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The problem of identifying and therefore modelling a complex system makes use of various techniques and strategies whose computational efforts change drastically. It is not straightforward to analyse the complexity of a system as a whole because of myriads of factors, such as the way of arranging its constituent items and how they interact mutually. Intuitively, the bigger the set of sub-parts is, the more numerous the degrees of freedom are. Additionally there is not a specific and global criterion for optimally determining an always-working method that makes the identification procedure easier, especially in those contexts where the number of unknown variables can make the difference. In this sense, plasma physics is not an exception, being a field where complex phenomena, such as plasma instabilities, easily arise. From a systemic, high-level perspective, the possibility of employing a model that can describe these behaviours is particularly appealing, since it can be exploited for control applications that have not to neglect the underlying physical nature. So far, most of the work published in literature has focused on more physically-grounded models, which could describe how plasma physics works in detail, but very little has been done as mentioned before, with the aim of providing a computational, yet system-oriented, insight of these physical systems. Starting from real flux measurements recorded thanks to suitable sensors installed inside Tokamak machines, the paper attempts to provide a solution based on already known tools available in literature to solve the aforementioned problem, by combining both machine learning-based strategies for dimensionality reduction and control theory. More in detail, the whole architecture presented in this work is founded on the use of auto-encoders, which are intrinsically capable of compressing input features thanks to their structure, and Hammerstein–Wiener models, which are structurally endowed with both linear and non-linear sub-modellers for better capturing the whole dynamics to identify. By merging these functional blocks, it is possible to address both the issue of establishing the most relevant sub-set of variables for identification and the identification problem itself, resulting in a fully customisable approach to data-driven modelling.

In this communication, some concepts related to matrix numerical computations are reviewed. In particular, the relationship among matrix computing with suitable dynamical systems is reported. It is also presented the possibility of obtaining the open-loop balanced representations of linear time-invariant systems by using an analog computation paradigm. This point of view allows to remark an analog circuit approach towards system theory computation.

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.

In this paper, the main concepts and the preliminary results related to a new approach for creating innovative green laboratory experiences in applied science and technology will be discussed. The term ebatronics is here introduced for the first time in the literature to indicate a kind of experimental laboratory based on the conjunction of wooden recycled materials and microcontroller based devices. In particular, tensegrity based systems are presented. A gallery of prototypes developed by the authors is shown. An intense set of photos will illustrate the real effectiveness of the proposed laboratory project.

In this contribution, a new aspect in robotic basic applications is approached. The problem regards the human-machine modeling for remote ultrasound scan equipments. Even if robotic systems for ultrasound scan applications with remote operations have been widely studied, in this research the remote force feedback control has been investigated. This means that the human operator does receive as input the correct force perception that is transmitted by the remote ultrasound scan equipment in analyzing the body of the patient. Two principal aspects have been investigated. The first one is to consider an artificial body model that will receive the control signals from the remote equipment. The second one is to study the suitable feedback control law that try to compensate both the uncertainty between the artificial body and the patient body also taking into account the transmission delay. Therefore the task is to give to the operator the real perception also taking into account of the force effect, thus improving the experience of the remote operation in performing ultrasound scan procedures.

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.

Plasma physics is an example of research field where many measurements carried out at very specific working conditions need to be collected and processed. By looking at the properties of these data, it can be possible to explore their hidden features in order to solve challenging problems that usually require high computational efforts, such as the tomographic reconstruction. In this paper, preliminary but nontrivial analyses of flux measurements produced in a Tokamak machine are shown and discussed, with the aim of introducing an application of some algorithms for feature selection to detect hidden, relevant relationships within given sets of channels. All the statistical details, and therefore the feature selection procedure itself, are introduced in view of further deepenings, such as the aforementioned problem of tomographically reconstructing plasma profiles from flux measurements or modelling the system in terms of its input-output relationship.

