Inverse Optimal Control with Speed Gradient for a Power Electric System Using a Neural Reduced Model

This paper presented an inverse optimal neural controller with speed gradient (SG) for discrete-time unknown nonlinear systems in the presence of external disturbances and parameter uncertainties, for a power electric system with different types of faults in the transmission lines including load variations. It is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF) based algorithm. It is well known that electric power grids are considered as complex systems due to their interconections and number of state variables; then, in this paper, a reduced neural model for synchronous machine is proposed for the stabilization of nine bus system in the presence of a fault in three different cases in the lines of transmission

Fast Chaotic Encryption for Hyperspectral Images

The information collected by hyperspectral images (HI) is essential in applications of remote sensing like object detection, geological process recognition, and identifying materials. However, HI information could be sensitive, and therefore, it should be protected. In this chapter, we show a parallel encryption algorithm specifically designed for HI. The algorithm uses multiple chaotic systems to produce a crossed multidimensional chaotic map for encrypting the image; the scheme takes advantage of the multidimensional nature of HI and is highly parallelizable, which leads to a time-efficient algorithm. We also show that the algorithm gets high-entropy ciphertext and is robust to ciphertext-only attacks

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. \ua9 2013 Alma Y. Alanis et al

Particle Swarm Optimization Algorithm with a Bio-Inspired Aging Model

A Particle Swarm Optimization with a Bio-inspired Aging Model (BAM-PSO) algorithm is proposed to alleviate the premature convergence problem of other PSO algorithms. Each particle within the swarm is subjected to aging based on the age-related changes observed in immune system cells. The proposed algorithm is tested with several popular and well-established benchmark functions and its performance is compared to other evolutionary algorithms in both low and high dimensional scenarios. Simulation results reveal that at the cost of computational time, the proposed algorithm has the potential to solve the premature convergence problem that affects PSO-based algorithms; showing good results for both low and high dimensional problems. This work suggests that aging mechanisms do have further implications in computational intelligence

Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications

This book contains the successful invited submissions [...

Nested High Order Sliding Mode Controller with Back-EMF Sliding Mode Observer for a Brushless Direct Current Motor

This work presents a nested super-twisting second-order sliding mode speed controller for a brushless direct current motor with a high order sliding mode observer used for back electromotive force (back-EMF) estimation. Due to the trapezoidal nature of the back-EMF, a modified Park transformation is used in order to achieve proper field orientation. Such transformation requires information from the back-EMF that is not accessible. A second-order sliding mode observer is used to estimate the back electromotive forces needed in the modified transformation. Sliding mode control is known to be robust to matched uncertain disturbances and parametric variations but it is prone to unmatched perturbations that affect the performance of the system. A nested scheme is used to improve the response of the controller in presence of unmatched disturbances. Simulations performed under similar conditions to real-time experimentation show a good regulation of the rotor speed in terms of transient and steady-state responses along with a reduced torque ripple