Modeling and Robust Attitude Controller Design for a Small Size Helicopter

This paper addresses the design and application controller for a small-size unmanned aerial vehicle (UAV). In this work, the main objective is to study the modeling and attitude controller design for a small size helicopter. Based on a non-simplified helicopter model, a new robust attitude control law, which is combined with a nonlinear control method and a model-free method, is proposed in this paper. Both wind gust and ground effect phenomena conditions are involved in this experiment and the result on a real helicopter platform demonstrates the effectiveness of the proposed control algorithm and robustness of its resultant controller.Comment: 6 page

Homography-based pose estimation to guide a miniature helicopter during 3D-trajectory tracking

This work proposes a pose-based visual servoing control, through using planar homography, to estimate the position and orientation of a miniature helicopter relative to a known pattern. Once having the current flight information, the nonlinear underactuated controller presented in one of our previous works, which attends all flight phases, is used to guide the rotorcraft during a 3Dtrajectory tracking task. In the sequel, the simulation framework and the results obtained using it are presented and discussed, validating the proposed controller when a visual system is used to determine the helicopter pose information.Fil: Brandão, Alexandre . Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; BrasilFil: Sarapura, Jorge Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; ArgentinaFil: Sarcinelli Filho, Mario . Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; BrasilFil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina. Universidad Nacional de San Juan; Argentin

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Hybrid active force control for fixed based rotorcraft

Disturbances are considered major challenges faced in the deployment of rotorcraft unmanned aerial vehicle (UAV) systems. Among different types of rotorcraft systems, the twin-rotor helicopter and quadrotor models are considered the most versatile flying machines nowadays due to their range of applications in the civilian and military sectors. However, these systems are multivariate and highly non-linear, making them difficult to be accurately controlled. Their performance could be further compromised when they are operated in the presence of disturbances or uncertainties. This dissertation presents an innovative hybrid control scheme for rotorcraft systems to improve disturbance rejection capability while maintaining system stability, based on a technique called active force control (AFC) via simulation and experimental works. A detailed dynamic model of each aerial system was derived based on the Euler–Lagrange and Newton-Euler methods, taking into account various assumptions and conditions. As a result of the derived models, a proportional-integral-derivative (PID) controller was designed to achieve the required altitude and attitude motions. Due to the PID's inability to reject applied disturbances, the AFC strategy was incorporated with the designed PID controller, to be known as the PID-AFC scheme. To estimate control parameters automatically, a number of artificial intelligence algorithms were employed in this study, namely the iterative learning algorithm and fuzzy logic. Intelligent rules of these AI algorithms were designed and embedded into the AFC loop, identified as intelligent active force control (IAFC)-based methods. This involved, PID-iterative learning active force control (PID-ILAFC) and PID-fuzzy logic active force control (PID-FLAFC) schemes. To test the performance and robustness of these proposed hybrid control systems, several disturbance models were introduced, namely the sinusoidal wave, pulsating, and Dryden wind gust model disturbances. Integral square error was selected as the index performance to compare between the proposed control schemes. In this study, the effectiveness of the PID-ILAFC strategy in connection with the body jerk performance was investigated in the presence of applied disturbance. In terms of experimental work, hardware-in-the-loop (HIL) experimental tests were conducted for a fixed-base rotorcraft UAV system to investigate how effective are the proposed hybrid PID-ILAFC schemes in disturbance rejection. Simulated results, in time domains, reveal the efficacy of the proposed hybrid IAFC-based control methods in the cancellation of different applied disturbances, while preserving the stability of the rotorcraft system, as compared to the conventional PID controller. In most of the cases, the simulated results show a reduction of more than 55% in settling time. In terms of body jerk performance, it was improved by around 65%, for twin-rotor helicopter system, and by a 45%, for quadrotor system. To achieve the best possible performance, results recommend using the full output signal produced by the AFC strategy according to the sensitivity analysis. The HIL experimental tests results demonstrate that the PID-ILAFC method can improve the disturbance rejection capability when compared to other control systems and show good agreement with the simulated counterpart. However, the selection of the appropriate learning parameters and initial conditions is viewed as a crucial step toward this improved performance

Wind Preview-Based Model Predictive Control of Multi-Rotor UAVs Using LiDAR

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview’s timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts