Quadcopter: Design, modelling, control and trajectory tracking

A quadcopter is a type of unmanned aerial vehicles (UAV). The industry of this type of UAVs is growing exponentially in terms of new technology development and the increase of potential applications that may cover construction inspections, search and rescue, surveillance, aerial photography, monitoring, mapping, etc. A quadcopter is a nonlinear and under-actuated system that introduces complex aerodynamics properties and create challenges which demands the development of new, reliable and effective control techniques to enhance the stability of flight control, plan and track a desired trajectory while minimizing the effect induced by the operational environment and its own sensors. Hence, many control techniques have been developed and researched. Some of such developments work well with the provision of having an accurate mathematical model of the system while other work is associated with a mathematical model that can accommodate certain level of wind disturbances and uncertainties related to measurement noise. Moreover, various linear, nonlinear and intelligent control techniques were developed and recognized in the literature. Each one of such control techniques has some aspect that excels in under certain conditions. The focus of this thesis is to develop different control techniques that can improve flight control stability, trajectory tracking of a quadcopter and evaluate their performance to select the best suitable control technique that can realize the stated technical flight control requirements. Accordingly, three main techniques have been developed: Standard PID, Fuzzy based control technique that tune PID parameters in real time (FPID) and a Hybrid control strategy that consists of three control techniques: (a) FPID with state coordinates transformation (b) State feedback (c) Sliding mode The configuration of the hybrid control strategy consists of two control loops. The inner control loop aims to control the quadcopter\u27s attitude and altitude while the outer control loop aims to control the quadcopter\u27s position. Two configurations were used to configure the developed control techniques of the control loops. These configurations are: (a) A sliding mode control is used for the outer loop while for the inner loop two control techniques are used to realize it: a Fuzzy gain scheduled PID with state coordinates transformation and a state feedback control. (b) Fuzzy gain scheduled PID control is used for the outer loop while for the inner loop two control techniques are used to realize it using the same formation as in (a) above. Furthermore, in order to ensure a feasible desired trajectory before tracking it, a trajectory planning algorithm has been developed and tested successfully. Subsequently, a simulation testing environment with friendly graphical User Interface (GUI) has been developed to simulate the quadcopter mathematical model and then to use it as a test bed to validate the developed control techniques with and without the effect of wind disturbance and measurement noise. The quadcopter with each control technique has been tested using the simulation environment under different operational conditions. The results in terms of tracking a desired trajectory shows the robustness of the first configuration of control techniques within the hybrid control strategy under the presence of wind disturbance and measurement noise compared to all the other techniques developed. Then, the second configuration of the control techniques came second in terms of results quality. The third and fourth results in the sequence shown by the fuzzy scheduled PID and the standard PID respectively. Finally, Validating the simulation results on a real system, a quadcopter has been successfully designed, implemented and tested. The developed control techniques were tested using the implemented quadcopter and the results were demonstrated and compared with the simulation results

Bio inspired techniques for simultaneous design of multiple optimal power system stabilizers

Bio-inspired techniques are fields of study that are inspired from topics of connectionism, social behavior and emergence. Researchers have ventured into the intricacies involved with the techniques and devised algorithms based on their study. Such techniques are the focus of this thesis. The two bio-inspired techniques used for simultaneous design of power system stabilizers (PSSs) in this study are - Particle Swam Optimization (PSO) and Bacteria Foraging Algorithm (BFA). The work in this thesis is presented in three papers as follows: Paper 1 -This paper introduces an improved PSO called Small Population based PSO (SPPSO) with less number of particles and unique regeneration concept. The efficacy of the algorithm is evaluated for the simultaneous design of power system stabilizers (PSSs) on the two-area and 16 machine power systems. Paper 2 - The second paper presents a new algorithm - Bacterial Foraging Algorithm (BFA) for simultaneous tuning of multiple PSSs on a 16 machine power system. The variants of the BFA like the run length and the swarming are explored for better performance for two different design techniques and the results are compared. Paper 3 - The third paper compares SPPSO and BFA towards simultaneous tuning of multiple PSSs on two-area and Nigerian power system. This paper presents both algorithms as a first step towards online optimization and proposes to implement these algorithms in real power systems in near future --Abstract, page iv

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

Control system design using fuzzy gain scheduling of PD with Kalman filter for railway automatic train operation

