Aggressive Attitude Control of Unmanned Rotor Helicopters Using a Robust Controller

Abstract

In this paper a robust controller is proposed for unmanned helicopters. The mathematical model of the helicopter is a multi-input, multi-output (MIMO) system with nonlinearities, parameter uncertainties, coupling effects, and external disturbances. A novel robust controller, which includes a nominal controller and a robust compensator, is proposed for obtaining robust attitude tracking performance in pitch and roll channels, respectively. The nominal controller is designed to achieve desired tracking performance for the nominal model, and the robust compensator design is based on robust signal compensation technology for restraining the effects of external disturbances, parameter uncertainties, nonlinearities and couplings. The proposed controller is linear, time invariant, and easy to implement. The robust property of the system is analyzed. It is proved that robust attitude tracking performance can be achieved. Experiments were carried out on a prototype unmanned helicopter THeli260, which included simulation evaluation and flight test under aggressive maneuvers. The results of the experiment exhibit advanced performance of the robust controller.

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“…Block (kh (1) , ss (1) ) Convolutional Block (kh (2) , ss (2) ) Convolutional Block (kh (n) , ss (n) ) Fully connected layer (V) Convolutional Block (kh (1) , ss (1) ) Convolutional Block (kh (2) , ss (2) ) Convolutional Block (kh (n) , ss (n) ) Fully connected layer (V) Each convolutional block consists of two different layers, a convolutional layer and a subsample layer, with their respectively hyper-parameters. The first is the fundamental part of this algorithm [48], in it the convolution operation is carried out between the input of the convolutional block φ (ℓ−1) and the filters of this layer κ (ℓ) which theirs size are 3, thus generating the feature maps χ (ℓ) as follows:…”

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