This paper presents a Model-Based Design (MBD) methodology as a promising approach for the rapid and se-cure development of Embedded System applications, including those involving digital controllers. Based on existing hardware and software application-oriented tools by STMicroelectronics, a new modeling technique has been implemented to move from a traditional design workflow to an MBD one by using Mathworks®software platform. For a practical application of the proposed approach, a case study is reported to design a Permanent Magnet Synchronous Motor (PMSM) drive based on the STM32 MCU family. The main contribution of this paper is to make focus on concretely building a unique model architecture of a complex software/hardware system able to implement all the major aspects of MBD methodology such as executable requirements, Normal and Processor-In-the-Loop (PIL) simulation modes, continuous test and verification, automatic code generation. To achieve this purpose, a new Simulink®blockset dedicated to the STM32 Motor Control ecosystem was developed. It includes Simulink blocks for maths, algorithms, and IPs, electronic circuitries, MCU peripherals, speed sensors. Simulink®Embedded Coder tool has been used to automatically generate code for a specific STM32 MCU tar-get to overcome the time consuming and error-prone problems of the handwritten coding.

This paper applies Model-Based Design methodologies to develop a high-performance device driver model suitable for advanced smart solutions. These modeling techniques are mandatory to use when designing advanced smart applications based on embedded devices. The paper proposes an innovative approach to accurately models the operations of the STM32 analog converter peripheral for both simulation purpose and to generate an optimized code that integrates on the Low-Level STM32 firmware driver. A new Simulink toolbox has been developed to replicate the ADC functions. Finally, an example has been presented to show the effectiveness of this new modeling approach.

In this paper, a novel method to determine from experimental observations the thermal models of integrated devices is presented. In particular, we focus on experimental data providing the temperature distribution over the device surface and determine a model in which the spatial diffusion of heat loads is explicitly taken into account and modeled. The adoption of this approach leads to the possibility to sensibly reduce the order of complete thermal model, with respect to classical approaches, even considering numerically generated datasets.

The behavior of complex electromechanical systems is strictly related to the mechanical vibrations to which they undergo. It is also well known that their performance are often positively improved by the imperfections linked to the technological construction process. Indeed this controversial aspect has been generalized in a wider class of systems that has been mentioned as imperfect systems. In this paper, we focus on the possibility of reviewing the imperfect system framework highlighting the connections with the stochastic resonance phenomenon. The performance of imperfect systems are evaluated in presence of intentionally induced noise, showing that a positive effects for enhancing the correct behavior of the system can be obtained by soliciting the dynamics connected to imperfections. The main objective that is discussed in this contribution is to formulate a connection between imperfect systems and stochastic resonance behavior of complex devices.

Synchronization of chaotic dynamics can be pursued by means of different coupling strategies. Definitely, master-slave coupling represents one of the most adopted solutions, even if it presents some limitations due to the coupling term's selection strategy. In this paper, we investigate the role of different structures of coupling terms on the synchronization properties of master-slave chaotic system configurations. Here, Lyapunov theory for linear systems with nonlinear vanishing perturbations is exploited. The obtained results allow to determine the capability of a static, dynamic, or mixed coupling connection in stabilizing the synchronization manifold, using linear techniques based on the root locus. This knowledge allows to design the coupling structure considering also the synchronization error transient features, which are, here, shown to improve in the presence of higher-order dynamic couplings. A number of cases of study, involving classical chaotic nonlinear systems, show the efficacy and simplicity of the application of the strategy proposed.

In the recent years, two-phase microfluidics has contributed to various scientific areas such as chemistry, biology and medicine. In particular, two-phase flows, obtained from two immiscible fluids or a mix of fluid and microparticles, gained attention for their possible application in implementing logic gates and programmable devices. Moreover, their behavior in networks of microchannels is still not well studied, even if some experimental results are presented in literature but far from well-established framework that can drive to the flow control. Recently, the dynamics of two-phase microfluidic setups has been characterized by means of chaos synchronization obtaining a behavioral model of the bubble flow. Starting from these results, in this contribution the possibility to model the bubble flow behavior in a two-phase microfluidic with a simple nonlinear two-element circuit composed by a capacitor and a memristive device. The circuit is able to synchronize its dynamics to the microfluidic setup behavior, therefore acting as a qualitative sensor of the hydrodynamic regime.

