Adaptive fuzzy control method for a single tilt tricopter

[[abstract]]This article proposes an adaptive fuzzy gain scheduling (FSG) design of the traditional proportional integral derivative (PID) control method by using fuzzy logic rules to schedule controlled gains at different phases. Owing to minimization of the tracking error of the controller design using three parameters and the integral of time weighted-squared error (ITSE) minima criterion of the controller design process, the fuzzy rules of the triangular membership functions are exploited online to verify the PID controller gains in different operated scheduling modes. For that reason, the controller designs can be used to tune the system models during the whole operation time period to enable efficient error tracking. The continuous genetic algorithm (GA) is considered an innovation because in it, the decode chromosome step is totally neglected. Owing to this improvement, it is superior to the standard GA because it requires less storage and enables naturally faster convergence. In this research, the controlled parameters were optimized using the continuous GA to enhance the efficiency of the proposed method. Thereafter, it was implemented to a single tilt Tricopter model to test whether the control performance is better when compared with the conventional PID control method.[[notice]]補正完

Modeling and Robust Control of Flying Robots Using Intelligent Approaches Modélisation et commande robuste des robots volants en utilisant des approches intelligentes

This thesis aims to modeling and robust controlling of a flying robot of quadrotor type. Where we focused in this thesis on quadrotor unmanned Aerial Vehicle (QUAV). Intelligent nonlinear controllers and intelligent fractional-order nonlinear controllers are designed to control. The QUAV system is considered as MIMO large-scale system that can be divided on six interconnected single-input–single-output (SISO) subsystems, which define one DOF, i.e., three-angle subsystems with three position subsystems. In addition, nonlinear models is considered and assumed to suffer from the incidence of parameter uncertainty. Every parameters such as mass, inertia of the system are assumed completely unknown and change over time without prior information. Next, basing on nonlinear, Fractional-Order nonlinear and the intelligent adaptive approximate techniques a control law is established for all subsystems. The stability is performed by Lyapunov method and getting the desired output with respect to the desired input. The modeling and control is done using MATLAB/Simulink. At the end, the simulation tests are performed to that, the designed controller is able to maintain best performance of the QUAV even in the presence of unknown dynamics, parametric uncertainties and external disturbance

Automatic Landing of a Rotary-Wing UAV in Rough Seas

Rotary-wing unmanned aerial vehicles (RUAVs) have created extensive interest in the past few decades due to their unique manoeuverability and because of their suitability in a variety of flight missions ranging from traffic inspection to surveillance and reconnaissance. The ability of a RUAV to operate from a ship in the presence of adverse winds and deck motion could greatly extend its applications in both military and civilian roles. This requires the design of a flight control system to achieve safe and reliable automatic landings. Although ground-based landings in various scenarios have been investigated and some satisfactory flight test results are obtained, automatic shipboard recovery is still a dangerous and challenging task. Also, the highly coupled and inherently unstable flight dynamics of the helicopter exacerbate the difficulty in designing a flight control system which would enable the RUAV to attenuate the gust effect. This thesis makes both theoretical and technical contributions to the shipboard recovery problem of the RUAV operating in rough seas. The first main contribution involves a novel automatic landing scheme which reduces time, cost and experimental resources in the design and testing of the RUAV/ship landing system. The novelty of the proposed landing system enables the RUAV to track slow-varying mean deck height instead of instantaneous deck motion to reduce vertical oscillations. This is achieved by estimating the mean deck height through extracting dominant modes from the estimated deck displacement using the recursive Prony Analysis procedure. The second main contribution is the design of a flight control system with gust-attenuation and rapid position tracking capabilities. A feedback-feedforward controller has been devised for height stabilization in a windy environment based on the construction of an effective gust estimator. Flight tests have been conducted to verify its performance, and they demonstrate improved gust-attenuation capability in the RUAV. The proposed feedback-feedforward controller can dynamically and synchronously compensate for the gust effect. In addition, a nonlinear H1 controller has been designed for horizontal position tracking which shows rapid position tracking performance and gust-attenuation capability when gusts occur. This thesis also contains a description of technical contributions necessary for a real-time evaluation of the landing system. A high-infedlity simulation framework has been developed with the goal of minimizing the number of iterations required for theoretical analysis, simulation verification and flight validation. The real-time performance of the landing system is assessed in simulations using the C-code, which can be easily transferred to the autopilot for flight tests. All the subsystems are parameterized and can be extended to different RUAV platforms. The integration of helicopter flight dynamics, flapping dynamics, ship motion, gust effect, the flight control system and servo dynamics justifies the reliability of the simulation results. Also, practical constraints are imposed on the simulation to check the robustness of the flight control system. The feasibility of the landing procedure is confimed for the Vario helicopter using real-time ship motion data

Multi-agent Collision Avoidance Using Interval Analysis and Symbolic Modelling with its Application to the Novel Polycopter

Coordination is fundamental component of autonomy when a system is defined by multiple mobile agents. For unmanned aerial systems (UAS), challenges originate from their low-level systems, such as their flight dynamics, which are often complex. The thesis begins by examining these low-level dynamics in an analysis of several well known UAS using a novel symbolic component-based framework. It is shown how this approach is used effectively to define key model and performance properties necessary of UAS trajectory control. This is demonstrated initially under the context of linear quadratic regulation (LQR) and model predictive control (MPC) of a quadcopter. The symbolic framework is later extended in the proposal of a novel UAS platform, referred to as the ``Polycopter" for its morphing nature. This dual-tilt axis system has unique authority over is thrust vector, in addition to an ability to actively augment its stability and aerodynamic characteristics. This presents several opportunities in exploitative control design. With an approach to low-level UAS modelling and control proposed, the focus of the thesis shifts to investigate the challenges associated with local trajectory generation for the purpose of multi-agent collision avoidance. This begins with a novel survey of the state-of-the-art geometric approaches with respect to performance, scalability and tolerance to uncertainty. From this survey, the interval avoidance (IA) method is proposed, to incorporate trajectory uncertainty in the geometric derivation of escape trajectories. The method is shown to be more effective in ensuring safe separation in several of the presented conditions, however performance is shown to deteriorate in denser conflicts. Finally, it is shown how by re-framing the IA problem, three dimensional (3D) collision avoidance is achieved. The novel 3D IA method is shown to out perform the original method in three conflict cases by maintaining separation under the effects of uncertainty and in scenarios with multiple obstacles. The performance, scalability and uncertainty tolerance of each presented method is then examined in a set of scenarios resembling typical coordinated UAS operations in an exhaustive Monte-Carlo analysis