This paper proposes a neural network (NN) model reference adaptive system (MRAS) speed observer suited for linear induction motor (LIM) drives. The voltage and current flux models of the LIM in the stationary reference frame, taking into consideration the end effects, have been first deduced. Then, the induced part equations have been discretized and rearranged so as to be represented by a linear NN (ADALINE). On this basis, the transport layer security EXIN neuron has been used to compute online, in recursive form, the machine linear speed. The proposed NN MRAS observer has been tested experimentally on suitably developed test set-up. Its performance has been further compared to the classic MRAS and the sliding-mode MRAS speed observers developed for the rotating machines.

This paper proposes a neural based full-order Luenberger adaptive speed observer for sensorless linear induction motor (LIM) drives, where the linear speed is estimated with the total least squares (TLS) EXIN neuron. A novel state space-vector representation of the LIM has been deduced, taking into consideration its dynamic end effects. The state equations of the LIM have been rearranged into a matrix form to be solved, in terms of the LIM linear speed, by any least squares technique. The TLS EXIN neuron has been used to compute online, in recursive form, the machine linear speed. A new gain matrix choice of the Luenberger observer, specifically taking into consideration the LIM dynamic end effects, has been proposed, overcoming the limits of the gain matrix choice based on the rotating-induction-machine model. The proposed TLS full-order Luenberger adaptive speed observer has been tested experimentally on an experimental rig. Results have been compared with those achievable with the TLS EXIN MRAS, the classic MRAS, and the sliding-mode MRAS observers.

This paper presents a sensorless technique for permanent-magnet synchronous motors (PMSMs) based on high-frequency pulsating voltage injection. Starting from a speed estimation scheme well known in the literature, this paper proposes the adoption of a neural network (NN) based adaptive variable-band filter instead of a fixed-bandwidth filter, needed for catching the speed information from the sidebands of the stator current. The proposed NN filter is based on a linear NN adaptive linear neuron (ADALINE), trained with a classic least mean squares (LMS) algorithm, and is twice adaptive. From one side, it is adaptive in the sense that its weights are adapted online recursively. From another side, its bandwidth is made adaptive during the running of the drive, acting directly on the learning rate of the NN filter itself. The immediate consequence of adopting a variable-band structure is the possibility to enlarge significantly the working speed range of the sensorless drive, which can be increased by a factor of five. The proposed observer has been tested experimentally on a fractional horsepower PMSM drive and has been compared also with a fixed-bandwidth structure.

This paper proposes an Energy Management System (EMS) of the electrical system of a luxury ship (yacht). Starting from the analysis of the current configuration of the electric system, it proposes some solutions for its improvement, aiming at the reduction of the fuel consumptions of the Diesel Engine (DE) driven Permanent Magnet Synchronous Generator (PMSG) and therefore at the reduction of the related ship polluting emissions. From the architectural side, a DC electrical distribution on-board has been adopted in place of the existing AC. Moreover, the DC micro-grid has been integrated with Renewable Energy Sources (RES) based generators, as well as an energy storage system (ESS). From the management point of view, the proposed EMS governs the ship electric system in three working conditions: 1) regular operation, 2) DE driven PMSG fault/outage, 3) overload. During regular operation, the EMS implements the variable speed paradigm of the DE driven PMSGs on-board, permitting a reduction of the fuel consumptions and the related polluting emissions. In this phase, the ESS is exploited to minimize the speed and power transients of the DE. In case of a fault of one of the DE driven PMSGs, the ESS compensates the power missing for the entire duration of the cold start-up of the auxiliary generator. In case of big overload, the ESS provides the exceeding power with respect to the rated one, thus avoiding potential micro-grid instability problems. This paper proposes also a stability analysis of the entire on-board DC micro-grid.

This paper presents a closed-loop model reference adaptive system (CL-MRAS) speed observer developed for linear induction motor (LIM) drives. Starting from the structure of the CL-MRAS speed observer developed in the literature for rotating induction motors, a corresponding speed observer for LIMs has been developed here. It is based on the LIM dynamic model taking into consideration its dynamic end effects. In particular, the following aspects are original: 1) It employs the voltage and current models of the LIM considering its dynamic end effects, 2) it proposes a net thrust model including the braking force caused by the dynamic end effects; 3) it models the friction load of the LIM drive experimentally obtained with off-line tests. The proposed sensorless technique has been tested on a purposely developed experimental set-up. Results show that the CL-MRAS observer permits the drive to operate down to the speed of 0.01 m/s, i.e., about 0.15% of the rated speed, which is about 20 times lower than the minimum working speed achieved with other observers implemented on the same LIM drive, such as the total least squares (TLS) MRAS observer, the TLS EXIN full-order Luenberger observer, the extended Kalman filter, and, finally, the TLS EXIN Kalman filter.

This paper deals with a robust input-output feedback linearization control technique for induction motors. The idea arises from the observation that classic feedback linearization suffers primarily from two disadvantages: the accuracy of the dynamic model and the corresponding correct knowledge of the model parameters. To cope with the limit of the feedback linearization control, intrinsically connected to its model based nature, the linear controller has been substituted with a suitably designed robust controller. Such robust controllers has been designed with the aim of copying any variation of the main parameters of the induction motor, particularly those depending on the heating/cooling of the machine like stator and rotor resistances and magnetic saturation of the iron core. The proposed controller has been tested experimentally on a suitably designed test set-up.