Stable indirect adaptive neural controller for a class of nonlinear system

Adaptive Integral Sliding Mode Control Using Fully Connected Recurrent Neural Network for Position and Attitude Control of Quadrotor

This article proposes an adaptive integral sliding mode control (ISMC) strategy for quadrotor control that ensures faster and finite-time convergence along with chattering attenuation. Quadrotor dynamics are assumed to be unknown because of the high degree of parametric uncertainties, including external disturbances. The equivalent control law obtained by ISMC consists of quadrotor dynamics and, thus, cannot be applied to the quadrotor. A new fully connected recurrent neural network (FCRNN) controller has been proposed to mimic the equivalent control instead of estimating the Quadrotor dynamics separately. The proposed FCRNN architecture consists of output feedback to the input layer and the hidden layer, which enhances the approximation capability of FCRNN. All hidden layer neurons receive self-feedback and feedback from other hidden layer neurons, which further strengthens FCRNN's potential to capture complex dynamic characteristics. As learning should happen in finite time, the finite-time stability of the overall system has been guaranteed using the Lyapunov stability theory, and the update laws for FCRNN weights in real time are derived using the same. To show the effectiveness of the proposed approach, a comprehensive analysis has been done against existing SMC strategy and against well-known function approximation techniques, e.g., the radial basis function network (RBFN) and RNN.

A Hybrid Method Based on Extreme Learning Machine and Self Organizing Map for Pattern Classification

Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In order to select a suitable number of hidden neurons, this paper proposes a novel hybrid learning based on a two-step process. First, the parameters of hidden layer are adjusted by a self-organized learning algorithm. Next, the weights matrix of the output layer is determined using the Moore–Penrose inverse method. Nine classification datasets are considered to demonstrate the efficiency of the proposed approach compared with original extreme learning machine, Tikhonov regularization optimally pruned extreme learning machine, and backpropagation algorithms. The results show that the proposed method is fast and produces better accuracy and generalization performances.

Multi-Agent System Based on the Extreme Learning Machine and Fuzzy Control for Intelligent Energy Management in Microgrid

Renewable energies constitute an alternative to fossil energies for several reasons. The microgrid can be assumed as the ideal way to integrate a renewable energy source in the production of electricity and give the consumer the opportunity to participate in the electricity market not just like a consumer but also like a producer. In this paper, we present a multi-agent system based on wind and photovoltaic power prediction using the extreme learning machine algorithm. This algorithm was tested on real weather data taken from the region of Tetouan City in Morocco. The process aimed to implement a microgrid located in Tetouan City and composed of different generation units (solar and wind energies were combined together to increase the efficiency of the system) and storage units (batteries were used to ensure the availability of power on demand as much as possible). In the proposed architecture, the microgrid can exchange electricity with the main grid; therefore, it can buy or sell electricity. Thus, the goal of our multi-agent system is to control the amount of power delivered or taken from the main grid in order to reduce the cost and maximize the benefit. To address uncertainties in the system, we use fuzzy logic control to manage the flow of energy, to ensure the availability of power on demand, and to make a reasonable decision about storing or selling electricity.

Trends in extreme learning machines: a review

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

Neural network L1 adaptive control of MIMO systems with nonlinear uncertainty

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L₁ adaptive controller and an auxiliary neural network (NN) compensation controller. The L₁ adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signals. The auxiliary compensation controller is designed to approximate the unknown nonlinear functions by MIMO RBF neural networks to suppress the influence of uncertainties. NN weights are tuned on-line with no prior training and the project operator ensures the weights bounded. The global stability of the closed-system is derived based on the Lyapunov function. Numerical simulations of an MIMO system coupled with nonlinear uncertainties are used to illustrate the practical potential of our theoretical results.