Retrospective Cost Adaptive Control of Generic Transport Model Under Uncertainty and Failure

Abstract

η = ground velocity alignment error with a ⇀ Θ, Φ, Ψ = pitch, roll, yaw angles θ = controller coefficients τ = turn rate ϕ = feedback vector I. IntroductionA LONG with aerodynamics, lightweight structures, and propulsion, feedback control is one of the key technologies that enable aviation.Feedback control uses inertial and noninertial sensors to provide measurements of position, velocity, acceleration, attitude, and angular rates to specify thrust and aerodynamic surfaces to apply corrective forces and moments. Closed-loop control enables the operation of autopilots, which can either assist the pilot or assume complete control of aircraft operation.The standard approach to feedback control of aircraft is based on classical control techniques [1][2][3]. With the advent of optimal control methods, state-space-based control techniques have also been successful [4,5]. Although classical optimal control does not account for model uncertainty, aircraft flight control has benefited from advances in robust control [6,7].For conventional aircraft operating under emergency flight conditions, as well as for unconventional aircraft, recent research has focused on adaptive control techniques [8][9][10][11][12][13]. The failure of the Honeywell MH-96 self-adaptive controller used on the X-15-3 was analyzed in [12], and L 1 adaptive control with an uncertain flight envelope has been flight tested on the NASA AirStar scaled aircraft [13].The goal of the present paper is to investigate the performance of an alternative technique for adaptive flight control, specifically, retrospective cost adaptive control (RCAC). RCAC is a direct discrete-time adaptive control technique for stabilization, command following, and disturbance rejection [14]. As a discrete-time approach, RCAC is motivated by the desire to implement control algorithms that operate at the sensor sample rate without the need for controller discretization. This also means that the required modeling information can be estimated based on data sampled at the same rate as the control update.RCAC was originally motivated by the notion of retrospectively optimized control, where past controller coefficients used to generate past control inputs are reoptimized in the sense that if the reoptimized coefficients had been used over a previous window of operation, then the performance would have been better. Unlike signal processing applications such as estimation and identification, however, it is impossible to change past control inputs, and thus the reoptimized controller coefficients are used only to generate the next control input.RCAC was originally developed within the context of active noise control experiments [15]. The algorithm used in [15] is gradient based, where the gradient direction and step size are based on different cost functions. In subsequent work [16], the gradient algorithm was replaced by batch least-squares optimization. In both [15,16], the modeling information is given by Markov parameters (impulse response components) of the openloop transfer function ...

Intelligent trajectory tracking of an aircraft in the presence of internal and external disturbances

“…Fault‐tolerant flight control (FTC) schemes have been studied extensively in the literature . The dynamic model of an aircraft changes significantly after the fault occurrence, which leads to reduced effectiveness of the controllers that are designed based on a nominal model and in some cases the closed‐loop system becomes unstable after the fault occurrence.…”

“…A nominal sliding mode control has been designed in the paper to track the desired altitude and velocity commands, while the actuator faults have been estimated using a backstepping sliding mode observer. A retrospective cost adaptive control scheme has been proposed in the work of Ansari and Bernstein for generic transport model (GTM) under different model uncertainties, external disturbances, and failures. The performance variable should be minimized in each iteration and the system inputs are determined as a function of past system inputs‐outputs.…”

Retrospective Cost Adaptive PID Control of Quadcopter/Fixed-Wing Mode Transition in a VTOL Aircraft

“…Using the NASA GTM model, 4-7 retrospective cost adaptive control (RCAC) was applied to the nominal aircraft as well as unknown off-nominal conditions. 8 RCAC is a direct, discrete-time adaptive control technique for stabilization, command following, and disturbance rejection. 9 As a discrete-time approach, RCAC is motivated by the desire to implement control algorithms that operate at the sensor sample rate without the need for controller discretization.…”

Retrospective Cost Adaptive Control of the Generic Transport Model under Abrupt Faults

“…14 Using the NASA GTM model, retrospective cost adaptive control (RCAC) was applied to the nominal aircraft as well as unknown off-nominal conditions. 1 RCAC is a direct, discrete-time adaptive control technique for stabilization, command following, and disturbance rejection. 15 As a discrete-time approach, RCAC is motivated by the desire to implement control algorithms that operate at the sensor sample rate without the need for controller discretization.…”

“…The goal of the present paper is to expand the study of RCAC in. 1 As in, 1 we interface RCAC with the NASA GTM simulation model. [21][22][23][24] This aircraft model includes an aerodynamic data base, trim function, and interface to facilitate feedback control from realistic sensors to thrust and control surfaces.…”