Recent results have shown that, for continuous-time systems obtained by cascading an asymptotically stable system with an inner system, the first $n$ Hankel singular values are greater than or equal to those of the original system. Similarly, cascading a system in minimal form with an inner system, the same property holds for the linear quadratic Gaussian (LQG) characteristic values. In this article, we consider the discrete-time case and demonstrate that the property also holds for these systems. A very important consequence stemming from these results is that the Hankel singular values and the LQG characteristic values of input-delayed discrete systems are greater than or equal to those of their zero-delay counterpart.

Electromechanical systems are often subjected to mechanical stress and vibrations which deeply influence their overall behavior, and nonlinear effects are predominant in such class of physical systems. The main objective of this short note is to propose evidence of stochastic resonance behavior in complex electromechanical devices, where noisy vibrations regularize the behavior of the device.

In this paper, we focus on the thermal characterization of nonlinear electronic circuits deriving some conditions which suggest the existence of a relationship with the specific dynamic behavior shown by the circuit. A series of thermographic experiments indicate that the chaotic behavior in electronic circuits is associated with slower thermal transients, a slow linear growth of the temperature during the normal operating regime and lower overall temperature increase with respect to other dynamical regimes. Three experimental case studies are reported and discussed, considering also the effect of chaos control strategies.

In this paper, new properties of the cascade between a multi-input multi-output linear time-invariant system, referred to in the following as the original system, and an inner system are dealt with. In particular, attention is devoted to the relationship between the Hankel singular values and characteristic values of the cascade system and those of the original one, proving that, if the inner system has gain greater than or equal to one, then the first $n$ Hankel singular values (characteristic values) of the cascade system are greater than or equal to those of the original system. The results are then applied to derive a new property of minimum phase systems.

In this paper the existence of multijump resonance is described for the driven Chua circuit. Multijump resonance is a phenomenon observable in driven nonlinear systems which leads to a multi-valued frequency response, showing several hysteresis windows with respect to the increase/decrease of the driving frequency. The phenomenon is here investigated in order to characterize its onset as a function of system parameter values. Finally, a physical implementation of the driven Chua circuit, in which jump and multijump resonance occur, is presented and discussed allowing to verify the robustness of such nonlinear phenomenon.

Mathematical modeling is very helpful for noninvasive investigation of glucose-insulin interaction. In this paper, a new time-delay mathematical model is proposed for glucose-insulin endocrine metabolic regulatory feedback system incorporating the [Formula: see text]-cell dynamic and function for regulating and maintaining bloodstream insulin level. The model includes the insulin degradation due to glucose interaction. The dynamical behavior of the model is analyzed and two-dimensional bifurcation diagrams with respect to two essential parameters of the model are obtained. The results show that the time-delay in insulin secretion in response to blood glucose level, and the delay in glucose drop due to increased insulin concentration, can give rise to complex dynamics, such as periodic oscillation. These dynamics are consistent with the biological findings and period doubling cascade and chaotic state which represent metabolic disorder that may lead to diabetes mellitus.

The concept of imperfection is always associated to a negative outlook. However, real devices are always imperfect and operate far from ideality. Despite this, real devices actually work. This is due to the fact that imperfections give rise to hidden dynamics, which can be excited to gain a positive effect on the overall behavior of the device. Human-machine systems are relevant examples of imperfect systems in which the presence of imperfections plays a crucial role, either negative or positive. In this paper, we focus on a paradigmatic example of imperfect systems, represented by a complex and imperfect electromechanical structure which supports and couples 15 coils rotating thanks to the electromagnetic interaction with associated magnets. The electrical and mechanical interaction between the coils and that between the coils and the structure generate complex patterns of vibration which may prevent the system from reaching the correct working conditions, i.e., all coils actually rotating. A control strategy to ensure coils rotation based on the excitation of the hidden dynamics induced by imperfections is discussed, characterizing its effect with respect to the control signal properties and to the power provided to the structure. It is worth to notice that the control strategy is essentially based on the excitation rather than on the suppression of the hidden dynamics, thus providing evidence on the possibility to control imperfect systems exploiting imperfections.

