Gianluca Ippoliti

A Quasi-Sliding Mode Approach for Robust Control and Speed Estimation of PM Synchronous Motors

This paper presents a discrete-time variable-structure-based control and a speed estimator designed for a permanent-magnet synchronous motor (PMSM). A cascade control scheme is proposed which provides accurate speed tracking performance. In this control scheme the speed estimator is a robust digital differentiator that provides the first derivative of the encoder position measurement. The analysis of the control stability is given and the ultimate boundedness of the speed tracking error is proved. The control scheme is experimentally tested on a commercial PMSM drive. Reported experimental evidence shows that the proposed solution produces good speed trajectory tracking performance and it is robust in the presence of disturbances affecting the system.

Neural Networks Based Control of Mobile Robots: Development and Experimental Validation

The paper proposes a neural networks approach to the solution of the tracking problem for mobile robots. Neural networks based controllers are investigated in order to exploit the nonlinear approximation capabilities of the nets for modeling the kinematic behavior of the vehicle and for reducing unmodeled tracking errors contributions. The training of the nets and the control performances analysis have been done in a real experimental setup. The proposed solutions are implemented on a PC-based control architecture for the real-time control of the LabMate mobile base and are compared with classical kinematic control schemes. Experimental results are satisfactory in terms of tracking errors and computational efforts.

Robust Control of Variable-Speed Wind Turbines Based on an Aerodynamic Torque Observer

This paper focuses on a robust power generation control strategy for a variable-speed wind energy conversion system based on a permanent magnet synchronous generator. The proposed control strategy combines a robust observer of the aerodynamic torque with a sliding mode-based field-oriented control strategy. The robust vanishing of the observation error and the tracking error is proved. Reported numerical simulations show that the proposed control policy is effective in terms of optimal power extraction and it is robust with respect to uncertainties affecting the system.

Minimal Resource Allocating Networks for Discrete Time Sliding Mode Control of Robotic Manipulators

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.

Fully sensorless robust control of variable-speed wind turbines for efficiency maximization.

Abstract This paper focuses on a sensorless robust power generation control strategy for a variable speed wind energy conversion system based on a permanent magnet synchronous generator. The proposed control strategy combines a robust observer of the aerodynamic torque, a simple technique for extracting rotor position using electrical signals, a robust observer of rotor speed, and a sliding mode based field oriented control strategy. The robust vanishing of the observation errors and tracking error is proved. Reported numerical simulations show that the proposed control policy is effective in terms of efficiency maximization and it is robust with respect to bounded parameter variations affecting the mechanical system.

Lyapunov-based switching control using neural networks for a remotely operated vehicle

This paper considers the tracking control problem of an underwater vehicle subjected to different load configurations, which from time to time introduce considerable variations of its mass and inertial parameters. The control of this kind of mode-switch process cannot be adequately faced with traditional adaptive control techniques because of the too long time needed for adaptation. To cope with this problem, a switching control scheme is proposed and the stability of this multi-controller system is analysed using the Lyapunov theory. The performance of the switched controller is evaluated by numerical simulations.

Fuzzy logic based economical analysis of photovoltaic energy management

Since 2002 the European Union has seen a rapid growth in the photovoltaic (PV) sector. During the last two years incentives for PV installations were cut almost worldwide slowing the growth of the market. In this scenario the design of a new plant ensuring economic convenience is strongly related to household electricity consumption patterns and energy management actions. This paper presents a high-resolution model of domestic electricity use based on Fuzzy Logic Inference System. Taking into account consumers sensibility concerning the rational use of energy, the model gives as output a 1-min resolution overall electricity usage pattern of the household. The focus of this work is the use of a novel fuzzy model combined with a cost benefits analysis to evaluate the real economic benefits of load shifting actions. A case study is presented to quantify its effectiveness in the new net metering Italian scenario.

Electric Motor Fault Detection and Diagnosis by Kernel Density Estimation and Kullback–Leibler Divergence Based on Stator Current Measurements

This paper deals with the problem of fault detection and diagnosis of induction motor based on motor current signature analysis. Principal component analysis is used to reduce the three-phase current space to a 2-D space. Kernel density estimation (KDE) is adopted to evaluate the probability density functions of each healthy and faulty motor, which can be used as features in order to identify each fault. Kullback-Leibler divergence is used as an index to identify the dissimilarity between two probability distributions, and it allows automatic fault identification. The aim is also to improve computational performance in order to apply online a monitoring system. KDE is improved by fast Gaussian transform and a points reduction procedure. Since these techniques achieve a remarkable computational cost reduction with respect to the standard KDE, the algorithm can be used online. Experiments are carried out using two alternate current motors: An asynchronous induction machine and a single-phase motor. The faults considered to test the developed algorithm are cracked rotor, out-of-tolerance geometry rotor, and backlash. Tests are carried out at different load and voltage levels to show the proposed method performance.

Localization of mobile robots: Development and comparative evaluation of algorithms based on odometric and inertial sensors

An autonomous mobile robot must be able to elaborate the measures provided by the sensor equipment to localize itself with respect to a coordinate system. The precision of the location estimate depends on the sensor accuracy and on the reliability of the measure processing algorithm. The purpose of this article is to propose a low cost positioning system using internal sensors like odometers and optical fiber gyroscopes. Three simple localization algorithms based on different sensor data processing procedures are presented. Two of them operate in a deterministic framework, the third operates in a stochastic framework where the uncertainty is induced by sensing and unmodeled robot dynamics. The performance of the proposed localization algorithms are tested through a wide set of laboratory experiments and compared in terms of localization accuracy and computational cost.

Sliding mode control based robust observer of aerodynamic torque for variable-speed wind turbines

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.