1,721,007 research outputs found
Condition monitoring of electric-cam mechanisms based on Model-of-Signals of the drive current higher order differences
Full text linkCondition monitoring of electric motor driven mechanisms is of great importance in industrial machines. The knowledge of the actual health state of such components permits to address maintenance policies which results in better exploitation of their actual operational life span and consequently in maintenance cost reduction. In this paper, we exploit the way electric cams are implemented on the vast majority of PLC/Motion controllers to develop a suitable condition monitoring procedure. This technique relies on computing the higher-order differences of the current absorbed by slave motors to get signals that do not depend on a priori knowledge of the cam trajectory and of the mechanism nominal model. Subsequently, we will use these data in the Model-of-Signals framework, to gather information on the mechanism’s health condition, which in turn can be used to perform predictive maintenance policies. The differenced signal is modelled as an ARMA process and the model capabilities in condition monitoring are then shown in simulation and experimental application. Besides, this framework allows exploiting the edge-computing capabilities of the machinery controllers by implementing recursive estimation algorithms
A Robust Sensorless Controller-Observer Strategy for PMSMs with Unknown Resistance and Mechanical Model
Full text linkIn this work, we present a mixed sensorless strategy for Permanent Magnet Synchronous Machines, combining a torque/current controller and an observer for position, speed, flux, and stator resistance. The proposed co-design is motivated by the need for an appropriate signal injection technique to guarantee full state observability. Neither the typical constant or slowly-varying speed assumptions, nor a priori mechanical model information are required. Instead, the rotor speed is modeled as an unknown input disturbance with constant (unknown) sign and uniformly non-zero magnitude. With the proposed architecture, we show that the torque tracking and signal injection tasks can be achieved and asymptotically decoupled. Because of these features, we refer to this strategy as a sensorless controller- observer with no mechanical model. Employing a gradient descent resistance/back-EMF estimation, combined with the unit circle formalism to describe the rotor position, we prove regional practical asymptotic stability of the overall scheme. In particular, the domain of attraction can be arbitrarily large, without including a lower-dimensional manifold. The effectiveness of this design is further validated with numerical simulations, related to a challenging application of UAV propellers control
A Semi-global Hybrid Sensorless Observer for Permanent Magnet Synchronous Machines with Unknown Mechanical Model
No full textIn this paper, we present a hybrid sensorless observer for Permanent Magnet Synchronous Machines, with no a priori knowledge of the mechanical dynamics and without the typical assumption of constant or slowly-varying speed. Instead, we impose the rotor speed to have a constant (unknown) sign and a non-zero magnitude at all times. For the design of the proposed scheme, we adopt meaningful Lie group formalism to describe the rotor position as an element of the unit circle. This choice, however, leads to a non-contractible state space, and therefore it introduces topological constraints that complicate the achievement of global/semi-global and robust results. In this respect, we show that the proposed observer, which augments a recent continuous-time solution, achieves semi-global practical asymptotic stability by periodically resetting the estimates. As highlighted in the simulation results, the novel hybrid strategy leads to improved transient performance, notably without any modification of the gains employed in the continuous-time version. These features motivate to augment the observer with a discrete-time identifier, leading to significantly faster rotor flux reconstruction
Constrained-Inversion MRAC: An Approach Combining Hard Constraints and Adaptation in Uncertain Nonlinear Systems”
No full textIn this work, we propose a design strategy for adaptive control of a class of nonlinear systems with input and state constraints. The systems of interest are required to have relative degree 1 and a convergent zero-dynamics: these properties cover a significant number of applications, after suitable changes of coordinates and with a proper selection of the regulated output. Through a design based on Barrier Lyapunov Functions, inspired by Explicit Reference Governors, we propose a feasible closed-form right-inverse unit that can be effectively interconnected with a nominal adaptive stabilizer, this way enforcing constraint satisfaction, while rejecting the effects of parametric uncertainties at the same time. The stabil- ity and feasibility properties of the control scheme are formally proven, and verified in a detailed numerical simulation
