Emanuele Lindo Secco

Control Design for Interval Type-2 Fuzzy Systems Under Imperfect Premise Matching

This paper focuses on designing interval type-2 (IT2) control for nonlinear systems subject to parameter uncertainties. To facilitate the stability analysis and control synthesis, an IT2 Takagi-Sugeno (T-S) fuzzy model is employed to represent the dynamics of nonlinear systems of which the parameter uncertainties are captured by IT2 membership functions characterized by the lower and upper membership functions. A novel IT2 fuzzy controller is proposed to perform the control process, where the membership functions and number of rules can be freely chosen and different from those of the IT2 T-S fuzzy model. Consequently, the IT2 fuzzy-model-based (FMB) control system is with imperfectly matched membership functions, which hinders the stability analysis. To relax the stability analysis for this class of IT2 FMB control systems, the information of footprint of uncertainties and the lower and upper membership functions are taken into account for the stability analysis. Based on the Lyapunov stability theory, some stability conditions in terms of linear matrix inequalities are obtained to determine the system stability and achieve the control design. Finally, simulation and experimental examples are provided to demonstrate the effectiveness and the merit of the proposed approach.

Principal components analysis based control of a multi-dof underactuated prosthetic hand

BackgroundFunctionality, controllability and cosmetics are the key issues to be addressed in order to accomplish a successful functional substitution of the human hand by means of a prosthesis. Not only the prosthesis should duplicate the human hand in shape, functionality, sensorization, perception and sense of body-belonging, but it should also be controlled as the natural one, in the most intuitive and undemanding way. At present, prosthetic hands are controlled by means of non-invasive interfaces based on electromyography (EMG). Driving a multi degrees of freedom (DoF) hand for achieving hand dexterity implies to selectively modulate many different EMG signals in order to make each joint move independently, and this could require significant cognitive effort to the user.MethodsA Principal Components Analysis (PCA) based algorithm is used to drive a 16 DoFs underactuated prosthetic hand prototype (called CyberHand) with a two dimensional control input, in order to perform the three prehensile forms mostly used in Activities of Daily Living (ADLs). Such Principal Components set has been derived directly from the artificial hand by collecting its sensory data while performing 50 different grasps, and subsequently used for control.ResultsTrials have shown that two independent input signals can be successfully used to control the posture of a real robotic hand and that correct grasps (in terms of involved fingers, stability and posture) may be achieved.ConclusionsThis work demonstrates the effectiveness of a bio-inspired system successfully conjugating the advantages of an underactuated, anthropomorphic hand with a PCA-based control strategy, and opens up promising possibilities for the development of an intuitively controllable hand prosthesis.

A Real-Time and Self-Calibrating Algorithm Based on Triaxial Accelerometer Signals for the Detection of Human Posture and Activity

Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a “reliability” value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2% accuracy when classifying both static and dynamic activities.

Heart Rate and Accelerometer Data Fusion for Activity Assessment of Rescuers During Emergency Interventions

The current state of the art in wearable electronics is the integration of very small devices into textile fabrics, the so-called ¿smart garment.¿ The ProeTEX project is one of many initiatives dedicated to the development of smart garments specifically designed for people who risk their lives in the line of duty such as fire fighters and Civil Protection rescuers. These garments have integrated multipurpose sensors that monitor their activities while in action. To this aim, we have developed an algorithm that combines both features extracted from the signal of a triaxial accelerometer and one ECG lead. Microprocessors integrated in the garments detect the signal magnitude area of inertial acceleration, step frequency, trunk inclination, heart rate (HR), and HR trend in real time. Given these inputs, a classifier assigns these signals to nine classes differentiating between certain physical activities (walking, running, moving on site), intensities (intense, mild, or at rest) and postures (lying down, standing up). Specific classes will be identified as dangerous to the rescuer during operation, such as, ¿subject motionless lying down¿ or ¿subject resting with abnormal HR.¿ Laboratory tests were carried out on seven healthy adult subjects with the collection of over 4.5 h of data. The results were very positive, achieving an overall classification accuracy of 88.8%.

A three-axial body force sensor for flexible manipulators

This paper introduces an optical based three axis force sensor which can be integrated with the robot arm of the EU project STIFF-FLOP (STIFFness controllable Flexible and Learnable Manipulator for Surgical Operations) in order to measure applied external forces. The structure of the STIFF-FLOP arm is free of metal components and electric circuits and, hence, is inherently safe near patients during surgical operations. In addition, this feature makes the performance of this sensing system immune against strong magnetic fields inside magnetic resonance (MR) imaging scanners. The hollow structure of the sensor allows the implementation of distributed actuation and sensing along the body of the manipulator. In this paper, we describe the design and calibration procedure of the proposed three axis optics-based force sensor. The experimental results confirm the effectiveness of our optical sensing approach and its applicability to determine the force and momentum components during the physical interaction of the robot arm with its environment.

Accurate Bolt Tightening using Model-Free Fuzzy Control for Wind Turbine Hub Bearing Assembly

In the modern wind turbine industry, one of the core processes is the assembly of the bolt-nut connections of the hub, which requires tightening bolts and nuts to obtain well-distributed clamping force all over the hub. This force deals with nonlinear uncertainties due to the mechanical properties and it depends on the final torque and relative angular position of the bolt/nut connection. This paper handles the control problem of automated bolt tightening processes. To develop a controller, the process is divided into four stages, according to the mechanical characteristics of the bolt/nut connection: a fuzzy logic controller (FLC) with expert knowledge of tightening process and error detection capability is proposed. For each one of the four stages, an individual FLC is designed to address the highly nonlinearity of the system and the error scenarios related to that stage, to promptly prevent and avoid mechanical damage. The FLC is implemented and real time executed on an industrial PC and finally validated. Experimental results show the performance of the controller to reach precise torque and angle levels as well as desired clamping force. The capability of error detection is also validated.

A continuum body force sensor designed for flexible surgical robotics devices

This paper presents a novel three-axis force sensor based on optical photo interrupters and integrated with the robot arm STIFF-FLOP (STIFFness controllable Flexible and Learnable Manipulator for Surgical Operations) to measure external interacting forces and torques. The ring-shape bio-compatible sensor presented here embeds the distributed actuation and sensing system of the STIFF-FLOP manipulator and is applicable to the geometry of its structure as well to the structure of any other similar soft robotic manipulator. Design and calibration procedures of the device are introduced: experimental results allow defining a stiffness sensor matrix for real-time estimation of force and torque components and confirm the usefulness of the proposed optical sensing approach.

Bio-inspired controller for a dexterous prosthetic hand based on principal components analysis

Controlling a dexterous myoelectric prosthetic hand with many degrees of freedom (DoFs) could be a very demanding task, which requires the amputee for high concentration and ability in modulating many different muscular contraction signals. In this work a new approach to multi-DoF control is proposed, which makes use of Principal Component Analysis (PCA) to reduce the DoFs space dimensionality and allow to drive a 15 DoFs hand by means of a 2 DoFs signal. This approach has been tested and properly adapted to work onto the underactuated robotic hand named CyberHand, using mouse cursor coordinates as input signals and a principal components (PCs) matrix taken from the literature. First trials show the feasibility of performing grasps using this method. Further tests with real EMG signals are foreseen.

Long-distance monitoring of physiological and environmental parameters for emergency operators

The recent disaster provoked by the earthquake in middle Italy has pointed out the need for minimizing risks endangering rescuers’ lives. An European Project called ProeTEX (Protection e-Textiles: MicroNanoStructured fiber systems for Emergency-Disaster Wear) aims at developing smart garments able to monitor physiological and environmental parameters of emergency operators. The goal is to realize a wearable system detecting health state parameters of the users (heart rate, breathing rate, body temperature, blood oxygen saturation, position, activity and posture) and environmental variables (external temperature, presence of toxic gases and heat flux passing through the garments) and remotely transmitting useful information to the operation manager. This work presents an overview of the main features of the second prototype realized by ProeTEX with particular emphasis to the sensor’s body network and the long distance transmission of signals.

A new approach of multi-d.o.f. prosthetic control

In this paper we propose a new method to enhance prosthetics functionality, that integrates concepts extracted from the neuroscience background with the technology mainly exploited in prosthetic control. This new method allows controlling multi-degrees of freedom (d.o.f.) prostheses with signals having few d.o.f.