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Papers by fernando ornelas
This paper deals with blood glucose level control. Inverse optimal trajectory tracking for discre... more This paper deals with blood glucose level control. Inverse optimal trajectory tracking for discrete time non-linear positive systems is applied. The scheme is developed for MIMO (multi-input, multi-output) affine systems. The control law calculates the subcutaneous insulin delivery rate in order to prevent hyperglycemia and hypoglycemia levels. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an affine form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Simulation results illustrate the applicability of the control law in biological processes.
Proceedings of the 18th IFAC World Congress, 2011
This paper presents a robust inverse optimal control approach for robust stabilization of discret... more This paper presents a robust inverse optimal control approach for robust stabilization of discrete-time perturbed nonlinear systems, avoiding the need to solve the associated Hamilton-Jacobi-Isaacs equation, and minimizing a meaningful cost function. This robust stabilizing optimal controller is based on a discrete-time control Lyapunov function.
2011 IEEE International Conference on Control Applications (CCA), 2011
This paper presents an inverse optimal control approach for exponential stabilization of discrete... more This paper presents an inverse optimal control approach for exponential stabilization of discrete-time non- linear systems, avoiding to solve the associated Hamilton- Jacobi-Bellman (HJB) equation, and minimizing a meaningful cost function. This stabilizing optimal controller is based on a discrete-time control Lyapunov function. The applicability of the proposed approach is illustrated via simulations by stabilization of an example. In optimal
2010 IEEE International Symposium on Intelligent Control, 2010
This paper presents an inverse optimal neural controller, which is constituted by the combination... more This paper presents an inverse optimal neural controller, which is constituted by the combination of two techniques: a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to build a model of an assumed unknown nonlinear system. The applicability of the proposed approach is illustrated by real-time control of a planar robot.
49th IEEE Conference on Decision and Control (CDC), 2010
ABSTRACT This paper presents an inverse optimal control approach for output tracking of discrete-... more ABSTRACT This paper presents an inverse optimal control approach for output tracking of discrete-time nonlinear systems, avoiding to solve the associated Hamilton-Jacobi-Bellman (HJB) equation, and minimizing a meaningful cost function. This stabilizing optimal controller is based on discrete-time passivity theory. The applicability of the proposed approach is illustrated via simulations by trajectory tracking control of a planar robot.
IEEE Conference on Decision and Control and European Control Conference, 2011
In this paper, discrete time inverse optimal trajectory tracking for a class of non-linear positi... more In this paper, discrete time inverse optimal trajectory tracking for a class of non-linear positive systems is proposed. The scheme is developed for MIMO (multi-input, multi-output) a!ne systems. This approach is adapted for glycemic control of type 1 diabetes mellitus (T1DM) patients. The control law calculates the insulin delivery rate in order to prevent hyperglycemia levels. A neural model is
ABSTRACT This paper presents an inverse optimal neural controller, which is developed on the basi... more ABSTRACT This paper presents an inverse optimal neural controller, which is developed on the basis of two techniques: a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to build a model of an assumed unknown nonlinear system. The applicability of the proposed approach is illustrated via simulations by trajectory tracking control of a planar robot.
![Research paper thumbnail of Isomers on the [H, S 2, Cl] potential energy surface: A high level investigation](https://attachments.academia-assets.com/70200291/thumbnails/1.jpg)
](Journal of Molecular Structure Theochem, May 1, 2009
This study reports a systematic state-of-the-art characterization of new sulfur-chlorine species ... more This study reports a systematic state-of-the-art characterization of new sulfur-chlorine species on the [H, S 2 , Cl] potential energy surface. Coupled cluster theory singles and doubles with perturbative contributions of connected triples, using the series of correlation consistent basis sets with extrapolations to the complete basis set limit (CBS), were employed to quantify the energetic quantities involved in the isomerization processes on this surface. The structures and vibrational frequencies are unique for some species and represent the most accurate investigation to date. These molecules are potentially a new route of coupling the sulfur and chlorine chemistries in the atmosphere, and conditions of high concentration of H 2 S (HS) like in volcanic eruptions might contribute to their formation. Also an assessment of the MP2/CBS approach relative to CCSD(T)/CBS provides insights on the expected performance of MP2/ CBS on the characterization of polysulfides, and also of more complex systems containing disulfide bridges.