In this paper, a fractional order representation and the related hardware realization of the Duffing system are presented. The considered system can be defined as the general case of the standard Duffing system, which is a typical example of a non autonomous chaotic system. The aim of this paper is to confirm that an opportunely excited fractional order Duffing system, with order less than two, can have a chaotic behavior. This statement is supported with the circuital implementation of the circuit.

The past few decades have seen an explosion of satellite remote sensing techniques for the monitoring of volcanic thermal features. Here, we propose an artificial neural network approach for improving the cloud detection through imperfect multispectral satellite images analysis. The cloud detection algorithm has been tested on a data set of MSG-SEVIRI images acquired over the area of Etna volcano in Sicily (Italy) before and during the 2008 eruption. Results show that this approach is robust in terms of percentage of correctly classified pixels.

Although the Very Low-Frequency (VLF) waveforms have limited practical applications in acoustics (sonar) and secure military communications with radars and submarines; to this end; this paper presents a new and simple analytical model of VLF monostatic direct chaotic radar system. The model hypothetically depends on the two identical coupled time-delayed feedback chaotic systems which can generate and recover a long-wave chaotic signal. To resist the influence of positive Lyapunov exponents of the time-delay chaotic systems, the complete replacement of Pecaro and Carroll (PC) synchronization is employed. It can faithfully recover the chaotic signal from the back-scattered (echo) signal from the target over a noisy channel. The system performance is characterized in terms of the time series of synchronization in addition to the peak of the cross-correlation. Simulation results are conducted for substantial sensitivities of the chaotic signal to the system parameters and initial conditions. As a result, it is found that an effective and robust chaotic radar (CRADAR) model can be obtained when the signal-to-noise ratio (SNR) highly degrades to 0 dB, but with clear peak in correlation performance for detecting the target. Then, the model can be considered as a state of the art towards counter stealth technology and might be developed for other acoustic secure applications.

Although Electromyography (EMG) signals are sources of neural information that are essential in controlling the prosthetic hand, many confounding factors caused the variation of EMG signals properties over time. These factors degraded the performance of myoelectric prosthesis and made it unstable over time, across subjects and sessions such as stress, fatigue, muscular dystrophy, shifting electrodes locations, etc. The spatial information of muscle activity can be augmented using high-density surface electromyography (HD-sEMG) electrodes technology. In this study, HD-sEMG electrodes technology are integrated with the robust hybrid spatial features to improve the performance of myoelectric prostheses towards the non-stationary characteristics of EMG signals over time and across sessions. Three types of spatial feature sets are proposed using histogram oriented gradient (HOG) algorithm and intensity features. Three sub databases are used for evaluating the SVM classifier based on the proposed features. Intra-session and inter-session evaluation in offline manner show the potential of the proposed feature sets to improve the classification performance. The classification performance based on hybrid spatial features achieved precision of 97.9 %, sensitivity of 97.5 % for intra-session evaluation and a classification accuracy about 92.18 % for inter-session evaluation. Online classification results exhibit the robustness of hybrid spatial features (i.e. it achieved a classification accuracy based on hybrid spatial features of 92 % for intra-session evaluation and 89.9 % between sessions). Further, reducing the sampling rate to a certain extent without affecting the classification accuracy indicates the robustness and reliability of the proposed features. The results confirm that the robust spatial features have a significant effect on the classification accuracy more than that of the classifier algorithm.

In this paper, positive-real systems under lossless positive-real transformations are investigated. Let G(s) be the transfer function matrix of a continuous-time positive-real system of order n and F(s) a lossless transfer function of order nF. We prove here that the lossless positive-real transformed system, i.e. G(F(s)), is also positive-real. Furthermore, the stochastic balanced representation of positive-real systems under lossless positive-real transformations is considered. In particular, it is proved that the positive-real characteristic values πj of G(F(s)) are the same of G(s) each with multiplicity nF, independently from the choice of F(s). This property is exploited in the design of reduced order models based on stochastic balancing. Finally, the proposed technique is a passivity preserving model order reduction method, since it is proven that the reduced order model of G(F(s)) is still positive-real. An error bound for truncation related to the invariants πj is also derived.