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Ionic Polymer-Metal Composites (IPMCs) are electro-active polymers transforming mechanical forces into electric signals and vice versa. This paper proposes an improved electro-mechanical grey-box model for IPMC membrane working as actuator. In particular the IPMC nonlinearity has been characterized through experimentation and included within the electric model. Moreover identification of the model parameters has been performed via optimization algorithms using both single- and multi-objective formulation. Minimization was attained via the Nelder-Mead simplex and the Genetic Algorithms considering as cost functions the error between the experimental and modeled absorbed current and the error between experimental and modeled displacement. The obtained results for the different formulations have been then compared.

Ionic polymer-polymer composites (IP2Cs) are electroactive polymers which can be used both as sensors and as actuators. In this paper, a new multiphysics model of IP2Cs working as an actuator is presented and implemented using a finite element methods solver (COMSOL Multiphysics). The model involves electrical, mechanical, chemical, and thermal effects and yields a unique solution. Knowledge acquired by measuring campaigns has been included in the model. More specifically the frequency dependence of Young's modulus was experimentally determined and introduced in such a model. A frequency-domain investigation is performed and a model optimization procedure that integrates the Nelder-Mead simplex method with the COMSOL Multiphysics models is exploited to identify IP2C model parameter by fitting experimental data. A fractional order dynamics has been identified in the model, confirming previous studies on IPMC gray box modeling and on electroactive polymeric devices.

1Department of Electrical, Electronics and Computer Engineering, University of Catania, Viale A. Doria 6, 95125 Catania, Italy 2ISEP-Institute of Engineering, Polytechnic of Porto, Department of Electrical Engineering, Rua Dr. Antonio Bernardino de Almeida 431, 4200-072 Porto, Portugal 3Department of Mathematical Analysis, University of La Laguna, C/Astr. Francisco Sanchez s/n, La Laguna, 38271 Tenerife, Spain

The street light system, using a Power Line network, requires detailed knowledge about transmission medium, such as: transfer function, parasitic capacity, induced inductances and noise due to capacitive coupling between phases, in order to undertake a reliable master/slave communication. This paper proposes an innovative algorithm useful for a network dynamic configuration. It will allow to reduce impact noise, facing problems related to the attenuation, increasing with frequency and distance square, and cross coupling between phases, making network communication between master and slave nodes (street lights) reliable and more noise immune.

Modern aerospace vehicles are expected to have non-conventional flight envelopes and, in order to operate in uncertain environments, they must guarantee a high level of robustness and adaptability. A Neural Networks (NN) controller, with real-time learning capability, can be used in applications with manned or unmanned aerial vehicles. In this paper we propose a realtime system, based on a NN model, in order to control the trajectories of a hexacopter. The paper shows a performance evaluation, through a real experimental testbed, of the proposed approach in terms of error measures and obtained coordinates of the hexacopter.

In this paper an integrated circuit (IC) design of the fractional order proportional-integral-derivative (PID) controller is proposed. The development of the IC device is realized in Cadence environment, using the switched capacitors (SC) technology in order to reduce the area on the silicon wafer and to improve the electrical controllability. In order to obtain transfer functions that describe the fractional order of the differ-integral operator it is necessary to use interpolation methods, in particular, the choice in this work has fallen on the Oustaloup interpolation. This procedure is aimed at implementing a series of pole-zero blocks that approximate the non-integer order. The realized approach is able to guarantee a good approximation of the fractional order PID and simultaneously propose a detailed circuit analysis of the influence of the non-idealities, in particular the phenomenon of warping. This takes into account the distortion introduced by the s-domain to the z-domain transition, acting on the positions of poles and zeros, especially those at a higher frequency. Time and frequency domain result tests confirm the feasibility and reliability of the SC implementation.

IPMCs are electroactive polymers which can be used both as sensors and as actuators. The modeling of IPMC transducers is an open issue relevant to the development of effective applications. A multiphysics model of IPMC actuators is here implemented. It integrates the description of the electrical, mechanical, chemical and thermal coupled physics domains in a unique solution and, as a novelty, it allows the study in the frequency domain and the comparison with experimental response of the IPMC device. The IPMC white box modeling requires several macro- and microscopic parameters, not always accessible via theoretical approaches or experimentation. This work presents a new model optimization procedure which integrates the Nelder–Mead simplex method with the COMSOL Multiphysics®models. The proposed procedure uses experimental data and fits model simulations to IPMC real behavior for microscopic parameters’ identification. The model is developed for IPMCs with ethylene glycol as the solvent.

