Development of Robust Control Laws for Disturbance Rejection in Rotorcraft UAVs

Inherent stability inside the flight envelope must be guaranteed in order to safely introduce private and commercial UAV systems into the national airspace. The rejection of unknown external wind disturbances offers a challenging task due to the limited available information about the unpredictable and turbulent characteristics of the wind. This thesis focuses on the design, development and implementation of robust control algorithms for disturbance rejection in rotorcraft UAVs. The main focus is the rejection of external disturbances caused by wind influences. Four control algorithms are developed in an effort to mitigate wind effects: baseline nonlinear dynamic inversion (NLDI), a wind rejection extension for the NLDI, NLDI with adaptive artificial neural networks (ANN) augmentation, and NLDI with L1 adaptive control augmentation. A simulation environment is applied to evaluate the performance of these control algorithms under external wind conditions using a Monte Carlo analysis. Outdoor flight test results are presented for the implementation of the baseline NLDI, NLDI augmented with adaptive ANN and NLDI augmented with L1 adaptive control algorithms in a DJI F330 Flamewheel quadrotor UAV system. A set of metrics is applied to compare and evaluate the overall performance of the developed control algorithms under external wind disturbances. The obtained results show that the extended NLDI exhibits undesired characteristics while the augmentation of the baseline NLDI control law with adaptive ANN and L1 output-feedback adaptive control improve the robustness of the translational and rotational dynamics of a rotorcraft UAV in the presence of wind disturbances

Communication system improvement with control performance based on link quality in wireless sensor actuator networks

New communication and networking paradigms started with wireless sensor actuator networks (WSANs) to introduce new applications. One of these is the automatic gain control system (AGC). It will enable a high degree of the decentralized and mobile control. In this study, neural networks (NN) with fuzzy logic (one of the techniques of artificial intelligence (AI)) is used to enhance the control performance depending on the link quality. The NN and fuzzy inference system (FIS) with Mamdani’s method used to build a model reference, adaptive controller, for recompensing for delay time packets losses, and improving the reliability of WSAN. Between 88.62% and 99.99%, validation data is obtained for the medium and high conditions of operation with the proposed algorithm. Experimental and simulation results show a promising approach

Journal of Telecommunications and Information Technology, 2003, nr 3

kwartalni

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book

ANFIS Based Data Rate Prediction For Cognitive Radio

Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. A cognitive radio system participates in a continuous process, the ‘‘cognition cycle”, during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type, etc.) expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms utilize information from measurements sensed from the environment, gathered experience and stored knowledge and guide in decision making. This thesis introduces and evaluates learning schemes that are based on adaptive neuro-fuzzy inference system (ANFIS) for predicting the capabilities (e.g. data rate) that can be achieved by a specific radio configuration in cognitive radio. First a ANFIS based scheme is proposed. The work reported here is compare previous neural network based learning schemes. Cognitive radio is a intelligent emergent technology, where learning schemes are needed to assist in its functioning. ANFIS based scheme is one of the good learning Artificial intelligence method, that combines best features of neural network and fuzzy logic. Here ANFIS and neural networks methods are able to assist a cognitive radio system to help in selecting the best one radio configuration to operate in. Performance metric like RMSE, prediction accuracy of ANFIS learning has been used as performance index

Modelling of propagation path loss using adaptive hybrid artificial neural network approach for outdoor environments.

Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal. Durban, 2018.Prediction of signal power loss between transmitter and receiver with minimal error is an important issue in telecommunication network planning and optimization process. Some of the basic available conventional models in literature for signal power loss prediction includes the Free space, Lee, COST 234 Hata, Hata, Walficsh- Bertoni, Walficsh-Ikegami, dominant path and ITU models. But, due to poor prediction accuracy and lack of computational efficiency of these traditional models with propagated signal data in different cellular network environments, many researchers have shifted their focus to the domain of Artificial Neural Networks (ANNs) models. Different neural network architectures and models exist in literature, but the most popular one among them is the Multi-Layer Perceptron (MLP) ANN which can be attributed to its superb architecture and comparably clear algorithm. Though standard MLP networks have been employed to model and predict different signal data, they suffer due to the following fundamental drawbacks. Firstly, conventional MLP networks perform poorly in handling noisy data. Also, MLP networks lack capabilities in dealing with incoherence datasets which contracts with smoothness. Firstly, in this work, an adaptive neural network predictor which combines MLP and Adaptive Linear Element (ADALINE) is developed for enhanced signal power prediction. This is followed with a resourceful predictive model, built on MLP network with vector order statistic filter based pre-processing technique for improved prediction of measured signal power loss in different micro-cellular urban environments. The prediction accuracy of the proposed hybrid adaptive neural network predictor has been tested and evaluated using experimental field strength data acquired from Long Term Evolution (LTE) radio network environment with mixed residential, commercial and cluttered building structures. By means of first order statistical performance evaluation metrics using Correlation Coefficient, Root Mean Squared Error, Standard Deviation and Mean Absolute Error, the proposed adaptive hybrid approach provides a better prediction accuracy compared to the conventional MLP ANN prediction approach. The superior performance of the hybrid neural predictor can be attributed to its capability to learn, adaptively respond and predict the fluctuating patterns of the reference propagation loss data during training

Dynamic Data Assimilation

Data assimilation is a process of fusing data with a model for the singular purpose of estimating unknown variables. It can be used, for example, to predict the evolution of the atmosphere at a given point and time. This book examines data assimilation methods including Kalman filtering, artificial intelligence, neural networks, machine learning, and cognitive computing