A finite control set model predictive control (FCS-MPC) strategy is investigated for the speed regulation of permanent magnet synchronous motors (PMSMs). A cascaded control structure is implemented, consisting of an inner FCS-MPC current loop, enhanced by a variable structure (VS) approach, and an outer discrete-time VS control (DTVSC) speed loop. The adoption of FCS-MPC for current regulation in industrial PMSMs is driven by its inherent fast dynamic response, ability to enforce constraints, and the potential for modulator-less operation. Furthermore, DTVSC has proven to be an effective PMSM control strategy, particularly valued for its robustness in the face of model inaccuracies and external disturbances. The proposed control methodology, compared to the industry-standard PI cascaded control implemented by Whirlpool in domestic appliances, provides an improvement in control performance and robustness characteristics.

This paper proposes a discrete-time sliding mode control (DTSMC) approach for controlling a permanent-magnet synchronous motor (PMSM). To ensure precise speed tracking, a cascade control strategy is introduced. The stability of the proposed control scheme is analyzed, and a formal proof of the ultimate boundedness of the speed tracking error is provided. The developed control methodology yields superior control performance and robustness characteristics relative to the industry-standard PI cascaded control employed by Whirlpool in domestic appliances. Experimental testing of the control scheme was also conducted using a commercial PMSM drive.

A geofence is a virtual perimeter representing the limits of a real-world operating area. The development of a control policy allowing to guarantee the safety of the Unmanned Aircraft Vehicles' (UAVs) users and stakeholders represents an important industrial world problem, yet studied in depth by the international scientific research community. In this paper, a geofencing system for UAVs based on the Model Predictive Control (MPC) paradigm is proposed. MPC permits to optimally drive dynamical systems explicitly imposing constraints on input and output by the prediction of the future evolution of the controlled plant. In this paper, an MPC policy is proposed to impose the geofencing area of a pre-compensated multi-rotor UAV. The proposed approach considers recomputing iteratively the controlled UAV speed constraints with respect to a prescribed maximum vehicle deceleration, in order to correctly impose the limits on vehicle speed and to stop the UAV on the borders of the prescribed geofence operating area. The proposed algorithm has been verified in simulation tests controlling a pre-compensated multi-rotor vehicle in a considered control scenario.

Nowadays many machines and robots are programmed to perform the same task repeatedly. The Iterative Learning Control (ILC) paradigm is based on the idea that the performance of a system that executes the same trial multiple times can be improved by learning from the previous iterations. The objective of ILC is to improve the batch process performance by incorporating past trials error information into the control reference signal for the subsequent iteration. The ILC algorithms are categorized with respect to the number of past iterations considered to compute the next control signal and the first order ILC includes those algorithms considering only information about the last trial. In this paper different first order ILC update laws have been considered and compared controlling a Single-Input Single-Output (SISO) micro-positioning piezostage system. The proposed comparison allows to evaluate the performance of different first order ILC algorithms tested on the considered real world case study.

This paper presents a Model Predictive Control (MPC) strategy for a triaxial piezoelectric actuators (PAs) system. PAs systems require appropriate controllers to guarantee fast and high-precision positioning performances avoiding effects of non-linearities. Typically, commercial systems provide integrated Proportional-Integral (PI) controllers guarantying to maintain system stability in the presence of uncertainty and disturbance. MPC owes its success to the ability of optimally regulate multivariable systems through the minimization of a Quadratic Programming (QP) problem subjected to prescribed constraints. Alternatively, unconstrained MPC eliminates constrains from the problem, reducing the number of operations elapsed to compute the solution. The aim of this work is to design an unconstrained MPC for a 3-DOF PA replacing PI controllers to improve control performances by a smaller increase of required computational effort. The system is described by a Multi-Input Multi-Output (MIMO) Linear Time-Invariant (LTI) model, experimentally identified by the open-loop real plant response. Effectiveness of the proposed method is validated by simulation tests and experiments on the real system, comparing MPC with PI controllers tuned to guarantee common PA stability requirements.

The paper focuses on variable-rotor-speed/variable-blade-pitch wind turbines operating in the region of high wind speeds, where control is aimed at limiting the turbine energy capture to the rated power value. A robust sliding mode approach is proposed, using the blade pitch as control input, in order to regulate the rotor speed to a fixed rated value, in the presence of uncertainties characterizing the wind turbine model. Closed loop convergence of the overall control system is proved. The proposed control solution has been validated on a 5-MW three-blade wind turbine using the National Renewable Energy Laboratory (NREL) wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. A comparison with the standard FAST baseline controller [1], [2] has been also included.

Piezoelectric drivers are widely used in nanoscale image acquisition systems. A source of performance degradation of these drivers is hysteresis, which introduces low rate noise and causes a slow continuous drift, decreasing the quality of scanned images. In this paper a sliding mode control policy, based on an estimator of the perturbation due to hysteresis, is applied to the control of a piezostage in a three dimensional images acquisition system, in order to improve control performances and final scanning results. The presented solution has been experimentally tested and compared with the Proportional-Integrative-Derivative (PID) controllers provided with the piezoelectric acquisition system, showing a noticeable improvement both in the piezostage behavior and in the images quality.