The paper proposes a new procedure which allows to define the parameters of a Fractional Order Proportional Integrative Derivative (FO-PID) controller that stabilizes a first-order plant with time-delay. The stabilizing FO-PID parameters complete set has been determined by the application of the Hermite–Biehler theorem to fractional order systems. The proposed procedure has been verified by computer simulations that confirm the effectiveness of the approach. The diffused PID controller industrial use and the verified capability of their non-integer order form justifies the timely FO-PID controller interest.

Fractional-order differential equations are interesting for their applications in the construction of mathematical models in finance, materials science or diffusion. In this paper, an application of a well known transformation technique, Differential Transform Method (DTM), to the area of fractional differential equation is employed for calculating Lyapunov exponents of fractional order systems. It is known that the Lyapunov exponents, first introduced by Oseledec, play a crucial role in characterizing the behaviour of dynamical systems. They can be used to analyze the sensitive dependence on initial conditions and the presence of chaotic attractors. The results reveal that the proposed method is very effective and simple and leads to accurate, approximately convergent solutions.

IPMC actuators suffer because of a large number of influencing factors that do not allow adequate open loop working conditions and they require the use of controlling strategies. IPMC controllers can be designed by using suitable device models. Here a non integer order transfer function is used to model IPMC actuators. In the present paper the IPMC model is scaled as a function of the actuator length and the control law has been parameterized according to this physical parameter

IPMCs are electroactive polymers which can be used both as sensors and as actuators. A multiphysics model of IPMC devices working as actuators is here presented. The model integrates electrical, mechanical, chemical and thermal effects in a unique solution and attention is focused on the coupling factors among the physics domains. The model was validated through experimentation.

IPMCs are a novel class of composite materials capable of electromechanical reversible transduction. It has been suggested in literature that they can be modeled by using fractional transfer functions. In this paper a gray box approach is used to identify IPMC actuators. Moreover the proposed transfer functions are ruled by using the device geometrical parameters, in such a away to guarantee the control of the transducer behavior during the design phase. Experimental validation of the proposed approach is reported.

An FPGA based system has been developed in order to characterize, in real time, Electrocardiographic (ECG) signals. These signals are analyzed by using nonlinear data analysis to extract the d ∞ parameter, characterizing their asymptotic chaotic behaviour. The FPGA implemented algorithm has been applied on a statistically significant number of ECG signals taken from the MIT-BIH database and including normal subjects and subjects affected by arrhythmia and ventricular arrhythmia. The real time feature of the proposed approach allows a clinical applications of such methodology in the early diagnosis of cardiac pathologies.

Many researches have highlighted the influence of climate on mortality, showing a high increase in mortality in the summer time during “heat waves”, periods of very high temperature and humidity levels. The bioclimatic indexes are used in urban climate studies to describe the level of thermal sensation that a person experiences due to the modified climatic conditions of an urban area. The index provides a meaningful and realistic indicator that can not only be used as an information as to how hot it feels, but also as a readily identifiable warning for individuals subject to the physiological dangers of heat exposure. The authors have developed a methodology that, by means of the Neural Network (NN), permits one to predict the values of meteorological data and then the calculation of the bioclimatic indexes. The meteorological data required for the calculation of the bioclimatic index concerning hourly values of air temperature, relative humidity, wind speed have been used according to the records of the meteorological station of the Pergusa Lake (EN), for the year from 2003 to 2006 NN-estimated, the bioclimatic indexes values were compared with coincident bioclimatic indexes values obtained from air temperature and relative humidity observations recorded at standard meteorological stations. Statistical analysis showed a good agreement between the NN-estimated and the station-observed bioclimatic indexes values, with a root mean square error (RMSE) < 1%.

In this chapter a Switched Capacitors (SC) implementation of fractional differintegral operator is proposed. Time and frequency domain result tests validate the feasibility and reliability of the SC circuit implementation. A detailed analysis of the influence of the non-idealities is proposed in order to obtain a design ready for Integrated Circuit (IC) implementation. The proposed approach may guarantee a good degree of approximation of fractional differintegral operator and therefore the possibility to realize PI λ D μ controller IC based on switched capacitors technology.

A Switched Capacitors (SC) implementation of fractional integrator is proposed in this paper. Time and frequency domain result tests validate the feasibility and reliability of the SC circuit implementation. A detailed analysis of the influence of the non-idealities is proposed in order to obtain a design fit for Integrated Circuit (IC) implementation. The proposed approach may guarantee a good degree of approximation of the fractional differ-integral operator and therefore the possibility to realize a PIλDμ controller IC based on switched capacitors technology.

A direct method for computing the L∞ norm of a transfer matrix is presented. The proposed procedure allows the norm to be computed through the introduction of strategy to solve the problem numerically in terms of a generalized eigenvalue problem. At the same time, the procedure also makes it possible to determine certain particular frequencies where the singular values of the transfer matrix assume peculiar properties. Accuracy is guaranteed since it does not depend on searching strategies. The complete procedure does not involve complicated computational tools, thus resulting in numerical efficiency. Two examples are given showing the suitability of the proposed approach.

