Inclusion of Rotor Dynamics in Controller Design for Helicopters

Abstract

State-feedback-controllers and state-estimators (filters) are designed for the roll-pitch-horizontal motions of a helicopter near hover, using a new quadratic synthesis technique. One model (tenth order) uses a dynamic model of the rotor, whereas the other model (sixth order) assumes the rotor can be tilted instantaneously. It is shown that, for tight control, neglecting the rotor dynamics in designing the autopilot can produce unstable closed-loop response on the model that includes rotor dynamics. Two filters are designed to use only fuselage sensors and two are designed to use both fuselage and rotor sensors. It is shown that rotor states can be estimated with sufficient accuracy using only fuselage sensors so that it does not seem worthwhile to use rotor sensors. The mean square response of the vehicle to a gusty, random wind, using several different filter/state-feedback compensators, is shown to be satisfactory. NomenclatureA = matrix of state weighting coefficients (i.e., weighting of OF = ABF) C = matrix of control gains F = open loop (controls fixed) dynamics matrix G = control distribution matrix H = measurement distribution matrix K = matrix of Kalman filter gains P = covariance matrix of error estimate Q = spectral density matrix of process noise R = spectral density matrix of measurement noise X = mean square response matrix of state responses X = mean square response matrix of state estimates p = roll rate q = pitch rate u, v = longitudinal and lateral velocities of vehicle center of mass u = control vector w = wind vector 6 = pitch angle B c = lateral cyclic pitch 0s = longitudinal cyclic pitch T C -correlation time of wind <p = roll angle T = process noise distribution matrix Subscripts ( )R = rotor state ( )F = fuselage state Superscripts ( Y = matrix of augmented system (J T = matrix transpose (rows and columns interchanged) () = estimated state f)' = error in estimated state

Composite nonlinear H∞ based output feedback controller for an un-crewed helicopter in its hover flight

“…The wind velocity vector, δ = [ u w , v w , w w ] T affecting the vehicle velocity in the body frame is shown in Figure 2. It is modeled by independent exponentially correlated Gauss-Markov processes, with correlation time as 3 s and strength of wind taken as 6 m /s (Hall and Bryson, 1973). The magnitude of wind velocities along y and z axes are considered to be 1/4 th and 1/6 th of the wind velocity along the x -axis.…”

Handling Qualities in Rotorcraft Conceptual Design

“…In fact, it is known from the literature that modeling rotor dynamics may have a relevant effect on the design of control laws procedure [9,11,43]. The model can be formulated in state-space form, with respect to the state and input vectors…”

Dynamic Decoupling Control Optimization for a Small-Scale Unmanned Helicopter

“…The wind turbulence disturbances (W , W , W ), as shown in Figure 8, are also injected to the velocity vector along body frame X B -, Y B -, and Z B -axes. Here, a shaping filter [37]…”