techniques and teleoperation in strongly classical mechanical Leonardo machines, remarking that the projects of Leonardo are prompt to be efficiently controlled. This task could not be explored by Leonardo himself due to the lack of control technology. Moreover, the paper is addressed also to stimulate the young generations of engineers in joining classical mechanics with advanced technology. Therefore, the paper is also devoted to give focus on the fact that the Leonardo machines encompass all the key aspects of modern system engineering.

In this contribution, we approached a new aspect in robotic applications. We investigated human–machine modeling for remote ultrasound scan equipment. While robotic systems for ultrasound scan applications with remote operations have been widely studied, in this research, remote force-feedback control was tested. The goal is for the human operator to receive, as physical input, the correct force perception transmitted by the remote ultrasound scan equipment in analyzing the body of the patient. Two principal aspects were investigated. The first was an artificial body model to receive the control signals from the remote equipment. The second aspect was to study a suitable feedback control law that attempts to compensate for the uncertainty between the artificial body and the patient’s body, while also taking into account the transmission delay. Therefore, the task was to give the operator relevant information while considering the force effect; thus, providing a reliable and efficient platform in order to work in remote conditions with ultrasound scan equipment.

The huge amount of information coming from remote sensors on satellites has allowed monitoring changes in the planetary environment from about 50 years. These instruments are widely adopted to observe extreme thermal events such as eruptive phenomena in volcanic areas. Although the availability of so many different infrared sensors makes these instruments suitable to observe different kind of thermal phenomena, choosing the right infrared sensor to monitor each thermal event is not straightforward. In fact, the decision should take into account both the main features of the phenomena under investigation, e.g., its size and temperatures, that are often not known a priori, and the instruments specifications, e.g., spatial resolution. Here, a smart decision support system (SDSS) is proposed to address this task. In particular, we used a SDSS to simulate remote sensors responses, collect data coming from three different classes of remote sensors, retrieve information about the main features of the observed thermal event and, consequently, select the most suitable infrared remote sensor for the specific observed phenomena. Results obtained for a real case of study at Etna volcano is shown.

Accurate detection of time gravity changes attributable to the dynamics of volcanoes requires high-precision gravity measurements. With the aim of improving the quality of data from the Mount Etna gravity network, we used both absolute and relative gravimeters in a hybrid method. In this report, some of the techniques for gravity surveys are reviewed, and the results related to each method are compared. We show how the total uncertainty estimated for the gravity measurements performed with this combined use of absolute and relative gravimeters is roughly comparable to that calculated when the measurements are acquired using only relative gravimeters (the traditional method). However, the data highlight how the hybrid approach improves the measurement capabilities for surveying the Mount Etna volcanic area. This approach enhances the accuracy of the data, and then of the four-dimensional surveying, which minimizes ambiguities inherent in the gravity measurements. As a case study, we refer to two gravity datasets acquired in 2005 and 2010 from the western part of the Etna volcano, which included five absolute and 13 relative stations of the Etna gravity network.

Spaceborne remote sensing of high-temperature volcanic features offers an excellent opportunity to monitor the onset and development of new eruptive activity. To provide a basis for real-time response during eruptive events, we designed and developed the volcano monitoring system that we call HOTSAT. This multiplatform system can elaborate both Moderate Resolution Imaging Spectroradiometer (MODIS) and Spinning Enhanced Visible and Infrared Imager (SEVIRI) data, and it is here applied to the monitoring of the Etna volcano. The main advantage of this approach is that the different features of both of these sensors can be used. It can be refreshed every 15 min due to the high frequency of the SEVIRI acquisition, and it can detect smaller and/or less intense thermal anomalies through the MODIS data. The system consists of data preprocessing, detection of volcano hotspots, and radiative power estimation. To locate thermal anomalies, a new contextual algorithm is introduced that takes advantage of both the spectral and spatial comparison methods. The derivation of the radiative power is carried out at all ‘hot’ pixels using the middle infrared radiance technique. The whole processing chain was tested during the 2008 Etna eruption. The results show the robustness of the system after it detected the lava fountain that occurred on May 10 through the SEVIRI data, and the very beginning of the eruption on May 13 through the MODIS data analysis.