LinearAlgebraQuickReview

Linear Algebra Quick Review

DDRTC-of-UMSs

This paper presents a data-driven control design framework to achieve robust tracking control without exploiting mathematical model of nonlinear underactuated mechanical systems (UMS). The method leverages the differential flatness property of linearized systems and online estimation and compensation of disturbances by active disturbance rejection control (ADRC). The differentially flat output is derived directly from measured data with unknown dynamics and parameters of UMS by the flat output identification (FOID) algorithm. A reduced nominal model of UMS is proposed to simplify the process of finding flat output and trajectory planning. Technique of sparse regression is applied to identify the relationships between flat output and system states, which reduces the order of the well-known extended state observer (ESO) and thereby make the ESO more effective for both trajectory planning and tracking in terms …

FOIDA

For paper: Identification of Differentially Flat Output of Underactuated Dynamic Systems - International Journal of Control(2020). The code is written in MATLAB.

AutomaticModelSelection

Neural networks and deep learning are changing the way that artificial intelligence is being done. Efficiently choosing a suitable network architecture and fine tuning its hyper-parameters for a specific dataset is a time-consuming task given the staggering number of possible alternatives. In this paper, we address the problem of model selection by means of a fully automated framework for efficiently selecting a neural network model for a selected task, whether it is classification or regression. The algorithm, named Automatic Model Selection, is a modified micro-genetic algorithm that automatically and efficiently finds the most suitable fully connected neural network model for a given dataset. The main contributions of this method are: a simple, list based encoding for neural networks, which will be used as the genotype in our evolutionary algorithm, novel crossover and mutation operators, the introduction of a fitness function that considers the accuracy of the neural network and its complexity, and a method to measure the similarity between two neural networks. AMS is evaluated on two different datasets. By comparing some models obtained with AMS to state-of-the-art models for each dataset we show that AMS can automatically find efficient neural network models. Furthermore, AMS is computationally efficient and can make use of distributed computing paradigms to further boost its performance.