Condition Monitoring by Model-of-Signals: Application to gearbox lubrication
No full textIn this work, we make use of the Model-of-Signal technique to perform lubrication monitoring of a large industrial worm gear motor. We assume sensor measurements to be modelled by autoregressive processes and exploit the edge-computing capabilities of programmable logic controllers to perform the Recursive Least Squares algorithm to identify them. Then, we use those models to compute indicators able to diagnose the lubricant level within the gearbox and compare them to statistical indexes, which are traditionally used for monitoring. The aim of this application is to show how to build a condition monitoring infrastructure in an industrial environment able to detect possible occurring faults locally and acquire knowledge about them by exchanging information with external computers, paving the way towards Intelligent Maintenance Systems in Industry 4.0
A Hybrid Adaptation Strategy for Repetitive Control of an Uncertain Delay Lagrangian System
Full text linkIn this work, we present a novel repetitive control (RC) strategy to achieve accurate position tracking of a 1-DOF Lagrangian system. Such controller is able to cope with model uncertainties and unknown transmission delays in the control architecture. The classic repetitive structure is augmented with an observer of the residual disturbance, to be compensated by means of the RC action. The repetitive unit is updated at hybrid instants so that the disturbance observer is close to its steady-state before anew repetitive correction is applied. In addition, communication delay is also estimated by the proposed control structure. This way, practical asymptotic stability of the overall system can be achieved with a simple proportional correction of the RC, also under perturbations of the steady-state estimate due to model uncertainties. In light of the aforementioned properties, the proposed RC-based controller is shown to be an easy-to-tune, robust solution capable of improving the tracking performance for the given case of study
Identification of many-core systems-on-chip with input and output noises
No full textDynamic optimization of computing performance under thermal constraints is expected to be a cornerstone in next generation many-core systems-on-chip. Toward this goal,
compact, scalable and accurate thermal models are crucial.
In this work, a two-step identification procedure is presented to derive a set of local, yet interconnected, thermal models which are suitable for distributed control. The case of very noisy temperature measurement available on each core is considered, as it is the most commonly encountered in real-life multicore chips. The first identification step is based on a MISO Frisch scheme to deal with both input and output noises. Then, exploiting physical insight on the characteristics of measurement noises, a second ad-hoc procedure is proposed to refine the identified model. The proposed solution has been
successfully applied to an Intel’s Single-chip-Cloud-Computer (SCC), a prototype with 48 cores
Modeling and Control Design for Power Systems Driven by Battery/Supercapacitor Hybrid Energy Storage Devices
No full textThis paper addresses several important problems in a typical power system driven by battery/supercapacitor hybrid energy storage devices. The currents in the battery and the supercapacitor are actively controlled by two bidirectional buck-boost converters. Detailed investigation is conducted on deriving two state-space averaged models for the whole interconnected system. The control objective is to track the references of two variables. The control design problem is converted into a numerically efficient optimization problem with linear matrix inequality (LMI) constraints. When the optimization algorithm is applied to an experimental system, the resulting optimal control law is very close to a simple integrator control (with two inputs and two outputs). The simple integrator control is applied to the system in both simulation and experiment. The results confirm the effectiveness of the modeling and control design methods
SCC thermal model identification via advanced bias-compensated least-squares
No full textCompact thermal models and modeling strategies are today a cornerstone for advanced power management to counteract the emerging thermal crisis for many-core systems-on-chip. System identification techniques allow to extract models directly from the target device thermal response. Unfortunately, standard Least Squares techniques cannot effectively cope with both model approximation and measurement noise typical of real
systems. In this work, we present a novel distributed identification strategy capable of coping with real-life temperature sensor noise and effectively extracting a set of low-order predictive Thermal models for the tiles of Intel’s Single-chip-Cloud-Computer (SCC) many-core prototype
- …
