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We introduce the motivation for the development of fractional-order elements (FOE) and fractional-order devices (FOD) derived from them. Short introductions to some of the material science and engineering applications are presented along with an introduction into the fractional calculus which has proven to be highly effective in modeling complex systems. We explore the questions of “why power–law” and “how is it useful”? Finally we introduce the concept of a fractional-order device and how it can be included in an electronic control circuit.

The paper presents the controller design of a inertial stabilization platform for optical devices in which a one axis gimbal system is used. The proposed approach is based on a Fractional Order Proportional Integral Derivative (FOPID) controller, whose parameters are designed by using genetic algorithms optimization. The controller performance has been compared with those of a standard PID controller.

A new strategy to improve generalization capabiliy of data-driven soft sensor for industrial processes is proposed in the paper. The method is useful when a limited data set is available. The proposed approach is based on the integration of bootstrap resampling, noise injection and stacked neural networks. In the paper it 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.

Digital realization of non-integer-order controllers is important to exploit the benefits provided by these controllers, in terms of flexibility, dynamic performance and robust stability, for applications in mechatronics, industrial and automotive systems. To realize infinite-dimensional fractional-order operators and controllers in the digital domain, a discrete-time approximation is necessary that must be characterized by stable and minimum-phase properties for control purposes. This paper provides a design method useful for a wide class of plants and applies a consolidated approximation technique. Moreover, the practical implementation problems of digital non-integer control algorithms are deeply analyzed by considering the effects of the sampling period, of the conversion between analog and digital domain (and vice versa) and the associated quantization. Results show benefits and limitations of the approach.

This paper describes a soft computing approach to control a semiactive sky-hook suspension system. The fuzzy controller has been optimized by means of a genetic algorithm and implemented using a dedicated hardware device. After a preliminary study on a quarter car, a seven degree-of-freedom (7-DOF) model was implemented and used for controller simulation and optimization. The proposed control system takes the vehicle heave into account, improving the opposing requirements of comfort and drivability. The following conditions were optimized: drivability over random test roads, sine-wave holes, and sleeper-plates at different speeds. Fuzzy control makes it possible to change the characteristic parameters of the sky-hook suspension in an optimal way according to the different road conditions. The number of fuzzy controller inputs is kept as small as possible, avoiding the use of expensive sensors that increase the overall cost of the system. The proposed system is also easily applicable to existing commercial suspension systems. In addition to the comfort features, the fuzzy controller allows a reduction in load fluctuation, abnormal behavior and tire consumption.

IPMCs (Ionic Polymer-Metal Composites) are electroactive polymers which can be used both as sensors and actuators. The dynamic behavior of IPMC actuators is affected by the relative humidity value. In this scenario there is a need to implement models that include the dependence of the dynamic IPMC behavior on environmental variables. In this work a multiphisics model of IPMC actuators is proposed that takes into account the dependence of the device transduction capabilities on the environmental relative humidity. Moreover the frequency domain investigation of the transducers is performed and a model optimization procedure is used to identify some IPMC model parameters by fitting experimental data. The proposed model is useful to understand the influence of relative humidity on the system performance and hence could be used to describe a IPMC vibrating devices as humidity sensors.

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.

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.

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.

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

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.

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.

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

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.

In this work a nonlinear dynamical model of a fuel cell stack is developed by means of artificial neural networks. The model presented is a black-box model, based on a set of easily measurable exogenous inputs like pressures and temperatures at the stack and is able to predict the output voltage of the fuel cell stack. The model obtained is being exploited as a component of complex control systems able to manage the energy flows between fuel cell stack, battery pack, auxiliary systems and electric engine in a zero-emission vehicle prototype

Presents some new trends for the control of climbing and walking robots (CLAWAR). In the first part some paradigms concerning the soft computing approach for the control of walking robots are illustrated. Then some considerations concerning the fuzzy control of the robot ROBINSPEC, the self-learning strategy adopted to analyze collective behaviour of the piezo light intelligent flea microrobots and the fuzzy control of the robot WIRE, are shown. In the third part of the paper the cellular neural network paradigm is used to generate and control motion in mechatronic devices. CNNs have been shown to be able to generate active nonlinear waves that can be used as the basis to generate and control motion in some devices reflecting architectural paradigm met in animals.

In this paper a new method for non-integer order integration is presented. It is based on a Multi-layer Perceptron modelization that allows an easy circuit design and a wide range of integration order. The neural network approach allows one to overcome some problems related to practical implementation of the circuit. An example of application regarding the control of a chaotic system is also reported in order to validate the proposed neural network modeling technique.

Cellular neural networks (CNNs) are widely used, both for image processing (a classical application) and for generation of locomotion patterns. In particular, a hexapod walking robot is presented. The locomotion patterns are generated by a reaction diffusion-CNN and a visual servoing approach exploiting an external camera is adopted to control the trajectory.

In the case study reported in this chapter, it will be evidenced how soft computing techniques, and in particular the integration of analytical and neural-type procedures, will allow us to model and control the complex systems encountered in the semiconductor industry.

Noninteger order circuits and systems main results on noninteger order systems approximation of noninteger order systems via integer order systems control problem - noninteger order feedback controllers noninteger order cellular neural network systems circuit design of noninteger order chaotic systems.

The methodology proposed in the paper deals with the use of artificial intelligence techniques in the modelling and control of climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity on such systems are, in fact, difficult to be modelled and controlled using traditional techniques. The paper proposes a framework for the development of fuzzy logic controllers in modern greenhouses.

The methodology proposed in the paper deals with the use of artificial intelligence techniques in the modelling and control of some climate variables within a greenhouse. The nonlinear physical phenomena governing the dynamics of temperature and humidity on such systems are, in fact, difficult to be modelled and controlled using traditional techniques. The paper proposes a framework for the development of fuzzy logic controllers (FLC) in modern greenhouses.