In this chapter a new model for Ionic Polymer Metal Composites (IPMC) actuators, based on non-integer order models is proposed. IPMCs are very interesting polymer because of their capability to transform electrical energy into mechanical energy and vice-versa, making them particularly attractive for possible applications in different fields, such as robotics, aerospace, biomedicine, etc. An experimental setup has been realized to study the IPMCs behavior and an algorithm has been developed in Matlab environment in order to identify a fractional order model of IPMC actuators.

A new strategy to realize an FPGA implementation of a soft sensor for an industrial process is proposed. The proposed approach is based on the integration on Field Programmable Gate Array (FPGA) of a neural networks. The proposed method has been applied to develop a soft sensor for the estimation of the freezing point of kerosene in an atmospheric distillation unit (topping) working in a refinery in Sicily, Italy.

In this paper the fractional order integrative operator s−m , where m is a real positive number, is approximated via a mathematical formula and then an hardware implementation of fractional integral operator is proposed using Field Programmable Gate Array (FPGA). Digital hardware implementation of fractional-order integral operator requires careful consideration of issue of system performance, hardware cost, and hardware speed. FPGA-based implementation are up to one hundred times faster than implementations based on micro-processors; this extra speed can be exploited to allow higher performance in terms of digital approximations of fractional-order systems.Copyright © 2009 by ASME

The paper presents an hardware realization of a self-tuning control system implemented on a development board, Field Programmable Gate Arrays (FPGAs) based, able to adapt the control rules for an uncertain and disturbance affected plant. In the paper the on-line estimation of the plant parameters is realized by applying the “Recursive Least Squares with exponential forgetting” method and the control law is designed by using the “Pole Placement” procedure. These algorithms require a greater computational load, justifying therefore the FPGA utilization, especially in the case of high speed variation of the plant parameters. In order to test the FPGA hardware implementation of Self-Tuning regulators the process is implemented on DSPACE and the parameter variations are produced via an Human Machine Interface (HMI) console. Besides, thanks to the reprogrammability of FPGAs, these devices allow the use of such adaptive control systems in hazardous area.Copyright © 2009 by ASME

A new strategy to realize an FPGA implementation of a soft sensor for an industrial process is proposed. In order to cope with the problem of small data sets in the identification of a non linear model the proposed approach is based on the integration of bootstrap re-sampling, noise injection and stacked neural networks (NNs), using the Principal Component Analysis (PCA). The aggregated final NN-PCA system has been implemented on Field Programmable Gate Array (FPGA). The proposed method has been applied to develop a soft sensor for the estimation of the freezing point of kerosene in an atmospheric distillation unit (topping) working in a refinery in Sicily, Italy.

The paper proposes a new model for IPMC actuators, based on non-integer order models. IPMCs are very interesting because of their capability to transform electrical energy into mechanical energy and vice-versa, making them particularly attractive for possible applications in different fields, such as robotics, aerospace, biomedicine, etc. A great interest has been devoted to non-integer, or fractional, order systems because they well model a lot of physical systems. An experimental setup has been realized to study the IPMCs behavior and an algorithm has been developed in Matlab Environment to identify a fractional order model of IPMC actuators.

In this paper a remote control equipment for monitoring and managing a street lighting system is presented. It is composed by a local control, realized by master boards located inside electrical panels and slave boards mounted on each lamppost, and by a remote control realized by a central unit for the remote communication with the local control system. The communication between master and slave boards is realized using power line modems, while the remote control central unit is connected to master boards via a GPRS-GSM communication. Master board allows to choice the electrical phase for the power line communication and to send the control commands to the slave boards on each lamppost. Slave boards allow to turn on/off the lamppost, to reduce power consumption using a device developed by ALBATROS Italia S.r.L., and to detect the lamp status, checking the current flow on the lamp itself.

Recently, a renewed interest has been devoted to noninteger, or fractional, order systems. This is due to the fact that they well model a lot of physical systems and can be usefully applied in the area of automatic control. On drawback, mainly faced in the area of control systems, is related to their practical realization, essentially due to their infinite dimension nature. In this paper, an analog implementation of a noninteger order PIλDμ controller by using field programmable analog array is proposed. The frequency analysis of the reported examples shows the feasibility and reliability of the proposed approach.

