Leonardo Giovanini | Universidad Nacional del Litoral (original) (raw)

Papers by Leonardo Giovanini

Research paper thumbnail of Simultaneous moving horizon estimation and control for nonlinear systems subject to bounded disturbances

International Journal of Robust and Nonlinear Control

SummaryIn this work, we address the output–feedback control problem for nonlinear systems under b... more SummaryIn this work, we address the output–feedback control problem for nonlinear systems under bounded disturbances using a moving horizon approach. The controller is posed as an optimisation‐based problem that simultaneously estimates the state trajectory and computes future control inputs. It minimizes a criterion that involves finite backward and forward horizons with respect to the unknown initial state, measurement noises and control input variables. The main novelty of this work relies on linking the lengths of the forward and backward windows with the closed‐loop stability, assuming detectability and decoding sufficient conditions to assure system stabilizability. It leads to a formulation that does not require to be a Control Lyapunov Function for the terminal cost of the controller. Simulation examples are carried out to compare the performance of solving simultaneously and independently the estimation and control problems. Furthermore, the examples show how the controller...

Research paper thumbnail of Robust stability of moving horizon estimation for non‐linear systems with bounded disturbances using adaptive arrival cost

IET Control Theory & Applications, 2020

In this paper, the robust stability and convergence to the true state of moving horizon estimator... more In this paper, the robust stability and convergence to the true state of moving horizon estimator based on an adaptive arrival cost are established for nonlinear detectable systems. Robust global asymptotic stability is shown for the case of nonvanishing bounded disturbances whereas the convergence to the true state is proved for the case of vanishing disturbances. Several simulations were made in order to show the estimator behaviour under different operational conditions and to compare it with the state of the art estimation methods.

Research paper thumbnail of 0Distributed Model Predictive Control based on Dynamic Games

Model predictive control (MPC) is widely recognized as a high performance, yet practical, control... more Model predictive control (MPC) is widely recognized as a high performance, yet practical, control technology. This model-based control strategy solves at each sample a discrete-time optimal control problem over a finite horizon, producing a control input sequence. An at-tractive attribute of MPC technology is its ability to systematically account for system con-

Research paper thumbnail of MPC for nonlinear systems: A comparative review of discretization methods

2017 XVII Workshop on Information Processing and Control (RPIC), 2017

This work provides a comparative review of three different numerical methods generally used to di... more This work provides a comparative review of three different numerical methods generally used to discretize continuous-time non-linear equations appearing in model predictive control problems: direct multiple shooting, direct collocation and successive linearizations. An overview of the characteristics of each method is given and the performance of each method is evaluated through the simulation of two test cases.

Research paper thumbnail of Adaptive polytopic estimation for nonlinear systems under bounded disturbances using moving horizon

This paper introduces an adaptive polytopic estimator design for nonlinear systems under bounded ... more This paper introduces an adaptive polytopic estimator design for nonlinear systems under bounded disturbances combining moving horizon and dual estimation techniques. It extends the moving horizon estimation results for LTI systems to polytopic LPV systems. The design and necessary conditions to guarantee the robust stability and convergence to the true state and parameters for the case of bounded disturbances and convergence to the true system and state are given for the vanishing disturbances.

Research paper thumbnail of Modeling and control of an agricultural articulated vehicle carrying an implement

2017 XVII Workshop on Information Processing and Control (RPIC), 2017

This work focuses on the study of an agricultural articulated vehicle which carries an implement ... more This work focuses on the study of an agricultural articulated vehicle which carries an implement (such as a plow or a harvester). The derivation of a mathematical model of its kinematics is presented, and then the methodology of model predictive control is used in order to have the implement follow a predefined trajectory. The performance of the system and the corresponding controller is ilustrated through numerical simulations, which yield satisfactory results.

Research paper thumbnail of Moving Horizon Estimation for GNSS-IMU sensor fusion

Revista Tecnología y Ciencia, 2020

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measu... more The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

Research paper thumbnail of Adaptive arrival cost update for improving Moving Horizon Estimation performance

ISA Transactions, 2017

Moving horizon estimation is an efficient technique to estimate states and parameters of constrai... more Moving horizon estimation is an efficient technique to estimate states and parameters of constrained dynamical systems. It relies on the solution of a finite horizon optimization problem to compute the estimates, providing a natural framework to handle bounds and constraints on estimates, noises and parameters. However, the approximation of the arrival cost and its updating mechanism are an active research topic. The arrival cost is very important because it provides a mean to incorporate information from previous measurements to the current estimates and it is difficult to estimate its true value. In this work, we exploit the features of adaptive estimation methods to update the parameters of the arrival cost. We show that, having a better approximation of the arrival cost, the size of the optimization problem can be significantly reduced guaranteeing the stability and convergence of the estimates. These properties are illustrated through simulation studies.

