P. Giannone

Bio: P. Giannone is an academic researcher from University of Catania. The author has contributed to research in topics: Ionic polymer–metal composites & Actuator. The author has an hindex of 15, co-authored 39 publications receiving 1281 citations.

A nonlinear model for ionic polymer metal composites as actuators

Abstract: This paper introduces a comprehensive nonlinear dynamic model of motion actuators based on ionic polymer metal composites (IPMCs) working in air. Significant quantities ruling the acting properties of IPMC-based actuators are taken into account. The model is organized as follows. As a first step, the dependence of the IPMC absorbed current on the voltage applied across its thickness is taken into account; a nonlinear circuit model is proposed to describe this relationship. In a second step the transduction of the absorbed current into the IPMC mechanical reaction is modelled. The model resulting from the cascade of both the electrical and the electromechanical stages represents a novel contribution in the field of IPMCs, capable of describing the electromechanical behaviour of these materials and predicting relevant quantities in a large range of applied signals. The effect of actuator scaling is also investigated, giving interesting support to the activities involved in the design of actuating devices based on these novel materials. Evidence of the excellent agreement between the estimations obtained by using the proposed model and experimental signals is given.

A model for ionic polymer metal composites as sensors

Abstract: This paper introduces a comprehensive model of sensors based on ionic polymer metal composites (IPMCs) working in air. Significant quantities ruling the sensing properties of IPMC-based sensors are taken into account and the dynamics of the sensors are modelled. A large amount of experimental evidence is given for the excellent agreement between estimations obtained using the proposed model and the observed signals. Furthermore, the effect of sensor scaling is investigated, giving interesting support to the activities involved in the design of sensing devices based on these novel materials. We observed that the need for a wet environment is not a key issue for IPMC-based sensors to work well. This fact allows us to put IPMC-based sensors in a totally different light to the corresponding actuators, showing that sensors do not suffer from the same drawbacks.

A Tactile Sensor for Biomedical Applications Based on IPMCs

Abstract: In this paper, a first prototype of a multifunctional tactile sensor using ionic polymer metal composites (IPMCs) is proposed, designed, and tested. Two IPMC strips are used, one as an actuator and one as a sensor, both positioned in a cantilever configuration; working together they enable the system to detect the presence of a material in contact with it and to measure its stiffness. These sensing capabilities can be exploited in various biomedical applications, such as catheterism, laparoscopy and the surgical resection of tumors. Moreover, the simple structure of the proposed tactile sensor can easily be extended to devices in which a sensing tip for exploration of the surrounding environment is required. Compared with other similar tools, the one proposed works with a very low-power supply (the order of magnitude being a few volts), it needs very simple electronics, it is very lightweight and has a low cost.

Characterization of the harvesting capabilities of an ionic polymer metal composite device

Abstract: Harvesting systems capable of transforming dusty environmental energy into electrical energy have aroused considerable interest in the last two decades Several research works have focused on the transformation of mechanical environmental vibrations into electrical energy Most of the research activity refers to classic piezoelectric ceramic materials, but more recently piezoelectric polymer materials have been considered In this paper, a novel point of view regarding harvesting systems is proposed: using ionic polymer metal composites (IPMCs) as generating materials The goal of this paper is the development of a model able to predict the energy harvesting capabilities of an IPMC material working in air The model is developed by using the vibration transmission theory of an Euler?Bernoulli cantilever IPMC beam The IPMC is considered to work in its linear elastic region with a viscous damping contribution ranging from 01 to 100?Hz An identification process based on experimental measurements performed on a Nafion? 117 membrane is used to estimate the material parameters The model validation shows a good agreement between simulated and experimental results The model is used to predict the optimal working region and the optimal geometrical parameters for the maximum power generation capacity of a specific membrane The model takes into account two restrictions The first is due to the beam theory, which imposes a maximum ratio of 05 between the cantilever width and length The second restriction is to force the cantilever to oscillate with a specific strain; in this paper a 03% strain is considered By considering these two assumptions as constraints on the model, it is seen that IPMC materials could be used as low-power generators in a low-frequency region The optimal dimensions for the Nafion? 117 membrane are length = ?12?cm and width = ?62?cm, and the electric power generation is 3?nW at a vibrating frequency of 709?rad?s?1 IPMC materials can sustain big yield strains, so by increasing the strain allowed on the material the power will increase dramatically, the expected values being up to a few microwatts

A method to characterize the deformation of an IPMC sensing membrane

Abstract: Ionic polymer metal composites (IPMCs) are emerging materials used to realize motion sensors and actuators. In the former case by bending an IPMC membrane a voltage output is obtained, while in the latter case a voltage input is able to cause the membrane to bend. Although a number of interesting properties have been described for such materials, few results have been obtained regarding their characterization. In this paper a system devoted to the characterization of IPMCs as motion sensors is presented. The system was built in order to study the IPMC reaction, in a large range of frequencies, to a mechanical bending stimulus, in a cantilever configuration. An IPMC strip acts as the movable capacitive plate between two fixed plates, inside a differential capacitive measuring system. The motion of the IPMC is transduced into the system output voltage, without any mechanical contact, while ad-hoc electronics detect the IPMC sensing output voltage. A description of the IPMC characterization system follows a brief introduction to IPMC behaviour as sensors and actuators. Then, steps devoted to evaluation of the relationship between the input displacement forced to the IPMC being tested and the corresponding IPMC voltage output are derived.

Fractional Order Systems: Modeling and Control Applications

Abstract: Fractional Order Systems Fractional Order PID Controller Chaotic Fractional Order Systems Field Programmable Gate Array, Microcontroller and Field Programmable Analog Array Implementation Switched Capacitor and Integrated Circuit Design Modeling of Ionic Polymeric Metal Composite

Deep Learning-Based Feature Representation and Its Application for Soft Sensor Modeling With Variable-Wise Weighted SAE

Abstract: In modern industrial processes, soft sensors have played an important role for effective process control, optimization, and monitoring. Feature representation is one of the core factors to construct accurate soft sensors. Recently, deep learning techniques have been developed for high-level abstract feature extraction in pattern recognition areas, which also have great potential for soft sensing applications. Hence, deep stacked autoencoder (SAE) is introduced for soft sensor in this paper. As for output prediction purpose, traditional deep learning algorithms cannot extract high-level output-related features. Thus, a novel variable-wise weighted stacked autoencoder (VW-SAE) is proposed for hierarchical output-related feature representation layer by layer. By correlation analysis with the output variable, important variables are identified from other ones in the input layer of each autoencoder. The variables are assigned with different weights accordingly. Then, variable-wise weighted autoencoders are designed and stacked to form deep networks. An industrial application shows that the proposed VW-SAE can give better prediction performance than the traditional multilayer neural networks and SAE.

Comparison of energy harvesting systems for wireless sensor networks

Abstract: Wireless sensor networks (WSNs) offer an attractive solution to many environmental, security, and process monitoring problems. However, one barrier to their fuller adoption is the need to supply electrical power over extended periods of time without the need for dedicated wiring. Energy harvesting provides a potential solution to this problem in many applications. This paper reviews the characteristics and energy requirements of typical sensor network nodes, assesses a range of potential ambient energy sources, and outlines the characteristics of a wide range of energy conversion devices. It then proposes a method to compare these diverse sources and conversion mechanisms in terms of their normalised power density.