This paper focuses on the so-called sensorless speed-tracking control for permanent magnet synchronous motors. A control strategy is proposed allowing to extract rotor position using electrical signals and an observer of electrical variables, and to guarantee the robust asymptotical tracking of a reference speed without measuring rotor velocity and position. Bounded parameter variations are supposed to affect the mechanical system, and a (possibly time-varying) uncertain load torque is considered. Reported experimental evidence shows the effectiveness of the proposed DSP-based control policy.

In this paper the problem of icing detection is considered for wind turbines operating in medium speed wind region (Region 2) and subject to a control law tracking the Maximum Delivery Point of the power coefficient characteristic. Based on an observer of the rotor angular acceleration, rotor inertia is estimated in order to detect its eventual increase due to icing. Moreover, the observed value of rotor inertia can be potentially used for updating the controller parameters or to stop the turbine when icing is too severe. The proposed approach has been tested by intensive MatLab®simulations using the NREL 5 MW wind turbine model.

The paper proposes two different architectural solutions for a quasi-geostationary monostatic SAR, hosted on-board of a COMSAT satellite, aiming at monitoring water-vapor, for Numerical Weather Forecast on a sub-continental scale, and hazards like hydrogeological landslides and deformations on a local scale. The two architectures address a system that combines an L-band, wide coverage beam, for coarse resolution imaging, and an X-band, spot beam, for fine resolution imaging. The challenge is to allow the X-band spot to be positioned everywhere within the L-band coverage, that should be hold fixed, but exploiting the same wide reflector.

In this paper the control of a boost converter in the presence of an uncertain load resistance is addressed. Exploiting a suitable change of coordinates and pointing out the internal and the external dynamics of the converter, a sliding mode control law is considered for the regulation of the converter output voltage. A sliding mode observer is introduced, whose dynamics allows to estimate the unknown load resistance. The robust control algorithm is designed to actuate directly the switching device, thus eliminating the need for a waveform modulator. The overall control system has been tested in simulated scenario. The performances are evaluated comparing with a standard double loop control scheme for boost converter, with a linear controller in the external loop and a sliding mode controller in the internal loop. Simulation tests show the effectiveness of the proposed approach, which is superior to the standard controller.

In this paper a Power Factor Control (PFC) of an AC-DC boost converter operating in light load condition has been presented. A Passivity-Based current control able to operate in either Continuous Conduction Mode (CCM) and in Discontinuous Conduction Mode (DCM) has been presented. Cascaded control with outer PI voltage loop and intermediary input voltage feedforward has been implemented in order to increase Power Factor (PF). The proposed solution has been numerically tested using a powerful software simulation platform.

In this paper a speed and current control for Permanent Magnet Synchronous Motors (PMSMs) with incremental encoder sensor has been designed. A Model Predictive Control (MPC) technique for PMSM has been explored, considering an off-line explicit MPC solution resulting in a PieceWise Affine (PWA) controller formulation. Reported numerical results show that the proposed controller enhances the overall performances of common cascaded Field Oriented Control (FOC) in different scenarios. The proposed solution has been experimentally tested on a commercial PMSM equipped with a control system based on Digital Signal Processor (DSP).

This paper focuses on a robust power generation control strategy for a variable speed wind energy conversion system. The proposed control strategy, by a robust observer of the aerodynamic torque, produces the required generator electromagnetic torque. The robust ultimate boundedness of the observation error and the tracking error, with arbitrary precision, is proved. The proposed sliding mode control approach has been validated on a 1.5-MW three-blade wind turbine using the National Renewable Energy Laboratory (NREL) wind turbine simulator FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code. Reported numerical simulations show that the proposed control solution is effective in terms of optimal power extraction and it is robust with respect to disturbances affecting the system.

This paper presents a discrete-time sliding mode control based on neural networks designed for robotic manipulators. Radial basis function neural networks are used to learn about uncertainties affecting the system. The online learning algorithm combines the growing criterion and the pruning strategy of the minimal resource allocating network technique with an adaptive extended Kalman filter to update all the parameters of the networks. A method to improve the run-time performance for the real-time implementation of the learning algorithm has been considered. The analysis of the control stability is given and the controller is evaluated on the ERICC robot arm. Experiments show that the proposed controller produces good trajectory tracking performance and it is robust in the presence of model inaccuracies, disturbances and payload perturbations.

This paper presents a discrete-time sliding mode control based on prediction compensation of uncertainties for planar robotic manipulators. Autoregressive models, identified on-line by Kalman Filters, are used to learn about uncertainties affecting the system. The analysis of the control stability is given and the controller is evaluated on the ERICC robot arm. Experiments show that the proposed controller produces good trajectory tracking performance and it is robust in the presence of model inaccuracies.

The stabilization problem of a class of linear, discrete-time, time-varying, uncertain systems is considered. The elements of the dynamical matrix A(middot) of a given time-varying system are modelled as unknown time functions taking values inside known compact sets. The elements of A(middot) are varying inside a fixed set for a sufficiently long time interval, and, at some unknown time instants, they suddenly switch from the actual set to another one. The measure and the control matrices of the system are assumed to be known and time-invariant. It is required to find a dynamic output controller yielding an asymptotically internally stable closed-loop system. The solution proposed is given by the connection of a family of linear controllers with a supervised switching scheme