Research paper thumbnail of Multivariable predictive feedback control

Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002

Abstract In this work a new method for designing predictive controllers for linear MIMO systems i... more Abstract In this work a new method for designing predictive controllers for linear MIMO systems is presented. It uses a prediction of the process output J time intervals ahead to compute the correspondent future error. Then, the predictive feedback controller is defined by introducing a filter that weights the last w-predicted errors. In this way, the resulting control action is computed by observing the system future behavior and also by weighting present and past errors

Research paper thumbnail of Cooperative-feedback control

ISA Transactions, 2007

In this work a control structure capable of handling controllability problems, which emerge from ... more In this work a control structure capable of handling controllability problems, which emerge from the presence of constraints, and improve the performance of the system by coordinating the use of several manipulated variables is introduced. In this scheme, the primary manipulated variable is used to handle the transient response while the auxiliary manipulated variable is used to keep the primary manipulated variable away from saturation. These two manipulated variables are coordinated through a user-defined non-linear function, which decides when and how the control structure changes. Its parameters determine the interaction between both inputs and the steady state value of each manipulated variable. The effectiveness of the proposed scheme is illustrated in two simulation examples.

Research paper thumbnail of Flexible-structure control: A strategy for releasing input constraints

ISA Transactions, 2004

In this paper output unreachability under input saturation phenomenon is studied: under a large d... more In this paper output unreachability under input saturation phenomenon is studied: under a large disturbance or setpoint change, the process output may never reach the set point even when the manipulated variable has driven to saturation. The process output can be brought back to the set point only by activating an auxiliary manipulated variable. A new control structure for designing and implementing a control system capable of solving this problem is proposed by transferring the control from one variable to another and taking into account the different dynamics involved in the system. The control structure, called flexible-structure control due to its ability to adapt the control structure to the operating conditons, is a generalization of the split-range control. It can be summarized as two controllers connected through a piecewise linear function. This function decides, based on the value of one manipulated variable, when and how the control structure changes. Its parameters control the interaction between both manipulated variables and leave the capability for handling the balance between control quality and other goals to the operator.

Research paper thumbnail of Observer-based adaptive control using multiple-models switching and tuning

IET Control Theory & Applications, 2014

Despite the remarkable theoretical accomplishments and successful applications of adaptive contro... more Despite the remarkable theoretical accomplishments and successful applications of adaptive control, this field is not mature enough to solve challenging problems where strict performance and robustness guarantees are required. The needs of an approach that explicitly accounts for robust performance and stability specifications is a critical to the design of practical adaptive control systems. Towards this goal, this study extends the robust adaptive controller using multiple models, switching and tuning to multiple input multiple output and non-linear systems. The use of 'extended superstability', instead of superstability, allows us to establish overall performance guarantees and reduce the conservativeness of the resulting closed-loop system. The authors show that under the proposed framework, the output and states remain bounded for bounded disturbances, as a direct consequence of the passivation properties of superstability. The effectiveness of the proposed algorithm is demonstrated in numerical simulations of a non-linear continuous stirred tank reactor.

Research paper thumbnail of Robust adaptive control using multiple models, switching and tuning

IET Control Theory & Applications, 2011

The supervisory control problem is analysed as an online robust design problem using switching to... more The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models P L(k) , from the set of admissible models P L , that explains the input-output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on P L , allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.

Research paper thumbnail of Mecánica Computacional, Volume XXX. Number 26. Optimization and Control (B)

Resumen. Este trabajo presenta los primeros pasos en el desarrollo de sistemas de control para av... more Resumen. Este trabajo presenta los primeros pasos en el desarrollo de sistemas de control para aviones. Como primera aplicación nuestro sistema a controlar es un modelo longitudinal de un avión. El mismo presenta tres grados de libertad: movimiento de traslación, movimiento de pitch y movimiento en el plano vertical. En primera instancia trabajamos con el modelo linealizado del mismo pudiendo luego comparar los resultados obtenidos al utilizar el sistema no lineal. La técnica de control elegida es Model Predictive Control (MPC). Esta técnica nos ...

