Andrea Perizzato

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Fault detection and isolation of bearings in a drive reducer of a hot steel rolling mill

Control Engineering Practice Volume June Pages Jun

Defective bearings are a major concern in rotating machinery. In this work we propose a two-step scheme, relying on two complementary data-driven techniques, for fault detection and isolation for a drive reducer in a hot steel rolling mill. A preliminary fault detection phase is based on a computationally lightweight time-domain multivariate statistical technique. Secondly, a more computationally intensive frequency-domain analysis method is used to confirm the fault detection and provide information on its frequency characteristics. Automatic procedures are sketched for the application of both techniques. Bearing defect models are employed to test their fault detection and isolation capabilities.

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Formation control and collision avoidance of unicycle robots with distributed predictive control

th IFAC Conference on Nonlinear Model Predictive Control NMPC Sep

In this paper we focus on formation control of a fleet of small-size unicycle vehicles with a distributed control scheme.We propose an approach based on a multi-level distributed predictive control (DPC) scheme, which also provides inter-robot and obstacle collision avoidance guarantees and, at the same time, requires the solution to a quadratic optimization problem, involving a limited computational burden. A real application example is illustrated, showing the effectiveness of the proposed control scheme.

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Production scheduling of parallel machines with model predictive control

Control Engineering Practice Volume September Pages Sep

This paper considers the problem of optimizing on-line the production scheduling of a multiple-line production plant composed of parallel equivalent machines which can operate at different speeds corresponding to different energy demands. The transportation lines may differ in length and the energy required to move the part to be processed along them is suitably considered in the computation of the overall energy consumption. The optimal control actions are recursively computed with Model Predictive Control aiming to limit the total energy consumption and maximize the overall production. Simulation results are reported to witness the potentialities of the approach in different scenarios.

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Application of distributed predictive control to motion and coordination problems for unicycle autonomous robots

Robotics and Autonomous Systems Volume October Pages Oct

This paper presents a Distributed Predictive Control (DPC) approach for the solution of a number of motion and coordination problems for autonomous robots. The proposed scheme is characterized by a multilayer structure: at the higher layer the reference trajectories of the robots are computed as the solution of suitable optimization problems. It is shown that, at this level, the definition of the cost function to be minimized allows to consider many different problems, such as formation control, coverage and optimal sensing, containment control, inter-robot and obstacle collision avoidance, and patrolling in an unknown environment. At the lower layers of the control structure, proper state and control reference trajectories are defined and

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Andrea Perizzato

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Behavioral traits