A novel approach for the nonlinear characterization of Electrocardiogram (ECG) signals has been developed. The new developed methodology is based on a numerical algorithm that extracts the value of dinfinity (d-infinite) characterizing the asymptotic chaotic behavior of a system. This algorithm also extracts a measure of the maximum Lyapunov exponent and it is applicable to time series where the knowledge of the system structure and laws is not necessary. In order to prove the significance of the extracted parameters, the presented algorithm was applied on a statistically significant number of ECG signals taken from the MIT-BIH database and including normal subjects and subjects affected by arrhythmia and ventricular arrhythmia. A systematic study, analyzing how dinfinity varies with initial condition was performed showing the sensitivity of such parameter to the initial conditions. Furthermore, two maps, one presenting the maximum Lyapunov exponent and the other the dinfinity versus a control parameter II, as a measure of the rate variation, were drawn using the parameters extracted by the experimental data. They clearly show three distinguishable zones where the normal subjects and the subjects affected by the two different pathologies can be mapped and discriminated. Concluding, the newly presented algorithm, thanks to its implementation features and its effectiveness, it lends itself to future real-time implementation for clinical application in the early diagnosis of cardiac pathologies.

This paper presents an hardware realization of a novel bio-inspired architecture with reconfiguration properties able to implement any logical function and to restore its own operativity when an error happens. Drawing inspiration from the biological systems, where the mechanisms of self-diagnosis and self-healing continuously operate for maintaining the functionality of the system, and exploiting the potentialities offered by the FPGAs, three important characteristics have been implemented in digital systems: self-replication, self-test and self-repair. Particularly, the paper describes the realization of the bio-inspired architecture on FPGA, focusing in detail the timing of the state machine of each cell, that manages the operation of the device and the properties of self-replication, self-test and self-repair.

In the paper a numerical approach to implement a fractional PID controller and some optimization routines are given. The novelty of the proposed algorithms consists in a new procedure to implement differentiation and integration with a limited samples storing. The introduced strategy will let to implement a fractional PID on microcontroller with limited storing capacity.Copyright © 2007 by ASME

A microcontroller based system has been developed in order to characterize, in real time, magnetoencephalographic (MEG) signals. The signal are analyzed by using nonlinear data analysis method to extract the dinfin parameter. The signals under consideration are acquired on the whole-head MEG acquisition system and are analyzed in order to characterize spatiotemporal pattern. The microcontroller based implementation represents a low cost stand-alone solution for the analysis of any physiological signals (MEG, EEG, ECG, etc.) suitable for first aid approaches and early diagnostics. The real time feature of the proposed approach could thus allow, in the future, a clinical applications of such methodology

The paper presents an approach for the automatic optimal state feedback controller design and its download on an 8-bit microcontroller. Once the system model has been defined, the proposed procedure makes it possible to find the optimal gains of a feedback state controller which are compatible with the integer representation of the microcontroller. A software tool has been developed in order to support the design, implementation and testing phases of the control system

In this paper a new type of PID controllers is introduced and some tuning properties are given. The novelty of the proposed controller consists in the extension of derivation and integration order from integer to non integer numbers. This approach provides a more flexible tuning strategy and therefore an easier achieving of control requirements with respects to classical one. Some examples are also reported to validate the introduced strategy. Moreover two approaches for controller implementation are proposed. The first one is based on digital circuits while the second proposes the analog approach.

This paper investigates several case studies in which noise signals or random sequences play an important role in the emergence of a self‐organized behaviour or a paradoxal behaviour. We considered the effects induced by using either a chaotic signal instead of noise or a chaotic sequence instead of a random one. We show that in many cases noise and chaos have similar effects and in some cases chaos work better than noise.

Complex systems made of nonlinear interacting cells are gaining ever and ever interest. An universal environment for the study these systems should provide to the user the possibility of defining the dynamics of the cells as well as the connections among them in a simple and flexible way. In this paper the main characteristics of E∧3, an universal emulator of complex systems, are discussed. This simulator is based on the paradigm of Cellular Nonlinear Network and makes use of parallel computing allowing to considerably extend the size of the simulated system. Each cell is conceived as a nth‐order dynamical system. Moreover, numerical results obtained by emulating a complex system made of a large number (more than 500000) of interacting chaotic units arranged in a 3D lattice are presented. These simulations show an emerging result: organized forms and structure arise from the dynamic evolution of such systems.

A CNN based circuit for chaotic signal applications in communication systems is proposed. An hyperchaotic Saito oscillator has been designed by using a configuration of cellular neural networks named state-controlled CNNs. A communication system, based on chaotic inverse system synchronization, is described and the results, relative to a prototype circuit realization, are given.

The choice of the optimal topology for a multilayer perceptron neural network is considered by using genetic algorithms (GAs). The proposed strategy is intended both to select the number of neurons in a structure with one hidden layer and to choose the number of layers into which a fixed number of neurons should be optimally arranged to solve a given problem. The proposed GA has shown its suitability in determining efficiently the optimal topology of a neural network. The procedure is not time consuming and is able to easily take into account all the constrains eventually included in the problem. >