Research paper thumbnail of Simultaneous state estimation and control for nonlinear systems subject to bounded disturbances

ArXiv, 2019

In this work, we address the output--feedback control problem for nonlinear systems under bounded... more In this work, we address the output--feedback control problem for nonlinear systems under bounded disturbances using a moving horizon approach. The controller is posed as an optimization-based problem that simultaneously estimates the state trajectory and computes future control inputs. It minimizes a criterion that involves finite forward and backward horizon with respect the unknown initial state, measurement noises and control input variables and it is maximized with respect the unknown future disturbances. Although simultaneous state estimation and control approaches are already available in the literature, the novelty of this work relies on linking the lengths of the forward and backward windows with the closed-loop stability, assuming detectability and decoding sufficient conditions to assure system stabilizability. Simulation examples are carried out to compare the performance of simultaneous and independent estimation and control approaches as well as to show the effects of ...

Research paper thumbnail of Wiener modelling and model predictive control for wastewater applications

The research presented in this paper aims to demonstrate the application of predictive control to... more The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control.

Research paper thumbnail of Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina

The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina... more The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible-Infectious-Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Paraná and Rosario), during the 2009 – 2018 period. Results obtained by solving the proposed SIR model for the 2010 outbreaks are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well when isolated cases appear outside the outbreak periods, probably due to non-climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet availab...

Research paper thumbnail of Influenza epidemic model using dynamic social networks of individuals with cognition maps

MethodsX, 2020

The dynamic of infectious disease is the result of the interplay between the spread of pathogens ... more The dynamic of infectious disease is the result of the interplay between the spread of pathogens and individuals' behaviour. This interaction can be modelled through a network of interdependent dynamical blocks with multiple feedback connections. Epidemic outbreaks trigger behavioural responses, at the group and individual levels, which in turn influence the development of the epidemic. The interactions can be modelled through adaptive temporal networks whose nodes represent the individuals interconnected. Here we introduce an individual-based model where the behaviour of each agent is governed by its appreciation of the environment and external stimulus and its appreciation of its environment. It is built as a combination of three interacting blocks: (i) individual behaviour, (ii) social behaviour and (iii) health state. • Here, we introduce an individual-based model. • Individual's behaviour is modelled through the interplay of information of its health state as well as its neighbourhood (infected and recovered neighbours) and global epidemic situation; • Social behaviour is modelled through contact network that aggregates the behaviour and health state of the individuals; • The proposed model allows to use a wide range of alternatives for modelling each of these blocks, that provides flexibility to select the most adequate tool to model each component of the framework.

Research paper thumbnail of Fault Isolation Filter for Nonlinear Systems

IFAC Proceedings Volumes, 2003

In the paper the problem of detecting and isolating multiple faults for nonlinear systems is cons... more In the paper the problem of detecting and isolating multiple faults for nonlinear systems is considered. A strategy of state filtering is derived in order to detect and isolate multiple faults which appear simultaneously or sequentiaIJy in a discrete time nonlinear systems with unknown inputs. For the considered system for which a fault isolation condition is fulfilled the proposed method can isolate p simultaneous faults with at least p+q output measurements, where q is the number of unknown inputs or disturbances. A reduced output residual vector of dimension p+q is generated and the elements of this vector are decoupled in a way that each element of the vector is associated with only one fault or un measured input.

Research paper thumbnail of Distributed Control Design for Underwater Vehicles

IFAC Proceedings Volumes, 2008

The vast majority of control applications are based on non-interacting decentralized control desi... more The vast majority of control applications are based on non-interacting decentralized control designs. Because of their single-loop structure, these controllers cannot suppress interactions of the system. It would be useful to tackle the undesirable effects of the interactions at the design stage. A novel model predictive control scheme based on Nash optimality is presented to achieve this goal. In this algorithm, the control problem is decomposed into that of several small-coupled mixed integer optimisation problems. The relevant computational convergence, closed-loop performance and the effect of communication failures on the closed-loop behaviour are analysed. Simulation results are presented to illustrate the effectiveness and practicality of the proposed control algorithm.

Research paper thumbnail of Simultaneous moving horizon estimation and control for nonlinear systems subject to bounded disturbances

International Journal of Robust and Nonlinear Control

SummaryIn this work, we address the output–feedback control problem for nonlinear systems under b... more SummaryIn this work, we address the output–feedback control problem for nonlinear systems under bounded disturbances using a moving horizon approach. The controller is posed as an optimisation‐based problem that simultaneously estimates the state trajectory and computes future control inputs. It minimizes a criterion that involves finite backward and forward horizons with respect to the unknown initial state, measurement noises and control input variables. The main novelty of this work relies on linking the lengths of the forward and backward windows with the closed‐loop stability, assuming detectability and decoding sufficient conditions to assure system stabilizability. It leads to a formulation that does not require to be a Control Lyapunov Function for the terminal cost of the controller. Simulation examples are carried out to compare the performance of solving simultaneously and independently the estimation and control problems. Furthermore, the examples show how the controller...

