Liyan Wen

Aircraft Turbulence Compensation Using Adaptive Multivariable Disturbance Rejection Techniques

Dδe = aerodynamic drag derivative with respect to elevator deflection angle Lp, Np = roll and yaw moment derivatives with respect to roll rate Lr, Nr = roll and yaw moment derivatives with respect to yaw rate Lr, Lβ = aerodynamic roll moment derivative with respect to yaw rate and sideslip angle Lα, Dα, Tα = aerodynamic lift, drag, and thrust derivatives with respect to angle of attack L_ α = aerodynamic lift derivative with respect to rate of change angle of attack Lβ, Nβ = roll and yaw moment derivatives with respect to sideslip angle Lδe ,Mδe = aerodynamic roll and pitch moment derivatives with respect to elevator deflection angle L0, D0 = benchmark aerodynamic lift and drag derivatives with respect to angle of attack MV ,Mq = aerodynamic pitch moment derivative with respect to aircraft flight speed V and pitch rate Mα,M _ α = aerodynamic pitch moment derivative with respect to α and _ α m, g = mass of the aircraft, gravity acceleration Nδa, Lδa = yaw and roll moment derivatives with respect to aileron deflection angle Nδr, Lδr = yaw and roll moment derivatives with respect to rudder deflection angle p, q, r = body axis components of the angular velocity pW , qW , rW = local air mass angular velocities resulting from the wind gradients T, δT , TδT = engine thrust, throttle, and aerodynamic thrust derivative with respect to the throttle TV , DV , LV = thrust, aerodynamic drag, and aerodynamic lift derivatives with respect to airspeed V u; v; w uA; vA;wA = body axis components of VA uK, vK, wK = body axis components of VK V, VA, VK = airspeed (norm of VA), aircraft flight velocity, and aircraft track velocity VW ; uW , vW , wW = local turbulence velocity and body axis components of VW V0, α0, γ0 = benchmark flight velocity, angle of attack, and flight-path angle Yβ, Yp, Yr = side force derivatives with respect to sideslip angle, roll rate, yaw rate Yδa, Yδr = side force derivatives with respect to aileron deflection angle and rudder deflection angle α, β = angle of attack and sideslip angle αK , βK = angle of attack and sideslip angle due to VK αW , βW = angle of attack and sideslip angle due to VW θ, φ, ψ = Euler pitch, roll, and yaw angle σ = angle between thrust and body xb axis

An adaptive disturbance rejection control scheme for multivariable nonlinear systems

ABSTRACT An adaptive disturbance rejection control scheme is developed for uncertain multi-input multi-output nonlinear systems in the presence of unmatched input disturbances. The nominal output rejection scheme is first developed, for which the relative degree characterisation of the control and disturbance system models from multivariable nonlinear systems is specified as a key design condition for this disturbance output rejection design. The adaptive disturbance rejection control design is then completed by deriving an error model in terms of parameter errors and tracking error, and constructing adaptive parameter-updated laws and adaptive parameter projection algorithms. All closed-loop signals are guaranteed to be bounded and the plant output tracks a given reference output asymptotically despite the uncertainties of system and disturbance parameters. The developed adaptive disturbance rejection scheme is applied to turbulence compensation for aircraft fight control. Simulation results from a benchmark aircraft model verify the desired system performance.

Parameterization and Adaptive Control of Multivariable Noncanonical T--S Fuzzy Systems

This paper conducts a new study for adaptive Takagi–Sugeno (T–S) fuzzy approximation-based control of multi-input and multi-output (MIMO) noncanonical-form nonlinear systems. Canonical-form nonlinear systems have explicit relative degree structures, whose approximation models can be directly used to derive desired parameterized controllers. Noncanonical-form nonlinear systems usually do not have such a feature, nor do their approximation models, which are also in noncanonical forms. This paper shows that it is desirable to reparameterize noncanonical-form T–S fuzzy system models with smooth membership functions for adaptive control, and such system reparameterization can be realized using relative degrees, a concept yet to be studied for MIMO noncanonical-form T–S fuzzy systems. This paper develops an adaptive feedback linearization scheme for control of such general system models with uncertain parameters, by first deriving various relative degree structures and normal forms for such systems. Then, a reparameterization procedure is developed for such system models, based on which adaptive control designs are derived, with desired stability and tracking properties analyzed. A detailed example is presented with simulation results to show the new control design procedure and desired control system performance.

Adaptive turbulence compensation for aircraft flight control

In this paper, a set of adaptive turbulence compensation techniques are investigated for aircraft flight control systems with different control gain matrices A(x), for which the multivariable adaptive disturbance rejection problems are formulated: one is for a nonsingular A(x), and one is for a singular A(x). Two unmatched disturbance rejection control design problems are addressed for the proposed aircraft turbulence compensation study: one with static feedback linearization under a direct control relative degree condition, and one with dynamic feedback linearization under an input extension control relative degree condition. For each problem, both nominal and adaptive turbulence compensation designs are developed for aircraft systems with known parameters and with unknown parameters, respectively. A typical aircraft application with turbulence disturbances is studied by a specific simulation study.

Adaptive LQ control based actuator failure compensation

An adaptive failure compensation control scheme is developed based on linear quadratic (LQ) control for discrete-time possibly nonminimum phase systems with unknown actuator failures with characterizations that some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants. The failure compensation control problem is formulated as a new LQ control problem. Based on some results on new controller structures and design conditions, a stable adaptive law is proposed for the estimation of uncertain parameters from both the plant and actuator-failures. The adaptive failure compensation controller is implemented with adaptive parameter estimates, for achieving closed-loop stability and output regulation. Simulation results are presented to verify the desired adaptive control performance in the presence of uncertain actuator failures.

An LQ control based actuator failure compensation scheme for possibly nonminimum phase systems

In this paper, an LQ control based actuator failure compensation control scheme is developed for possibly nonminimum phase systems with actuator failures. The failure compensation control problem is formulated as a new LQ control problem for systems with failure disturbances. A new disturbance rejection problem for systems with unmatched disturbances is solved through an LQ based control algorithm to ensure the maximum reduction of the disturbance effect on the system output. New controller structures and design conditions are derived using dynamic programming in the presence of actuator failures, and state feedback and observer-based feedback designs are presented. Simulation results are presented to verify the desired control system performance in the presence of actuator failures.

Adaptive turbulence compensation for multivariable nonlinear aircraft models

In this paper, a multivariable adaptive disturbance rejection scheme is developed for solving the nonlinear aircraft turbulence compensation problem. A nominal design for output rejection of unmatched input disturbances is first constructed based on feedback linearization, for which the relative degree characterization of the control and disturbance system models is specified as a key design condition for turbulence compensation designs. Adaptive disturbance rejection control is then completed by deriving an error model in terms of parameter errors and tracking error, and constructing an adaptive law for updating the controller parameters. All closed-loop signals are guaranteed to be bounded and the plant output tracks a given reference output asymptotically despite the uncertainties of system and disturbance parameters. A nonlinear turbulence compensation design is studied for an aircraft system model, and simulation results from this benchmark aircraft model verify the desired system performance.