J. de Jesus Rubio

A new discrete-time sliding-mode control with time-varying gain and neural identification

In this paper, we present a new sliding mode controller for a class of unknown non-linear discrete-time systems. We make the following two modifications. (1) The neural identifier which is used to estimate the unknown non-linear system uses the projection and the dead-zone approaches to assure non-singularity in the controller and stability of identification error. (2) We propose a new sliding mode controller with time-varying gain to reduce chattering. A necessary condition is given to make the switching function decrease exponentially. We prove that the closed-loop system with the sliding mode controller and the neural identifier is stable.

Recurrent Neural Networks Training With Stable Bounding Ellipsoid Algorithm

Bounding ellipsoid (BE) algorithms offer an attractive alternative to traditional training algorithms for neural networks, for example, backpropagation and least squares methods. The benefits include high computational efficiency and fast convergence speed. In this paper, we propose an ellipsoid propagation algorithm to train the weights of recurrent neural networks for nonlinear systems identification. Both hidden layers and output layers can be updated. The stability of the BE algorithm is proven.

Dead-zone Kalman filter algorithm for recurrent neural networks

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence, although this algorithm is more complex and sensitive to the nature of noises. In this paper, extended Kalman filter is applied to train state-space recurrent neural networks for nonlinear system identification. In order to improve robustnees of Kalman filter algorithm dead-zone robust modification is applied to Kalman filter. Lyapunov method is used to prove that the Kalman filter training is stable.

Recurrent neural networks training with stable risk-sensitive Kalman filter algorithm

Compared to normal learning algorithms, for example backpropagation, Kalman filter-based algorithm has some better properties, such as faster convergence. In this paper, Kalman filter is modified with a risk-sensitive cost criterion, we call it as risk-sensitive Kalman filter. This new algorithm is applied to train recurrent neural networks for nonlinear system identification. Input-to-state stability is used to prove that the risk-sensitive Kalman filter training is stable. The contributions of this paper are: 1) the risk-sensitive Kalman filter is used for the state-space recurrent neural networks training, 2) the stability of the risk-sensitive Kalman filter is proved.

A new discrete-time sliding-mode control with neural identification

In this paper, we present a new sliding mode controller for a class of unknown nonlinear discrete-time systems. We make the following two modifications: 1) the neural identifier which is used to estimate the unknown nonlinear system, applies new learning algorithms. The stability and non-zero properties are proved by dead-zone and projection technique. 2) We propose a new sliding surface and give a necessary condition to assure exponential decrease of the sliding surface. The time-varying gain in the sliding mode produces a low-chattering control signal. The closed-loop system with sliding mode controller and neural identifier is proved to be stable by Lyapunov method

Modelling of crude oil blending via discrete-time neural networks

Crude oil blending is an important unit operation in petroleum refining industry. A good model for the blending system is beneficial for supervision operation, prediction of the export petroleum quality and realizing model-based optimal control. Since the blending cannot follow the ideal mixing rule in practice, we propose a static neural network to approximate the blending properties. By input-to-state stability and dead-zone approaches, we propose a new robust learning algorithm and give theoretical analysis. Real data is applied to illustrate the neuro modeling approache.

Recurrent neural networks training with optimal bounded ellipsoid algorithm

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied traing the weights of recurrent neural networks for nonlinear system identification Both hidden layers and output layers can be updated. From a dynamic systems point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. A simple simulation gives the effectiveness of the suggested algorithm.

Time-Delay Nonlinear System Modelling via Delayed Neural Networks

In this paper, nonlinear systems on-line identification via delayed dynamic neural networks is studied. Dynamic series-parallel neural network model with time delay is presented and the stability conditions are derived using Lyapunov-Krasovskii approach. The conditions for passivity, asymptotic stability are established in some senses. All the results are described by linear matrix inequality (LMI). We conclude that the gradient algorithm for weight adjustment is stable and robust to any bounded uncertainties