Advanced UAVs Nonlinear Control Systems and Applications

Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB®/Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results

Quasi and fully sensorless speed control of indirect RFOC induction motor drives for low speed operation

For high performance drive applications, the accuracy of speed estimation from a digital shaft encoder is reduced in the low speed range. Drive performance can be further impaired at low speed when low cost low-resolution digital encoders are used. This thesis contributes to the development of a quasi-sensorless solution for AC induction motor drives, where instead of a conventional position sensor and MH" or closed loop observer, an artificial intelligence based 64PPR SKF sensor bearing is proposed. Furthermore, the performance of speed-controlled IM drives is sensitive to rotor time constant mismatch. A newly developed closed loop RMLE-GNA observer is proposed as a new adaption mechanism with a Model Reference Adaptive Scheme for rotor flux estimation and on-fine rotor time constant adaptation. For high performance sensorless speed controlled IM drive applications, the IM is required to operate at low speed in all four-quadrants in a stable manner whilst maintaining constant air-gap flux and torque. Such low speed operation fails with conventional open loop observers and MRAS, and closed loop observers such as EKF suffer from a tuning problem and extensive computing time. Parameter uncertainty is a further factor limiting drive performance. A new joint state estimation and parameter adaptation closed loop Recursive Maximum Likelihood Estimator with an iterative Gauss Newton Raphson Algorithm (RMLE-GNA) is developed for the sensorless speed estimation and on-line parameter adaptation of IM drives. Two case studies are presented: correct and stable convergence of the closed loop observer within two iteration local loops utilising a full order IM model, and correct and stable convergence within a single iteration local loop utilising a simplified full order IM model. In both cases it is shown that the closed loop observer can provide satisfactory and stable convergence for 4-quadrant operation. Of particular interest are the correct convergence, robustness against parameter mismatch and the stability of the sensorless speed controlled IM drive at zero speed and zero stator flux frequency. Thus, a small signal transfer function of the closed loop observer with the sensorless speed vector controlled IM drives is derived. The stability analysis shows the robustness and the convergence of the observer for 4-quadrant operation for high and low speed' and up to the rated load torque. The effect of various machine model parameters on the stability and convergence of the RMLE-GNA-based sensorless drive system is investigated, providing an insight into the inherent dynamic characteristics of the proposed closed loop observer-based sensorless speed-controlled IM drives.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

UKF-based enhancement and ROS implementation of 4-WDDMR localization with advanced control system

This article exhibits integration of nonholonomic four-wheeled differential drive mobile robot platform (4-WDDMR) implemented using an Arduino Mega 2560 microcontroller and robot operating system. Particularly, estimating the current situation of the robot navigation system is complex thanks to uncertainty exerted by the robot incorporated with actuators complexities and nonlinearities. An efficient and accurate estimation technique which applies probabilistic algorithm based Unscented Kalman Filter is proposed. The stability of 4-WDDMR Control-Navigation system has been tested and improved based on Lyapunov criterion. The proposed techniques are implemented and evaluated using MATLAB/SIMULINK®. Both practical and simulation results demonstrate the vitality of the proposed Control-Navigation approach. It is believed that the proposed approaches will help to build an autonomous robotics system that can assist humans with their works in daily life basis

Design and implementation of auto car driving system with collision avoidance

This paper presents designing and manufacturing the hardware and software of a low-cost robotic system, which can be installed into any petrol car with an automatic gearbox, giving the ability to a car to be driverless. The proposed approach is composed of three systems; first, a computer vision system detects the lane located ahead of the vehicle to provide a reference angle for the steering wheel; second, to make the proper decision, a control and collision avoidance system are implemented, which are responsible for collecting sensors data and perform the right action by applying a closed-loop control system; third, the electro-mechanical system, which physically controls the steering wheel, braking and accelerating of the vehicle by actuators. The proposed system has been tested on-ground in several experiments and returned with acceptable results

A portable Raspberry Pi-based system for diagnosis of heart valve diseases using automatic segmentation and artificial neural networks

This study proposes a Raspberry Pi-based system for the diagnosis of heart valve diseases as a primary tool to improve the diagnostic accuracy of physicians. The proposed system is able to detect and classify nine common valvular heart cases encompassing eight types of heart valve diseases as well as the normal case of valves. The design and development of the proposed system are mainly divided into two phases, namely development of a disease classification approach, and design and implementation of the diagnostic hardware system. The developed disease classification approach is comprised of five stages, namely obtaining phonocardiogram (PCG) signals, preprocessing, segmentation using a proposed automatic algorithm, feature extraction in three domains (time, frequency, and wavelet decomposition domains) and classification using a backpropagation neural network. The hardware of the diagnostic system consists of a PCG signal acquisition module connected to a processing and displaying unit, which is represented by a Raspberry Pi connected to a touch screen. Where the developed disease classification approach is implemented in the software of the Raspberry Pi to enable it to detect the diseases in real time and fully automatically. The proposed system was clinically tested on 50 real subjects encompassing the nine cases. The performance of the diagnostic system is obtained with an accuracy of 96%, sensitivity of 95.23%, and specificity of 100%