Sliding Mode Control and Vision-Based Line Tracking for Quadrotors

This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition.This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition

Sliding Mode Control and Vision-Based Line Tracking for Quadrotors

This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition.This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition

Sliding Mode Control and Vision-Based Line Tracking for Quadrotors

This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition.This thesis describes the design of Sliding Mode Control applied to quadrotor UAV flight. This is a nonlinear control technique in which a discontinuous control signal is applied to drive the so-called sliding variable to zero, which defines the sliding surface. The sliding variable should be designed in such a way that approaching the sliding surface is beneficial to tracking the reference signals. The advantages of Sliding Mode Control are that the need for simplifying the underlying dynamical model through linearization is avoided, it is robust and adaptive, and works even if the system to be controlled is highly nonlinear or has model uncertainties. Sliding Mode Control has one major issue associated with it, namely the chattering phenomena in the control inputs, which is undesirable. This can be tackled by approximating the discontinuous sign function in the control input with a approximated continuous function, or by applying techniques such as adaptive fuzzy gain scheduling. As with other control methods, Sliding Mode Control requires tuning of the control parameters to obtain an optimal performance. In this work, genetic algorithms were investigated as a way to tune the controller parameters. The findings of this thesis were combined with the design of a line tracking algorithm in order to enter the MathWorks Minidrone Competition

Fault Diagnosis and Fault-Tolerant Control of Unmanned Aerial Vehicles

With the increasing demand for unmanned aerial vehicles (UAVs) in both military and civilian applications, critical safety issues need to be specially considered in order to make better and wider use of them. UAVs are usually employed to work in hazardous and complex environments, which may seriously threaten the safety and reliability of UAVs. Therefore, the safety and reliability of UAVs are becoming imperative for development of advanced intelligent control systems. The key challenge now is the lack of fully autonomous and reliable control techniques in face of different operation conditions and sophisticated environments. Further development of unmanned aerial vehicle (UAV) control systems is required to be reliable in the presence of system component faults and to be insensitive to model uncertainties and external environmental disturbances. This thesis research aims to design and develop novel control schemes for UAVs with consideration of all the factors that may threaten their safety and reliability. A novel adaptive sliding mode control (SMC) strategy is proposed to accommodate model uncertainties and actuator faults for an unmanned quadrotor helicopter. Compared with the existing adaptive SMC strategies in the literature, the proposed adaptive scheme can tolerate larger actuator faults without stimulating control chattering due to the use of adaptation parameters in both continuous and discontinuous control parts. Furthermore, a fuzzy logic-based boundary layer and a nonlinear disturbance observer are synthesized to further improve the capability of the designed control scheme for tolerating model uncertainties, actuator faults, and unknown external disturbances while preventing overestimation of the adaptive control parameters and suppressing the control chattering effect. Then, a cost-effective fault estimation scheme with a parallel bank of recurrent neural networks (RNNs) is proposed to accurately estimate actuator fault magnitude and an active fault-tolerant control (FTC) framework is established for a closed-loop quadrotor helicopter system. Finally, a reconfigurable control allocation approach is combined with adaptive SMC to achieve the capability of tolerating complete actuator failures with application to a modified octorotor helicopter. The significance of this proposed control scheme is that the stability of the closed-loop system is theoretically guaranteed in the presence of both single and simultaneous actuator faults

Robust Nonlinear Tracking Control for Unmanned Aircraft in the Presence of Wake Vortex

The flight trajectory of unmanned aerial vehicles (UAVs) can be significantly affected by external disturbances such as turbulence, upstream wake vortices, or wind gusts. These effects present challenges for UAV flight safety. Hence, addressing these challenges is of critical importance for the integration of unmanned aerial systems (UAS) into the National Airspace System (NAS), especially in terminal zones. This work presents a robust nonlinear control method that has been designed to achieve roll/yaw regulation in the presence of unmodeled external disturbances and system nonlinearities. The data from NASA-conducted airport experimental measurements as well as high-fidelity Large Eddy Simulations of the wake vortex are used in the study. Side-by-side simulation comparisons between the robust nonlinear control law and both linear H∞ role= presentation style= box-sizing: border-box; max-height: none; display: inline; line-height: normal; font-size: 13.2px; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; color: rgb(34, 34, 34); font-family: Arial, Arial, Helvetica, sans-serif; position: relative; \u3eH∞�∞ and PID control laws are provided for completeness. These simulations are focused on applications involving small UAV affected by the wake vortex disturbance in the vicinity of the ground (which models the take-off or landing phase) as well as in the out-of-ground zone. The results demonstrate the capability of the proposed nonlinear controller to asymptotically reject wake vortex disturbance in the presence of the nonlinearities in the system (i.e., parametric variations, unmodeled, time-varying disturbances). Further, the nonlinear controller is designed with a computationally efficient structure without the need for the complex calculations or function approximators in the control loop. Such a structure is motivated by UAV applications where onboard computational resources are limited

MODELING AND INTELLIGENT CONTROL OF A DRONE

This thesis tackles the modeling, design, and control of a Quadrotor unmanned aerial vehicle, with a focus on intelligent control and smart applications such as obstacle avoidance, robust trajectory tracking, visual soft landing, and disturbance compensation. It details the mathematical modeling opted for the simulation and the control. Furthermore, It describes the classic control methodology for both linear and nonlinear control techniques with interpreted simulations; The methodology is subsequently applied to develop an open-source autonomous quadrotor miniature model. In addition, advanced control theory has been applied using Adaptive Linear Quadratic Gaussian, Model predictive control, and intelligent Radial basis functions neural network for the robust tracking of generated trajectory for either obstacle avoidance or bio-inspired soft landing on a specially designed landing pad. The thesis depicts as well the adaptive optimal observation by an enhanced Kalman filter combined with Madgwick sensor’s data fuse. Control laws were mainly either mathematically derived or adaptively generated based on stability analysis using Lyapunov theory, The simulation incorporated several analytical comparisons to prove efficiency and compare the performance

Advances and Trends in Mathematical Modelling, Control and Identification of Vibrating Systems

This book introduces novel results on mathematical modelling, parameter identification, and automatic control for a wide range of applications of mechanical, electric, and mechatronic systems, where undesirable oscillations or vibrations are manifested. The six chapters of the book written by experts from international scientific community cover a wide range of interesting research topics related to: algebraic identification of rotordynamic parameters in rotor-bearing system using finite element models; model predictive control for active automotive suspension systems by means of hydraulic actuators; model-free data-driven-based control for a Voltage Source Converter-based Static Synchronous Compensator to improve the dynamic power grid performance under transient scenarios; an exact elasto-dynamics theory for bending vibrations for a class of flexible structures; motion profile tracking control and vibrating disturbance suppression for quadrotor aerial vehicles using artificial neural networks and particle swarm optimization; and multiple adaptive controllers based on B-Spline artificial neural networks for regulation and attenuation of low frequency oscillations for large-scale power systems. The book is addressed for both academic and industrial researchers and practitioners, as well as for postgraduate and undergraduate engineering students and other experts in a wide variety of disciplines seeking to know more about the advances and trends in mathematical modelling, control and identification of engineering systems in which undesirable oscillations or vibrations could be presented during their operation

PSO BASED TAKAGI-SUGENO FUZZY PID CONTROLLER DESIGN FOR SPEED CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR

A permanent magnet synchronous motor (PMSM) is one kind of popular motor. They are utilized in industrial applications because their abilities included operation at a constant speed, no need for an excitation current, no rotor losses, and small size. In the following paper, a fuzzy evolutionary algorithm is combined with a proportional-integral-derivative (PID) controller to control the speed of a PMSM. In this structure, to overcome the PMSM challenges, including nonlinear nature, cross-coupling, air gap flux, and cogging torque in operation, a Takagi-Sugeno fuzzy logic-PID (TSFL-PID) controller is designed. Additionally, the particle swarm optimization (PSO) algorithm is developed to optimize the membership functions' parameters and rule bases of the fuzzy logic PID controller. For evaluating the proposed controller's performance, the genetic algorithm (GA), as another evolutionary algorithm, is incorporated into the fuzzy PID controller. The results of the speed control of PMSM are compared. The obtained results demonstrate that although both controllers have excellent performance; however, the PSO based TSFL-PID controller indicates more superiority