—The problem of state estimation for nonlinear systems with unknown state or measurement delays i... more —The problem of state estimation for nonlinear systems with unknown state or measurement delays is still an open problem. In this paper we consider the case of measurement delay and propose an approach that combines a delay identifier with a suitable high-gain observer in order to achieve simultaneous estimation of state and delay. We provide sufficient conditions that guarantee the exponential convergence to zero of the errors, globally with respect to the system variables and locally with respect to the delay estimation. We validate the method through an example concerning population models.
ABSTRACT This paper presents a model-based control technique to provide the contribution of wind ... more ABSTRACT This paper presents a model-based control technique to provide the contribution of wind power generators to primary frequency regulation in electric power systems. Models of individual wind power generators and wind farm (WF) as a whole are presented and the proposed control strategy is detailed. It consists of a central controller, a central Kalman filter (KF), and some local KFs, one for each wind turbine. The central controller is disabled in normal operation conditions and its task is to set the power reference for each wind turbine, overwriting the local reference, when a disturbance occurs. Central KF is in charge of estimating the external load variation, while each local KF estimates wind speed and the wind turbines dynamical state. The key feature of this approach is that each wind turbine can react to grid disturbances in a different way, which depends on wind speed as seen by the wind turbine itself and by its dynamical conditions. Real wind data and a large WF connected to the grid in a dedicated simulation environment have been used to test the effectiveness of the proposed control strategy.
ABSTRACT This work proposes an innovative control technique for improving the contribution to the... more ABSTRACT This work proposes an innovative control technique for improving the contribution to the grid frequency regulation provided by a set of wind power generators belonging to a wind farm. Models of individual generators and of the conventional grid primary frequency control are developed and used for designing a model predictive controller. A proper estimation algorithm is also introduced in order to provide both the dynamical state of the wind turbines and the actual local wind conditions to the regulator. The availability of this data makes the control algorithm able to improve the participation of the whole wind farm to the frequency regulation by suitably coordinating and differentiating the contribution of the individual generators. The proposed strategy is tested on a large wind farm using a dedicated real-time/real-data simulation environment.
Chirp-pulse microwave computed tomography (CP-MCT) is a technique for imaging the distribution of... more Chirp-pulse microwave computed tomography (CP-MCT) is a technique for imaging the distribution of temperature variations inside biological tissues. Even if resolution and contrast are adequate to this purpose, a further improvement of image quality is desirable. In this paper, we discuss the blur of CP-MCT images and we propose a method for estimating the corresponding point spread function (PSF). To this purpose we use both a measured and a computed projection of a cylindrical phantom. We find a good agreement between the two cases. Finally the estimated PSF is used for deconvolving data corresponding to various kinds of cylindrical phantoms. We use an iterative nonlinear deconvolution method which assures nonnegative solutions and we demonstrate the improvement of image quality which can be obtained in such a way.
ABSTRACT In this paper it is studied the classical problem of target tracking by a new approach c... more ABSTRACT In this paper it is studied the classical problem of target tracking by a new approach consisting in the treatment of the classical nonlinear measurement process in a form amenable for polynomial �fitering without the need of the measure map linearization, as required by other standard sub-optimal algorithms. The main idea is to transfer the nonlinearity of the measure map into a modi�cation of the noise sequence distribution in a nongaussian white sequence. This is indeed the property required for Kalman fitering which, although non more optimal, remains to be the optimal linear fitering algorithm. Conditions for polynomial �ltering are also satis�fied, allowing to face the nongaussian nature of the modi�ed noise sequence. Simulations show high performances of the proposed algorithm.
ABSTRACT A new approach to the deconvolution and �ltering of 3-D microscopy images is introduced ... more ABSTRACT A new approach to the deconvolution and �ltering of 3-D microscopy images is introduced in this paper. A state-space representation of the image is derived according to the assumption that the whole image can be modelled by an ensemble of smooth 3-D Gaussian random fi�elds. Blurring and noise are then easily included in the representation. Making use of this model the image restoration is carried out by means of a Kalman-based minimum variance estimation algorithm. The reported simulation results show high performances of the proposed approach.
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
ABSTRACT In this paper the problem of output feedback stabilizability for a class of MIMO discret... more ABSTRACT In this paper the problem of output feedback stabilizability for a class of MIMO discrete-time nonlinear systems is considered. The proposed solution is based on an ad-hoc separation assumption that could be satisfied for the class of nonlinear systems characterized to have the full vector relative degree and the drift-observability properties. Numerical results for the simple academic case of pendulum show the effectiveness of this approach.
