Francesco Pierri

Adaptive Observer for Fault Diagnosis in Nonlinear Discrete-Time Systems

This paper deals with the problem of fault diagnosis (FD) for a class of nonlinear systems in the presence of actuator failures. The scheme is based on a discrete-time diagnostic observer which computes an estimate of the systems state. To cope with the uncertainties and discretization errors, a discrete-time adaptive law is developed, based on a parametric model of the uncertain terms. A stability proof is developed to prove the global exponential stability of the system in the absence of faults. The effectiveness of the proposed approach is experimentally tested on a case study developed for an industrial mechanical manipulator.

Control of quadrotor aerial vehicles equipped with a robotic arm

In this paper a novel hierarchical motion control scheme for quadrotor aerial vehicles equipped with a manipulator is proposed. The controller is organized into two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the top layer. A simulation case study is developed to demonstrate the effectiveness of the approach in the presence of disturbances and unmodeled dynamics.

Observer-based sensor fault detection and isolation for chemical batch reactors

In this paper a scheme for detection and isolation of sensor faults in chemical batch reactors is proposed. The scheme is based on a bank of two observers for residual generation which guarantees sensor fault detection and isolation in presence of external disturbances and model uncertainties. In the observers a Hog approach is adopted for the design of the gains, while the unknown dynamics of the reactor (i.e., the heat released by the reaction) are estimated by an on-line interpolator based on a radial basis functions (RBF) neural network. Finally, the estimates provided by the observers and the sensor measures are processed by a decision making system (DMS) that provides information about the faulty sensor and an healthy measure. In order to test the effectiveness of the proposed approach, a simulation case study is developed

Observer-Based Decentralized Fault Detection and Isolation Strategy for Networked Multirobot Systems

In this paper, we present a distributed fault detection and isolation (FDI) strategy for a team of networked robots that builds on a distributed controller-observer schema. Remarkably different from other works in literature, the proposed FDI approach makes each robot of the team able to detect and isolate faults occurring on other robots, even if they are not direct neighbors. By means of a local observer, each robot can estimate the overall state of the team and it can use such an estimate to compute its local control input to achieve global tasks. The same information used by the local observers is also used to compute residual vectors, whose aim is to allow the detection and the isolation of actuator faults occurring on any robot of the team. Adaptive thresholds are derived based on the dynamics of the residual vectors by considering the presence of nonzero initial observer estimation errors, and noise terms affecting state measurement and model dynamics. The approach is validated via both numerical simulations and experiments involving four Khepera III mobile robots.

Discrete-Time Framework for Fault Diagnosis in Robotic Manipulators

In this paper, a discrete-time framework for diagnosis of faults of joint sensors, wrist-mounted force/torque sensors, and actuators of robotic manipulators is devised. It is assumed that redundant joint sensor measurements are available. Sensor measurements, together with the estimates computed by two isolation observers, are processed by a decision-making system, providing detection and isolation of the faults of the joint sensors as well as healthy measurements. Then, healthy measurements are used to feed a bank of diagnostic observers aimed at detecting, isolating, and identifying faults of joint actuators and force/torque sensors. The framework is experimentally tested on a cooperative industrial setup, composed of two industrial robots with six degrees of freedom performing a cooperative task.

Adaptive control for UAVs equipped with a robotic arm

Abstract This paper deals with the trajectory tracking control for quadrotor aerial vehicles equipped with a robotic manipulator. The proposed approach is based on a two-layer controller: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables while in the bottom layer, an adaptive motion control algorithm is in charge of tracking the motion references. A stability analysis of the closed-loop system is developed. Finally, a simulation case study is presented to prove the effectiveness of the approach.

Grasp planning and parallel control of a redundant dual-arm/hand manipulation system

SUMMARY In this paper, a kinematic model of a dual-arm/hand robotic system is derived, which allows the computation of the object position and orientation from the joint variables of each arm and each finger as well as from a suitable set of contact variables. On the basis of this model, a motion planner is designed, where the kinematic redundancy of the system is exploited to satisfy some secondary tasks aimed at ensuring grasp stability and manipulation dexterity without violating physical constraints. To this purpose, a prioritized task sequencing with smooth transitions between tasks is adopted. Afterwards, a controller is designed so as to execute the motion references provided by the planner and, at the same time, achieve a desired contact force exerted by each finger on the grasped object. To this end, a parallel position/force control is considered. A simulation case study has been developed by using the dynamic simulator GRASPIT!, which has been suitably adapted and redistributed.

A neural network approach for on-line fault detection of nitrogen sensors in alternated active sludge treatment plants

In this paper, an effective strategy for fault detection of nitrogen sensors in alternated active sludge treatment plants is proposed and tested on a simulated set-up. It is based on two predictive neural networks, which are trained using a historical set of data collected during fault-free operation of a wastewater treatment plant and their ability to predict reduced (ammonium) and oxidized (nitrates and nitrites) nitrogen is tested. The neural networks are also characterized by good generalization ability and robustness with respect to the influent variability with time and weather conditions. Then, simulations have been carried out imposing different kinds of fault on both sensors, as isolated spikes, abrupt bias and increased noise. Processing of residuals, based on the difference between measured concentration values and neural networks predictions, allows a quick revealing of the fault as well as the isolation of the corrupted sensor.

6D physical interaction with a fully actuated aerial robot

This paper presents the design, control, and experimental validation of a novel fully-actuated aerial robot for physically interactive tasks, named Tilt-Hex. We show how the Tilt-Hex, a tilted-propeller hexarotor is able to control the full pose (position and orientation independently) using a geometric control, and to exert a full-wrench (force and torque independently) with a rigidly attached end-effector using an admittance control paradigm. An outer loop control governs the desired admittance behavior and an inner loop based on geometric control ensures pose tracking. The interaction forces are estimated by a momentum based observer. Control and observation are made possible by a precise control and measurement of the speed of each propeller. An extensive experimental campaign shows that the Tilt-Hex is able to outperform the classical underactuated multi-rotors in terms of stability, accuracy and dexterity and represent one of the best choice at date for tasks requiring aerial physical interaction.

Experiments on coordinated motion of aerial robotic manipulators

In this paper a three layer control architecture for multiple aerial robotic manipulators is presented. The top layer, on the basis of the desired mission, determines the end-effector desired trajectory for each manipulator, while the middle layer is in charge of computing the motion references in order to track such end-effectors trajectories coming from the upper layer. Finally the bottom layer is a low level motion controller, which tracks the motion references. The overall mission is decomposed in a set of elementary behaviors which are combined together, through the Null Space-based Behavioral (NSB) approach, into more complex compounds behaviors. The proposed framework has been tested conducting an experimental campaign.