Discrete-Time Output Trajectory Tracking by Recurrent High-Order Neural Network Control

The 2013 International Joint Conference on Neural Networks (IJCNN)

et al. 2013

Self Cite

Type 1 Diabetes mellitus (T1DM) is a chronic disease that occurs when the body cannot produce insulin. Since insulin was discovered in 1920, the way to keep T1DM patients blood glucose at normal levels has been insulin injections, via subcutaneous or intravenous paths. The e¤orts for an external infusion therapy have resulted in the so-called Arti…cial Pancreas. Such device attempts to integrate continuous insulin infusion, continuous glucose monitoring and an automatic control algorithm, which calculates the required insulin infusion. Considering all the problems related to T1DM, in this paper a neural model which captures the nonlinear behavior of the complex glucose-insulin dynamics is proposed; based on this model, a control algorithm is developed using the neural inverse optimal control via control lyapunov function (CLF) technique. Simulation results illustrate the applicability of the propounded scheme.

Section: Neural Network Architecturementioning

confidence: 98%

“…However, the neural model (5) can be seen as an special case of the neural identi…er presented in [31].…”

Section: Neural Network Architecturementioning

confidence: 99%

Subcutaneous neural inverse optimal control for an Artificial Pancreas

Leon

Alanis

The 2013 International Joint Conference on Neural Networks (IJCNN)

et al. 2013

Self Cite

“…Neural networks have grown to be a well-established methodology, which allows to solve very difficult problems in engineering, as exemplified by their applications to modeling and control of general nonlinear and complex systems. The most used NN structures are feedforward networks and recurrent ones [34][35][36][37]. Since the seminal paper [38], there has been a continuously increasing interest in applying NNs to identification and control of nonlinear systems [39].…”

Section: Neural Model and Controlmentioning

confidence: 99%

Inverse optimal neural control for a class of discrete‐time nonlinear positive systems

Leon

Alanis

Adaptive Control & Signal

et al. 2012

Self Cite

In this paper, a discrete-time inverse optimal trajectory tracking for a class of nonlinear positive systems is proposed. The scheme is developed for MIMO affine discrete-time positive nonlinear systems. This optimal controller is based on discrete time passivity and positive systems theory. The advantage of this scheme is that it avoids solving the associated Hamilton-Jacobi-Bellman equation and minimizes a meaningful cost function. The affine discrete-time positive nonlinear system is obtained from an online neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter. The applicability of the proposed approach is illustrated via simulation by trajectory tracking control of type 1 diabetes mellitus patients.

“…Based on this model, a control law is derived, which combines discrete-time block control and sliding mode techniques. The block control approach is used to design a nonlinear sliding surface such that the resulting sliding mode dynamics is described by a desired linear system [10]. Additionally, Simulations for the pro posed control scheme using a Mitsubishi PA \ 0-7CE robot arm are presented.…”

Section: Introductionmentioning

confidence: 99%

Decentralized neural block control for an industrial PA10-7CE robot arm

Garcia-Hernandez

The 2011 International Joint Conference on Neural Networks

Santibáñez

et al. 2011

This paper presents a solution of the trajectory tracking problem for robotic manipulators using a recurrent high order neural network (RHONN) structure to identify the robot arm dynamics, and based on this model a discrete-time control law is derived, which combines block control and the sliding mode techniques. The block control approach is used to design a nonlinear sliding surface such that the resulting sliding mode dynamics is described by a desired linear system.The neural network learning is performed on-line by Kalman filtering. The local controller for each joint uses only local angular position and velocity measurements. The applicability of the proposed control scheme is illustrated via simulations.