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

UltraSwarm: A Further Step Towards a Flock of Miniature Helicopters

We describe further progress towards the development of a MAV (micro aerial vehicle) designed as an enabling tool to investigate aerial flocking. Our research focuses on the use of low cost off the shelf vehicles and sensors to enable fast prototyping and to reduce development costs. Details on the design of the embedded electronics and the modification of the chosen toy helicopter are presented, and the technique used for state estimation is described. The fusion of inertial data through an unscented Kalman filter is used to estimate the helicopter’s state, and this forms the main input to the control system. Since no detailed dynamic model of the helicopter in use is available, a method is proposed for automated system identification, and for subsequent controller design based on artificial evolution. Preliminary results obtained with a dynamic simulator of a helicopter are reported, along with some encouraging results for tackling the problem of flocking

Evolution of Neural Networks for Helicopter Control: Why Modularity Matters

The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so

Flight control design for small-scale helicopter using disturbance observer based backstepping

Flight control design for small-scale helicopter using disturbance observer based backsteppin

Explicit non-linear model predictive control for autonomous helicopters

Trajectory tracking is a basic function required for autonomous helicopters, but it also poses challenges to control design due to the complexity of helicopter dynamics. This article introduces an explicit model predictive control (MPC) to solve this problem, which inherits the advantages of non-linear MPC but eliminates time-consuming online optimization. The explicit solution to the non-linear MPC problem is derived using Taylor expansion and exploiting the helicopter model. With the explicit MPC solution, the control signals can be calculated instantaneously to respond to the fast dynamics of helicopters and suppress disturbances immediately. On the other hand, the online optimization process can be removed from the MPC framework, which can accelerate the software development and simplify onboard hardware. Due to these advantages of the proposed method, the overall control framework has a low complexity and high reliability, and it is easy to deploy on small-scale helicopters. The proposed explicit non-linear MPC has been successfully validated in simulations and in actual flight tests using a Trex-250 small-scale helicopter

Piecewise constant model predictive control for autonomous helicopters

This paper introduces an optimisation based control framework for autonomous helicopters. The framework contains a high-level model predictive control (MPC) and a low-level linear controller. The proposed MPC works in a piecewise constant fashion to reduce the computation burden and to increase the time available for performing online optimisation. The linear feedback controller responds to fast dynamics of the helicopter and compensates the low bandwidth of the high-level controller. This configuration allows the computationally intensive algorithm applied on systems with fast dynamics. The stability issues of the high-level MPC and the overall control scheme are discussed. Simulations and flight tests on a small-scale helicopter are carried out to verify the proposed control scheme

The application of nonlinear control theory to robust helicopter flight control.

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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