A. Ficola

AUTONOMOUS AERIAL REFUELING FOR UAVS USING A COMBINED GPS-MACHINE VISION GUIDANCE

The most important factors affecting the performance of a control scheme for Autonomous Aerial Refueling (AAR) for UAVs are the magnitude of the wake effects from the Tanker and the accuracy of the measurements of the UAV-Tanker distance and attitude leading to the docking. The main objective of the effort described in this paper is the implementation of a detailed modeling and simulation environment for evaluating the AAR problem. In particular, a specific control scheme based on a sensor fusion between GPS- based and Machine Vision-based measurements is proposed. Furthermore, the iterative algorithm used for estimating the position of the optical markers has been modified to be robust to a loss of visibility by one or more optical markers during the docking sequence. The paper presents the results of a detailed analysis of the AAR under different scenarios.

Feature Matching Algorithms for Machine Vision Based Autonomous Aerial Refueling

In this paper a machine vision (MV) based system is proposed as distance estimation sensor to be employed by UAVs during the operations of autonomous aerial refueling. For studying this problem it was developed a simulator featuring a 3D virtual reality (VR) interface that generates the image stream of the AAR maneuver. The proposed MV algorithm performs specific tasks as image processing for features extraction, feature matching and pose estimation. The problem of tanker/UAV attitude estimation from images is investigated in two scenarios: with and without artificial markers installed on the tanker. Two feature matching algorithms are proposed and the performance of the optical feedback signal are analyzed and compared in closed loop simulations

A sliding mode controller for a two-joint robot with an elastic link

In this paper the problem of the motion control of a two-joint planar robot with a flexible link is considered. The model of the robot is developed by using a simplified finite element technique referring to the elastic link. The sliding mode technique is considered. Two sliding surfaces are defined, as many as the control inputs. The required measurements are the position and velocity of the two motors and some positions of the elastic link. The equivalent motion is described by a linear system and its stability can be proved considering its eigenvalues. From this inspection some necessary conditions on the choice of the sliding surfaces are derived. Simulation tests have been carried out for some reference trajectories; the tracking error is always very small; in any case the elastic link behaves as it were almost rigid notwithstanding its stiffness is very small.

Real-Time Evolutionary Algorithms for Constrained Predictive Control

In the last years, thanks to the great advancements in computing technology, Evolutionary Algorithms (EA) have been proposed with remarkable results as robust optimisation tools for the solution of complex real-time optimisation problems. In this chapter we review the most important results of our research studies concerning the design of EA-based schemes suitable for real-time optimisation problems for Nonlinear Model Based Predictive Control (MBPC). In the first part of the chapter it will be discussed some modifications of a standard EA in order to address some real-time implementation issues. The proposed extension concerns the adoption of a new realtime adaptive mutation range to generate smooth commands, and the adoption of an intermittent feedback to face the computational delay problem. It will be shown that the main advantage of the improved technique is that it allows an effective real-time implementation of the Evolutionary-MBPC with a limited computing power. The real-time feasibility of the proposed improved Evolutionary-MBPC will be demonstrated by showing the experimental results of the proposed method applied to the control of a laboratory flexible mechanical system characterized by fast dynamics and a very small structural damping. Later, the application of real-time EAs is exte nded to real-time motion planning problems for robotic systems with constraints either on input and state variables. Basing on a finite horizon prediction of the future evolution of the robot dynamics, the proposed EA-based device online preshapes the reference trajectory, minimizing a multi-objective cost function. The shaped reference is updated at discrete time intervals taking into account the full nonlinear robot dynamics, input and state constraints. A specialized Evolutionary Algorithm is employed as search tool for the online computation of a sub-optimal reference trajectory in the discretized space of the control alternatives. The effectiveness of the proposed method and the online computational burden are analyzed numerically in two significant robotic control problems.

Optimal operation of the leavening process for a bread-making industrial plant

In this work a model-based method is proposed for determining the optimal set points for the leavening process in a continuous bread-making industrial plant. The procedure is based on a model of the leavening process, which has been identified using Neural Networks and ARX models. A Genetic Algorithm has been used to derive the optimal production set points, taking into account economic constraints. Two modes of operation of the plant have been investigated and optimized results have been confirmed by experiments on an industrial plant.

An autonomous aerial vehicle for unmanned security and surveillance operations: design and test

This paper presents the development of an electrically powered unmanned aerial vehicle (UAV) with wingspan of 2.5 m. A flight control system is constructed using small and light-weight components. The logical interconnection and schematic layout of the AFC (Automatic Flight Control) are presented. The UAV prototype has been successfully tested carrying a high resolution camera, and was able to acquire the images and the video of the fly zone and transmit them back to the ground station.

Improving trajectory tracking by feedforward evolutionary multivariable constrained optimization

Improvements in positioning accuracy and reduction of trajectory tracking error in robotic systems require advanced control laws; these should take into account also multivariable concurrent specifications and be able to handle inputs and state constraints. In this work these requirements are considered exploiting a feedforward model-based predictive controller in which the control law is planned online on the basis of a multiobjective cost function, the minimization of which is executed by means of an evolutionary algorithm. The proposed scheme has been applied to an existing robust feedback control scheme, to achieve a more accurate trajectory tracking of the tip of a flexible link. Real time execution is possible; moreover, notwithstanding the stochastic inference engine of the evolutionary algorithms, the proposed scheme is sufficiently reliable, since it reveals a high degree of repeatability of the control signals.

An Approach to Fault Diagnosis for Non Linear Dynamic Systems Using Neural Networks

Abstract This paper proposes an approach to fault detection and identification (FDI) for non linear dynamic systems using neural networks. A fault is considered as a variation of physical parameters; therefore the FDI problem can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of operation of the plant. Under some assumptions, this can be efficiently accomplished by a combined use of a linear parameter estimator and a bank of neural classifiers. Each neural network is trained to perform the diagnosis in a certain working point of the plant; a supervisor is introduced to allow interpolation between the working points, which the system has been trained with. The FDI scheme has been tested by simulation on a non linear mechanical oscillator.

Predictive reference shaping for constrained robotic systems using evolutionary algorithms

This paper proposes a method for reducing the trajectory tracking errors of robotic systems in presence of input saturation and state constraints. Basing on a finite horizon prediction of the future evolution of the robot dynamics, the proposed device online preshapes the reference trajectory, minimizing a multi-objective cost function. The shaped reference is updated at discrete time intervals and is generated taking into account the full nonlinear robot dynamics, input and state constraints. A specialized Evolutionary Algorithm is employed as search tool for the online computation of a sub-optimal reference trajectory in the discretized space of the control alternatives. The effectiveness of the proposed method and the online computational burden are analyzed numerically in two significant robotic control problems; furthermore a comparison of the performance provided by this method and an iterative gradient-based algorithms are discussed.

ICP and CBF Regulation: A new Hypothesis to Explain the “Windkessel” Phenomenon

The brain tamponade represents the final condition of a progressive intracranial pressure (ICP) increase up to values close to arterial blood pressure (BP) producing a reverberating flow pattern in the cerebral arteries with no net flow. This finding implies intracranial volume changes, therefore a full application of the Monro-Kellie doctrine is impossible. To resolve this contradiction, in eight pigs a reversible condition of brain tamponade was produced by infusing saline into a cerebral ventricle. The following parameters were measured: BP in the common carotid artery, ICP by the same needle utilised for the infusion, arterial and venous blood flow velocity (BFV) at, respectively, internal carotid artery (ICA) and sagittal sinus (SS) site by ultrasound technique. When ICP approached carotid BP values, reverberating BFV waves both at ICA and SS site were simultaneously observed. The arterial and venous reverberating waves appeared to be almost exactly superimposable, with a delay of about 40 msec. This synchronism between the pulsatile arterial and venous BFV indicates that the residual pulsation, still occurring at the arterial proximal level, is compensated by a passive compression-distension of the SS with no blood volume (that is net flow) crossing the intracranial vasculature.