The development of train control systems has progressed towards following the rapid growth of railway transport demands. To further increase the capacity of railway systems, Automatic Train Operation (ATO) systems have been widely adopted in metros and gradually applied to mainline railways to replace drivers in controlling the movement of trains with optimised running trajectories for punctuality and energy saving. Many controller design methods have been studied and applied in ATO systems. However, most researchers paid less attention to measurement noise in the development of ATO control system, whereas such noise indeed exists in every single instrumentation device and disturbs the process output of ATO. Thus, this thesis attempts to address such issues. In order to overcome measurement error, the author develops Fuzzy gain scheduling of PD (proportional and derivative) control assisted by a Kalman filter that is able to maintain the train speed within the specified trajectory and stability criteria in normal and noisy conditions due to measurement noise. Docklands Light Railway (DLR) in London is selected as a case study to implement the proposed idea. The MRes project work is summarised as follows: (1) analysing literature review, (2) modelling the train dynamics mathematically, (3) designing PD controller and Fuzzy gain scheduling, (4) adding a Gaussian white noise as measurement error, (5) implementing a Kalman filter to improve the controllers, (6) examining the entire system in an artificial trajectory and a real case study, i.e. the DLR, and (7) evaluating all based on strict objectives, i.e. a ±3% allowable error limit, a punctuality limit of no later and no earlier than 30 seconds, Integrated Absolute Error (IAE) and Integrated Squared Error (ISE) performances. The results show that Fuzzy gain scheduling of PD control can cope well with the examinations in normal situations. However, such discovery is not found in noisy conditions. Nevertheless, after the introduction to Kalman filter, all control objectives are then satisfied in not only normal but also noisy conditions. The case study implemented using DLR data including on the route from Stratford International to Woolwich Arsenal indicates a satisfactory performance of the designed controller for ATO systems

Automatic Control of the Weld Bead Geometry

Automatic control of the welding process is complex due to its nonlinear and stochastic behavior and the difficulty for measuring the principal magnitudes and closing the control loop. Fusion welds involve melting and subsequent solidification of one or more materials. The geometry of the weld bead is a good indicator of the melting and solidification process, so its control is essential to obtain quality junctions. Different sensing, modeling, estimation, and control techniques are used to overcome this challenge, but most of the studies are using static single-input/single-output models of the process and focusing on the flat welding position. However, theory and practice demonstrate that dynamic models are the best representation to obtain satisfactory control performance, and multivariable techniques reduce the effect of interactions between control loops in the process. Also, many industrial applications need to control orbital welding. In this chapter, the above topics are discussed

Integration of Active Chassis Control Systems for Improved Vehicle Handling Performance

This thesis investigates the principle of integration of vehicle dynamics control systems by proposing a novel control architecture to integrate the brake-based electronic stability control (ESC), active front steering (AFS), normal suspension force control (NFC) and variable torque distribution (VTD). A nonlinear 14 degree of freedom passive vehicle dynamics model was developed in Matlab/Simulink and validated against commercially available vehicle dynamics software CarSim. Dynamics of the four active vehicle control systems were developed. Fuzzy logic and PID control strategies were employed considering their robustness and effectiveness in controlling nonlinear systems. Effectiveness of active systems in extending the vehicle operating range against the passive ones was investigated. From the research, it was observed that AFS is effective in improving the stability at lower lateral acceleration (latac) region with less interference to the longitudinal vehicle dynamics. But its ability diminishes at higher latac regions due to tyre lateral force saturation. Both ESC and VTD are found to be effective in stabilising the vehicle over the entire operating region. But the intrusive nature of ESC promotes VTD as a preferred stability control mechanism at the medium latac range. But ESC stands out in improving stability at limits where safety is of paramount importance. NFC is observed to improve the ability to generate the tyre forces across the entire operating range. Based on this analysis, a novel rule based integrated chassis control (ICC) strategy is proposed. It uses a latac based stability criterion to assign the authority to control the stability and ensures the smooth transition of the control authority amongst the three systems, AFS, VTD and ESC respectively. The ICC also optimises the utilisation of NFC to improve the vehicle handling performance further, across the entire operating regions. The results of the simulation are found to prove that the integrated control strategy improves vehicle stability across the entire vehicle operating region

MATLAB

A well-known statement says that the PID controller is the "bread and butter" of the control engineer. This is indeed true, from a scientific standpoint. However, nowadays, in the era of computer science, when the paper and pencil have been replaced by the keyboard and the display of computers, one may equally say that MATLAB is the "bread" in the above statement. MATLAB has became a de facto tool for the modern system engineer. This book is written for both engineering students, as well as for practicing engineers. The wide range of applications in which MATLAB is the working framework, shows that it is a powerful, comprehensive and easy-to-use environment for performing technical computations. The book includes various excellent applications in which MATLAB is employed: from pure algebraic computations to data acquisition in real-life experiments, from control strategies to image processing algorithms, from graphical user interface design for educational purposes to Simulink embedded systems

Improving off-road vehicle lateral stability with integrated chassis control

Dissertation (MSc (Engineering))--University of Pretoria, 2022.This study investigates the improvement of off-road vehicle lateral stability by integrated control of active rear steering (ARS) and rear differential braking (RDB) and how the performance of such systems compares on smooth and rough roads. The ARS and RDB controllers each comprise a sliding mode controller (SMC) for which the choice of reference model, SMC gain and integration rule are key design choices. Findings include that the kinematic model reference error is a preferred reference model over the phase plane location error on both terrains, the SMC gain is terrain dependant, and rear axle slip angle is a preferred integration rule over the stability index (SI) on both terrains. The study also found that RDB, and to a lesser degree ARS, tends to improve on the baseline vehicle path following ability for a double lane change (DLC) manoeuvre on both terrains, but RDB has a larger loss of speed compared to ARS. Rear axle slip angle was found to be a terrain dependant tuneable integration rule to combine ARS and RDB, and resulted in a control system that has the good path following ability of RDB but low loss of speed associated with ARS after tuning.Mechanical and Aeronautical EngineeringMEngUnrestricte