José de Jesús Rubio

Nonlinear system identification with recurrent neural networks and dead-zone Kalman filter algorithm

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 robustness 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.

Stability Analysis of Nonlinear System Identification via Delayed Neural Networks

In this brief, the identification problem for time-delay nonlinear system is discussed. We use a delayed dynamic neural network to do on-line identification. This neural network has dynamic series-parallel structure. The stability conditions of on-line identification are derived by Lyapunov-Krasovskii approach, which are described by linear matrix inequality. The conditions for passivity, asymptotic stability and uniform stability are established in some senses. We conclude that the gradient algorithm for updating the weights of the delayed neural networks is stable to any bounded uncertainties

Evolving intelligent algorithms for the modelling of brain and eye signals

In this paper, the modelling problem of brain and eye signals is considered. To solve this problem, three important evolving and stable intelligent algorithms are applied: the sequential adaptive fuzzy inference system (SAFIS), uniform stable backpropagation algorithm (SBP), and online self-organizing fuzzy modified least-squares networks (SOFMLS). The effectiveness of the studied methods is verified by simulations.

Backpropagation to train an evolving radial basis function neural network

In this paper, a stable backpropagation algorithm is used to train an online evolving radial basis function neural network. Structure and parameters learning are updated at the same time in our algorithm, we do not make difference in structure learning and parameters learning. It generates groups with an online clustering. The center is updated to achieve the center is near to the incoming data in each iteration, so the algorithm does not need to generate a new neuron in each iteration, i.e., the algorithm does not generate many neurons and it does not need to prune the neurons. We give a time varying learning rate for backpropagation training in the parameters. We prove the stability of the proposed algorithm.

Evolving intelligent system for the modelling of nonlinear systems with dead-zone input

In this paper, the modelling problem of nonlinear systems with dead-zone input is considered. To solve this problem, an evolving intelligent system is proposed. The uniform stability of the modelling error for the aforementioned system is guaranteed by means of a Lyapunov-like analysis. The effectiveness of the proposed technique is verified by simulations.

Adaptive least square control in discrete time of robotic arms

In this paper, the trajectory tracking problem of robotic arms in discrete time is considered. To solve this problem, an adaptive least square controller is proposed. The uniform stability of the tracking error and parameters error for the aforementioned controller is guaranteed by means of a Lyapunov-like analysis. The effectiveness of the proposed controller is verified by on-line simulations.In this paper, the trajectory tracking problem of robotic arms in discrete time is considered. To solve this problem, an adaptive least square controller is proposed. The uniform stability of the tracking error and parameters error for the aforementioned controller is guaranteed by means of a Lyapunov-like analysis. The effectiveness of the proposed controller is verified by on-line simulations.

Modeling and Control of Wind Turbine

In recent years, the energy production by wind turbines has been increasing, because its production is environmentally friendly; therefore, the technology developed for the production of energy through wind turbines brings great challenges in the investigation. This paper studies the characteristics of the wind turbine in the market and lab; it is focused on the recent advances of the wind turbine modeling with the aerodynamic power and the wind turbine control with the nonlinear, fuzzy, and predictive techniques.

Uniform stable observer for the disturbance estimation in two renewable energy systems.

In this study, an observer for the states and disturbance estimation in two renewable energy systems is introduced. The restrictions of the gains in the proposed observer are found to guarantee its stability and the convergence of its error; furthermore, these results are utilized to obtain a good estimation. The introduced technique is applied for the states and disturbance estimation in a wind turbine and an electric vehicle. The wind turbine has a rotatory tower to catch the incoming air to be transformed in electricity and the electric vehicle has generators connected with its wheels to catch the vehicle movement to be transformed in electricity.

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term.

A method for online pattern recognition of abnormal eye movements

A doctor could say that a patient is sick while he/she is healthy or could say that the patient is healthy while he/she is sick, by mistake. So it is important to generate a system that can give a good diagnosis, in this case for abnormal eye movements. An abnormal eye movement is when the patient wants to move the eye to up or down and the eye does not move or the eye moves to other place. In this paper, a method for the pattern recognition is used to provide a better diagnosis for patients related with the abnormal eye movements. The real data of signals of two eye movements (up and down) of patients are obtained using a mindset ms-100 system. A new method that uses one intelligent algorithm for online pattern recognition is proposed. The difference between the proposed method and the previous works is that, in other works, both behaviors (up and down) are trained with one intelligent algorithm, while in this work, up behavior is trained with one intelligent algorithm and down behavior is trained with other intelligent algorithm; it is because the multi-output system can always be decomposed into a collection of single-output systems with the advantage to use different parameters for each one if necessary. The intelligent algorithm used by the proposed method could be any of the following: the adaline network denoted as AN, the multilayer neural network denoted as NN, or the Sugeno fuzzy inference system denoted as SF. So the comparison results of the proposed method using each of the intelligent algorithms for online pattern recognition of two eye movements are presented.