Research paper thumbnail of Robust stability of moving horizon estimation for non‐linear systems with bounded disturbances using adaptive arrival cost

IET Control Theory & Applications, 2020

In this paper, the robust stability and convergence to the true state of moving horizon estimator... more In this paper, the robust stability and convergence to the true state of moving horizon estimator based on an adaptive arrival cost are established for nonlinear detectable systems. Robust global asymptotic stability is shown for the case of nonvanishing bounded disturbances whereas the convergence to the true state is proved for the case of vanishing disturbances. Several simulations were made in order to show the estimator behaviour under different operational conditions and to compare it with the state of the art estimation methods.

Research paper thumbnail of 0Distributed Model Predictive Control based on Dynamic Games

Model predictive control (MPC) is widely recognized as a high performance, yet practical, control... more Model predictive control (MPC) is widely recognized as a high performance, yet practical, control technology. This model-based control strategy solves at each sample a discrete-time optimal control problem over a finite horizon, producing a control input sequence. An at-tractive attribute of MPC technology is its ability to systematically account for system con-

Research paper thumbnail of MPC for nonlinear systems: A comparative review of discretization methods

2017 XVII Workshop on Information Processing and Control (RPIC), 2017

This work provides a comparative review of three different numerical methods generally used to di... more This work provides a comparative review of three different numerical methods generally used to discretize continuous-time non-linear equations appearing in model predictive control problems: direct multiple shooting, direct collocation and successive linearizations. An overview of the characteristics of each method is given and the performance of each method is evaluated through the simulation of two test cases.

Research paper thumbnail of Adaptive polytopic estimation for nonlinear systems under bounded disturbances using moving horizon

This paper introduces an adaptive polytopic estimator design for nonlinear systems under bounded ... more This paper introduces an adaptive polytopic estimator design for nonlinear systems under bounded disturbances combining moving horizon and dual estimation techniques. It extends the moving horizon estimation results for LTI systems to polytopic LPV systems. The design and necessary conditions to guarantee the robust stability and convergence to the true state and parameters for the case of bounded disturbances and convergence to the true system and state are given for the vanishing disturbances.

Research paper thumbnail of Modeling and control of an agricultural articulated vehicle carrying an implement

2017 XVII Workshop on Information Processing and Control (RPIC), 2017

This work focuses on the study of an agricultural articulated vehicle which carries an implement ... more This work focuses on the study of an agricultural articulated vehicle which carries an implement (such as a plow or a harvester). The derivation of a mathematical model of its kinematics is presented, and then the methodology of model predictive control is used in order to have the implement follow a predefined trajectory. The performance of the system and the corresponding controller is ilustrated through numerical simulations, which yield satisfactory results.

Research paper thumbnail of Moving Horizon Estimation for GNSS-IMU sensor fusion

Revista Tecnología y Ciencia, 2020

The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measu... more The aim of this work is to develop a Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensor fusion system. To achieve this objective, we introduce a Moving Horizon Estimation (MHE) algorithm to estimate the position, velocity orientation and also the accelerometer and gyroscope bias of a simulated unmanned ground vehicle. The obtained results are compared with the true values of the system and with an Extended Kalman filter (EKF). The use of CasADi and Ipopt provide efficient numerical solvers that can obtain fast solutions. The quality of MHE estimated values enable us to consider MHE as a viable replacement for the popular Kalman Filter, even on real time systems.

Research paper thumbnail of Adaptive arrival cost update for improving Moving Horizon Estimation performance

ISA Transactions, 2017

Moving horizon estimation is an efficient technique to estimate states and parameters of constrai... more Moving horizon estimation is an efficient technique to estimate states and parameters of constrained dynamical systems. It relies on the solution of a finite horizon optimization problem to compute the estimates, providing a natural framework to handle bounds and constraints on estimates, noises and parameters. However, the approximation of the arrival cost and its updating mechanism are an active research topic. The arrival cost is very important because it provides a mean to incorporate information from previous measurements to the current estimates and it is difficult to estimate its true value. In this work, we exploit the features of adaptive estimation methods to update the parameters of the arrival cost. We show that, having a better approximation of the arrival cost, the size of the optimization problem can be significantly reduced guaranteeing the stability and convergence of the estimates. These properties are illustrated through simulation studies.