—The problem of state estimation for nonlinear systems with unknown state or measurement delays i... more —The problem of state estimation for nonlinear systems with unknown state or measurement delays is still an open problem. In this paper we consider the case of measurement delay and propose an approach that combines a delay identifier with a suitable high-gain observer in order to achieve simultaneous estimation of state and delay. We provide sufficient conditions that guarantee the exponential convergence to zero of the errors, globally with respect to the system variables and locally with respect to the delay estimation. We validate the method through an example concerning population models.
ABSTRACT This paper presents a model-based control technique to provide the contribution of wind ... more ABSTRACT This paper presents a model-based control technique to provide the contribution of wind power generators to primary frequency regulation in electric power systems. Models of individual wind power generators and wind farm (WF) as a whole are presented and the proposed control strategy is detailed. It consists of a central controller, a central Kalman filter (KF), and some local KFs, one for each wind turbine. The central controller is disabled in normal operation conditions and its task is to set the power reference for each wind turbine, overwriting the local reference, when a disturbance occurs. Central KF is in charge of estimating the external load variation, while each local KF estimates wind speed and the wind turbines dynamical state. The key feature of this approach is that each wind turbine can react to grid disturbances in a different way, which depends on wind speed as seen by the wind turbine itself and by its dynamical conditions. Real wind data and a large WF connected to the grid in a dedicated simulation environment have been used to test the effectiveness of the proposed control strategy.
ABSTRACT This work proposes an innovative control technique for improving the contribution to the... more ABSTRACT This work proposes an innovative control technique for improving the contribution to the grid frequency regulation provided by a set of wind power generators belonging to a wind farm. Models of individual generators and of the conventional grid primary frequency control are developed and used for designing a model predictive controller. A proper estimation algorithm is also introduced in order to provide both the dynamical state of the wind turbines and the actual local wind conditions to the regulator. The availability of this data makes the control algorithm able to improve the participation of the whole wind farm to the frequency regulation by suitably coordinating and differentiating the contribution of the individual generators. The proposed strategy is tested on a large wind farm using a dedicated real-time/real-data simulation environment.
Chirp-pulse microwave computed tomography (CP-MCT) is a technique for imaging the distribution of... more Chirp-pulse microwave computed tomography (CP-MCT) is a technique for imaging the distribution of temperature variations inside biological tissues. Even if resolution and contrast are adequate to this purpose, a further improvement of image quality is desirable. In this paper, we discuss the blur of CP-MCT images and we propose a method for estimating the corresponding point spread function (PSF). To this purpose we use both a measured and a computed projection of a cylindrical phantom. We find a good agreement between the two cases. Finally the estimated PSF is used for deconvolving data corresponding to various kinds of cylindrical phantoms. We use an iterative nonlinear deconvolution method which assures nonnegative solutions and we demonstrate the improvement of image quality which can be obtained in such a way.
ABSTRACT In this paper it is studied the classical problem of target tracking by a new approach c... more ABSTRACT In this paper it is studied the classical problem of target tracking by a new approach consisting in the treatment of the classical nonlinear measurement process in a form amenable for polynomial �fitering without the need of the measure map linearization, as required by other standard sub-optimal algorithms. The main idea is to transfer the nonlinearity of the measure map into a modi�cation of the noise sequence distribution in a nongaussian white sequence. This is indeed the property required for Kalman fitering which, although non more optimal, remains to be the optimal linear fitering algorithm. Conditions for polynomial �ltering are also satis�fied, allowing to face the nongaussian nature of the modi�ed noise sequence. Simulations show high performances of the proposed algorithm.
ABSTRACT A new approach to the deconvolution and �ltering of 3-D microscopy images is introduced ... more ABSTRACT A new approach to the deconvolution and �ltering of 3-D microscopy images is introduced in this paper. A state-space representation of the image is derived according to the assumption that the whole image can be modelled by an ensemble of smooth 3-D Gaussian random fi�elds. Blurring and noise are then easily included in the representation. Making use of this model the image restoration is carried out by means of a Kalman-based minimum variance estimation algorithm. The reported simulation results show high performances of the proposed approach.
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012
ABSTRACT In this paper the problem of output feedback stabilizability for a class of MIMO discret... more ABSTRACT In this paper the problem of output feedback stabilizability for a class of MIMO discrete-time nonlinear systems is considered. The proposed solution is based on an ad-hoc separation assumption that could be satisfied for the class of nonlinear systems characterized to have the full vector relative degree and the drift-observability properties. Numerical results for the simple academic case of pendulum show the effectiveness of this approach.
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Papers by Francesco Conte