This paper deals with blood glucose level control. Inverse optimal trajectory tracking for discre... more This paper deals with blood glucose level control. Inverse optimal trajectory tracking for discrete time non-linear positive systems is applied. The scheme is developed for MIMO (multi-input, multi-output) affine systems. The control law calculates the subcutaneous insulin delivery rate in order to prevent hyperglycemia and hypoglycemia levels. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an affine form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Simulation results illustrate the applicability of the control law in biological processes.
Proceedings of the 18th IFAC World Congress, 2011
This paper presents a robust inverse optimal control approach for robust stabilization of discret... more This paper presents a robust inverse optimal control approach for robust stabilization of discrete-time perturbed nonlinear systems, avoiding the need to solve the associated Hamilton-Jacobi-Isaacs equation, and minimizing a meaningful cost function. This robust stabilizing optimal controller is based on a discrete-time control Lyapunov function.
2011 IEEE International Conference on Control Applications (CCA), 2011
This paper presents an inverse optimal control approach for exponential stabilization of discrete... more This paper presents an inverse optimal control approach for exponential stabilization of discrete-time non- linear systems, avoiding to solve the associated Hamilton- Jacobi-Bellman (HJB) equation, and minimizing a meaningful cost function. This stabilizing optimal controller is based on a discrete-time control Lyapunov function. The applicability of the proposed approach is illustrated via simulations by stabilization of an example. In optimal
2010 IEEE International Symposium on Intelligent Control, 2010
This paper presents an inverse optimal neural controller, which is constituted by the combination... more This paper presents an inverse optimal neural controller, which is constituted by the combination of two techniques: a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to build a model of an assumed unknown nonlinear system. The applicability of the proposed approach is illustrated by real-time control of a planar robot.
49th IEEE Conference on Decision and Control (CDC), 2010
ABSTRACT This paper presents an inverse optimal control approach for output tracking of discrete-... more ABSTRACT This paper presents an inverse optimal control approach for output tracking of discrete-time nonlinear systems, avoiding to solve the associated Hamilton-Jacobi-Bellman (HJB) equation, and minimizing a meaningful cost function. This stabilizing optimal controller is based on discrete-time passivity theory. The applicability of the proposed approach is illustrated via simulations by trajectory tracking control of a planar robot.
IEEE Conference on Decision and Control and European Control Conference, 2011
In this paper, discrete time inverse optimal trajectory tracking for a class of non-linear positi... more In this paper, discrete time inverse optimal trajectory tracking for a class of non-linear positive systems is proposed. The scheme is developed for MIMO (multi-input, multi-output) a!ne systems. This approach is adapted for glycemic control of type 1 diabetes mellitus (T1DM) patients. The control law calculates the insulin delivery rate in order to prevent hyperglycemia levels. A neural model is
ABSTRACT This paper presents an inverse optimal neural controller, which is developed on the basi... more ABSTRACT This paper presents an inverse optimal neural controller, which is developed on the basis of two techniques: a) inverse optimal control to avoid solving the Hamilton Jacobi Bellman (HJB) equation associated to nonlinear system optimal control, and b) an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF), in order to build a model of an assumed unknown nonlinear system. The applicability of the proposed approach is illustrated via simulations by trajectory tracking control of a planar robot.
Journal of Molecular Structure Theochem, May 1, 2009
This study reports a systematic state-of-the-art characterization of new sulfur-chlorine species ... more This study reports a systematic state-of-the-art characterization of new sulfur-chlorine species on the [H, S 2 , Cl] potential energy surface. Coupled cluster theory singles and doubles with perturbative contributions of connected triples, using the series of correlation consistent basis sets with extrapolations to the complete basis set limit (CBS), were employed to quantify the energetic quantities involved in the isomerization processes on this surface. The structures and vibrational frequencies are unique for some species and represent the most accurate investigation to date. These molecules are potentially a new route of coupling the sulfur and chlorine chemistries in the atmosphere, and conditions of high concentration of H 2 S (HS) like in volcanic eruptions might contribute to their formation. Also an assessment of the MP2/CBS approach relative to CCSD(T)/CBS provides insights on the expected performance of MP2/ CBS on the characterization of polysulfides, and also of more complex systems containing disulfide bridges.