Research paper thumbnail of Multivariable predictive feedback control

Proceedings First IEEE International Workshop on Electronic Design, Test and Applications '2002

Abstract In this work a new method for designing predictive controllers for linear MIMO systems i... more Abstract In this work a new method for designing predictive controllers for linear MIMO systems is presented. It uses a prediction of the process output J time intervals ahead to compute the correspondent future error. Then, the predictive feedback controller is defined by introducing a filter that weights the last w-predicted errors. In this way, the resulting control action is computed by observing the system future behavior and also by weighting present and past errors

Research paper thumbnail of Cooperative-feedback control

ISA Transactions, 2007

In this work a control structure capable of handling controllability problems, which emerge from ... more In this work a control structure capable of handling controllability problems, which emerge from the presence of constraints, and improve the performance of the system by coordinating the use of several manipulated variables is introduced. In this scheme, the primary manipulated variable is used to handle the transient response while the auxiliary manipulated variable is used to keep the primary manipulated variable away from saturation. These two manipulated variables are coordinated through a user-defined non-linear function, which decides when and how the control structure changes. Its parameters determine the interaction between both inputs and the steady state value of each manipulated variable. The effectiveness of the proposed scheme is illustrated in two simulation examples.

Research paper thumbnail of Flexible-structure control: A strategy for releasing input constraints

ISA Transactions, 2004

In this paper output unreachability under input saturation phenomenon is studied: under a large d... more In this paper output unreachability under input saturation phenomenon is studied: under a large disturbance or setpoint change, the process output may never reach the set point even when the manipulated variable has driven to saturation. The process output can be brought back to the set point only by activating an auxiliary manipulated variable. A new control structure for designing and implementing a control system capable of solving this problem is proposed by transferring the control from one variable to another and taking into account the different dynamics involved in the system. The control structure, called flexible-structure control due to its ability to adapt the control structure to the operating conditons, is a generalization of the split-range control. It can be summarized as two controllers connected through a piecewise linear function. This function decides, based on the value of one manipulated variable, when and how the control structure changes. Its parameters control the interaction between both manipulated variables and leave the capability for handling the balance between control quality and other goals to the operator.

Research paper thumbnail of Observer-based adaptive control using multiple-models switching and tuning

IET Control Theory & Applications, 2014

Despite the remarkable theoretical accomplishments and successful applications of adaptive contro... more Despite the remarkable theoretical accomplishments and successful applications of adaptive control, this field is not mature enough to solve challenging problems where strict performance and robustness guarantees are required. The needs of an approach that explicitly accounts for robust performance and stability specifications is a critical to the design of practical adaptive control systems. Towards this goal, this study extends the robust adaptive controller using multiple models, switching and tuning to multiple input multiple output and non-linear systems. The use of 'extended superstability', instead of superstability, allows us to establish overall performance guarantees and reduce the conservativeness of the resulting closed-loop system. The authors show that under the proposed framework, the output and states remain bounded for bounded disturbances, as a direct consequence of the passivation properties of superstability. The effectiveness of the proposed algorithm is demonstrated in numerical simulations of a non-linear continuous stirred tank reactor.

Research paper thumbnail of Robust adaptive control using multiple models, switching and tuning

IET Control Theory & Applications, 2011

The supervisory control problem is analysed as an online robust design problem using switching to... more The supervisory control problem is analysed as an online robust design problem using switching to select the relevant models for designing the control law. The proposed supervisory control algorithm is based on the integration of concepts used in supervisory adaptive control, robust control and receding horizon control. It involves a two-stage adaptive control algorithm: (i) the identification of a time-varying set of models P L(k) , from the set of admissible models P L , that explains the input-output behaviour of the system, followed by (ii) the design of the control law using a parametric linear optimisation problem. The authors show that under the proposed supervisory control algorithm, the system output remains bounded for any bounded disturbance. The use of superstability concepts, together with certain assumptions on P L , allows us to establish overall performance and robust stability guarantees for the supervisory scheme and to include constrains in the closed-loop variables as well as in the controller structure. The relevant features of the proposed control algorithm are demonstrated in a numerical simulation.

Research paper thumbnail of Mecánica Computacional, Volume XXX. Number 26. Optimization and Control (B)

Resumen. Este trabajo presenta los primeros pasos en el desarrollo de sistemas de control para av... more Resumen. Este trabajo presenta los primeros pasos en el desarrollo de sistemas de control para aviones. Como primera aplicación nuestro sistema a controlar es un modelo longitudinal de un avión. El mismo presenta tres grados de libertad: movimiento de traslación, movimiento de pitch y movimiento en el plano vertical. En primera instancia trabajamos con el modelo linealizado del mismo pudiendo luego comparar los resultados obtenidos al utilizar el sistema no lineal. La técnica de control elegida es Model Predictive Control (MPC). Esta técnica nos ...

Research paper thumbnail of Simultaneous state estimation and control for nonlinear systems subject to bounded disturbances

ArXiv, 2019

In this work, we address the output--feedback control problem for nonlinear systems under bounded... more In this work, we address the output--feedback control problem for nonlinear systems under bounded disturbances using a moving horizon approach. The controller is posed as an optimization-based problem that simultaneously estimates the state trajectory and computes future control inputs. It minimizes a criterion that involves finite forward and backward horizon with respect the unknown initial state, measurement noises and control input variables and it is maximized with respect the unknown future disturbances. Although simultaneous state estimation and control approaches are already available in the literature, the novelty of this work relies on linking the lengths of the forward and backward windows with the closed-loop stability, assuming detectability and decoding sufficient conditions to assure system stabilizability. Simulation examples are carried out to compare the performance of simultaneous and independent estimation and control approaches as well as to show the effects of ...

Research paper thumbnail of Wiener modelling and model predictive control for wastewater applications

The research presented in this paper aims to demonstrate the application of predictive control to... more The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control.

Research paper thumbnail of Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina

The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina... more The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible-Infectious-Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Paraná and Rosario), during the 2009 – 2018 period. Results obtained by solving the proposed SIR model for the 2010 outbreaks are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well when isolated cases appear outside the outbreak periods, probably due to non-climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet availab...

Research paper thumbnail of Influenza epidemic model using dynamic social networks of individuals with cognition maps

MethodsX, 2020

The dynamic of infectious disease is the result of the interplay between the spread of pathogens ... more The dynamic of infectious disease is the result of the interplay between the spread of pathogens and individuals' behaviour. This interaction can be modelled through a network of interdependent dynamical blocks with multiple feedback connections. Epidemic outbreaks trigger behavioural responses, at the group and individual levels, which in turn influence the development of the epidemic. The interactions can be modelled through adaptive temporal networks whose nodes represent the individuals interconnected. Here we introduce an individual-based model where the behaviour of each agent is governed by its appreciation of the environment and external stimulus and its appreciation of its environment. It is built as a combination of three interacting blocks: (i) individual behaviour, (ii) social behaviour and (iii) health state. • Here, we introduce an individual-based model. • Individual's behaviour is modelled through the interplay of information of its health state as well as its neighbourhood (infected and recovered neighbours) and global epidemic situation; • Social behaviour is modelled through contact network that aggregates the behaviour and health state of the individuals; • The proposed model allows to use a wide range of alternatives for modelling each of these blocks, that provides flexibility to select the most adequate tool to model each component of the framework.

Research paper thumbnail of Fault Isolation Filter for Nonlinear Systems

IFAC Proceedings Volumes, 2003

In the paper the problem of detecting and isolating multiple faults for nonlinear systems is cons... more In the paper the problem of detecting and isolating multiple faults for nonlinear systems is considered. A strategy of state filtering is derived in order to detect and isolate multiple faults which appear simultaneously or sequentiaIJy in a discrete time nonlinear systems with unknown inputs. For the considered system for which a fault isolation condition is fulfilled the proposed method can isolate p simultaneous faults with at least p+q output measurements, where q is the number of unknown inputs or disturbances. A reduced output residual vector of dimension p+q is generated and the elements of this vector are decoupled in a way that each element of the vector is associated with only one fault or un measured input.

Research paper thumbnail of Distributed Control Design for Underwater Vehicles

IFAC Proceedings Volumes, 2008

The vast majority of control applications are based on non-interacting decentralized control desi... more The vast majority of control applications are based on non-interacting decentralized control designs. Because of their single-loop structure, these controllers cannot suppress interactions of the system. It would be useful to tackle the undesirable effects of the interactions at the design stage. A novel model predictive control scheme based on Nash optimality is presented to achieve this goal. In this algorithm, the control problem is decomposed into that of several small-coupled mixed integer optimisation problems. The relevant computational convergence, closed-loop performance and the effect of communication failures on the closed-loop behaviour are analysed. Simulation results are presented to illustrate the effectiveness and practicality of the proposed control algorithm.