Jorge D. Rios

Neural identifier for unknown discrete-time nonlinear delayed systems

This work proposes a discrete-time nonlinear neural identifier based on a recurrent high-order neural network trained with an extended Kalman filter-based algorithm for discrete-time deterministic multiple-input multiple-output systems with unknown dynamics and time-delay. To prove the semi-globally uniformly ultimately boundedness of the proposed neural identifier, the stability analysis based on the Lyapunov approach is included. Applicability of the proposed identifier is shown via simulation and experimental results, all of them performed under the presence of unknown external and internal disturbances as well as unknown time-delays.

Real-time discrete neural identifier for a linear induction motor using a dSPACE DS1104 board

This paper presents a real-time discrete nonlinear neural identifier for a Linear Induction Motor (LIM). This identifier is based on a discrete-time recurrent high order neural network (RHONN) trained on-line with an extended Kalman filter (EKF)-based algorithm. A reduced order observer is used to estimate the secondary fluxes. The real-time implementation of the neural identifier is implemented by using dSPACE DS1104 controller board on MATLAB/Simulink with dSPACE RTI library and its performance is shown by graphs.

Real-time neural identification and inverse optimal control for a tracked robot

This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter–based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB®, and also the experimental tests use a modified HD2® Treaded ATR Tank Robot Platform with wireless communication.

Recurrent High Order Neural Observer for Discrete-Time Non-Linear Systems with Unknown Time-Delay

This work proposes a discrete-time non-linear neural observer based on a recurrent high order neural network in parallel model trained with an algorithm based on the extended Kalman filter for discrete-time multiple input multiple output non-linear systems with unknown dynamics and unknown time-delay. To prove the semi-globally uniformly ultimately boundedness of the proposed neural observer the stability analysis based on the Lyapunov approach is included. Applicability of the proposed observer is shown via simulation and experimental results.

Real-time neural inverse optimal control for a linear induction motor

ABSTRACT A discrete-time neural inverse optimal control is designed for a three-phase linear induction motor (LIM) in order to control its position. This controller is optimal in the sense that it minimises a cost functional. A recurrent high-order neural network, trained with the extended Kalman filter, is employed to obtain a mathematical model for the LIM with uncertainties. A super twisting-based state estimator provides an estimate of the unmeasurable state variables of the system. This control scheme is applied in real time in an LIM prototype which achieves trajectory tracking for a position reference.

RHONN identifier for unknown nonlinear discrete-time delay systems

This work proposes a discrete-time nonlinear neural identifier based on a Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF) based algorithm for discrete-time deterministic multiple input multiple output (MIMO) systems with unknown dynamics and time-delay. Applicability of the proposed identifier is shown via experimental results performed under the presence of unknown external and internal disturbances as well as unknown time-delays.

Neural Identifier-Control Scheme for Nonlinear Discrete Systems with Input Delay

This work presents a scheme based on a discrete recurrent high order neural network identifier and a block control based on sliding modes for nonlinear discrete-time systems with input delays in real-time. The identifier is trained with an extended Kalman Filter based algorithm and the block control is used for trajectory tracking. Experimental results are included using a linear induction motor prototype with added delays to its input signals.