Structured H-Infinity Command and Control-Loop Design for Unmanned Helicopters

Abstract

The aim of this paper is to present rigorous and efficient methods for designing flight controllers for unmanned helicopters that have guaranteed performance, intuitive appeal for the flight control engineer, and prescribed multivariable loop structures. Helicopter dynamics do not decouple as they do for the fixed-wing aircraft case, and so the design of helicopter flight controllers with a desirable and intuitive structure is not straightforward. We use an H 1 output-feedback design procedure that is simplified in the sense that rigorous controller designs are obtained by solving only two coupled-matrix design equations. An efficient algorithm is given for solving these that does not require initial stabilizing gains. An output-feedback approach is given that allows one to selectively close prescribed multivariable feedback loops using a reduced set of the states at each step. At each step, shaping filters may be added that improve performance and yield guaranteed robustness and speed of response. The net result yields an H 1 design with a control structure that has been historically accepted in the flight control community. As an example, a design for stationkeeping and hover of an unmanned helicopter is presented. The result is a stationkeeping hover controller with robust performance in the presence of disturbances (including wind gusts), excellent decoupling, and good speed of response. Nomenclature A = system or plant matrix a s = longitudinal blade angle B = control-input matrix b s = lateral blade angle C = output or measurement matrix D = disturbance matrix D in = inner-loop disturbance matrix D o = outer-loop disturbance matrix dt = disturbance G = nominal plant G s = loop-shaped plant K = static output-feedback gain matrix p = roll rate in the body-frame components Q = state weighting matrix q = pitch rate in the body-frame components R = control weighting matrix r = yaw rate in the body-frame components r fb = yaw-rate feedback U = velocity along the body-frame x axis ut = control input V = velocity along the body-frame y axis W = velocity along the body-frame z axis X = inertial position x axis x in t = inner-loop state vector x o t = outer-loop state vector Y = inertial position y axis y in t = inner-loop output vector y o t = outer-loop output vector Z = inertial position z axis zt = performance output = system L 2 gain in = L 2 gain inner loop o = L 2 gain outer loop = pitch angle = roll angle = yaw angle

State Estimation for a Small Scale Flybar-less Helicopter

“…The main rotor moves when (Elevator and Aileron) servo motors are active [2]. To describe this movement two angles ( ) are introduced. )…”

“…During the past few years, a large number of studies addressed the problem of formulating the dynamic model of different small-scale helicopters. In [2][3][4], a complete six-degrees of freedom dynamic model, with the flapping angles, was introduced for a small f f -scale helicopter. In [5], the generation of forces and moments from the main and tail rotor was derived.…”

Adaptive Fuzzy Sliding Mode Control for a Model-Scaled Unmanned Helicopter

“…However these linear controllers are uncomplicated and reliable, lack of robustness is a major defect of them. Most of linear controllers that have been applied to unmanned helicopters are based on the ∞ approach [7][8][9]. The main advantage of the ∞ approach is its robustness in the presence of model uncertainties and disturbances.…”

Spatial Trajectory Tracking Control of a Fully Actuated Helicopter in Known Static Environment

“…A large number of control techniques have been proposed in the literature for the flight control of helicopters, including sliding mode control [6], H ∞ control [7][8][9][10], backstepping control [11,12], neural network control [13][14][15], and so on. In all of these flight control methods, modelbased control, such as H ∞ control etc., is susceptible to uncertainties and disturbances.…”