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We consider the problem of identifying unknown physical processes and estimating inaccessible parameters in the global ionosphere and thermosphere model (GITM). In previous work, we took advantage of developments in the retrospective cost method to estimate unknown thermal conductivity and rate coefficients in a one dimensional column in the atmosphere. In the present work, we extend the application of this method to the identification of unknown inaccessible parameters in 3D GITM, as well as the identification of the unknown subsystem that governs the radiative nitrous oxide cooling process in 1D GITM.

A method is presented to obtain state estimates for a possibly nonminimum-phase system in the presence of unknown harmonic inputs. The method estimates the states and reconstructs the unknown harmonic input. An adaptive feedback model injects an input into the estimator such that the error between the estimator output and the actual output converges to zero despite the presence of the unknown harmonic input. Using input reconstruction based on a retrospective cost, the unknown harmonic input is reconstructed. Using the reconstructed input, the parameters of the adaptive feedback system are updated using recursive least squares. Results are presented for a rigid body, a damped rigid body, and a 2D missile with a three-loop autopilot topology.

We consider the problem of estimating the unknown solar driver F10.7 and physical states in the ionosphere and thermosphere using retrospective cost adaptive state estimation (RCASE). We interface RCASE with the Global Ionosphere Thermosphere Model (GITM) to demonstrate state estimation and F10.7 input reconstruction. We further examine the various factors that affect F10.7 estimation including saturation limits, initial estimates, and RCASE tuning parameters.

We apply an extension of retrospective cost adaptive control (RCAC) to a commandfollowing problem for uncertain Hammerstein systems. In particular, RCAC with a NARMAX controller strucuture is applied to linear systems cascaded with input nonlinearities. We assume that one Markov parameter of the linear plant is known. RCAC also uses knowledge of the monotonicity properties of the input nonlinearity. The goal is to determine whether RCAC with a NARMAX controller structure can improve the command-following performance compared to the linear RCAC controller.

In this paper we apply extensions of retrospective cost adaptive control (RCAC) to a 2D missile model considered in prior papers as a benchmark test of adaptive control methods. The dynamics of the missile are highly nonlinear, and instantaneous linearizations are nonminimum phase due to nose sensing and tail actuation. The results that we present in this paper show that the RCAC controller provides results that are comparable to a highly tuned autopilot based on aerodynamic modeling, whereas the RCAC controller uses no knowledge of the missile’s aerodynamics. These results significantly improve the results obtained on the same problem using an earlier version of RCAC, presented at the 2010 GNC.

We apply retrospective cost adaptive control (RCAC) to spacecraft attitude control. First, we develop results for angular rate control. These results are then extended to attitude control. We examine two problems for each of the controllers. For both problems, the spacecraft has an arbitrary initial angular rate, and in the case of attitude control, an arbitrary initial attitude. The objective for the first problem is to bring the body to rest and to a specified attitude in the attitude control case. The second problem seeks to bring the spacecraft to spin about a specified body axis, which, in the case of attitude control, is inertially pointed. We first test the algorithm using an estimate of the spacecraft’s Markov parameters obtained from discretization of the linearized Euler’s and Poisson’s equations. Then, we limit the dependence on knowledge of the mass properties by removing inertia information from the Markov parameter. Finally, we test for robustness by scaling the Markov parameter and rotating the actuator matrix.

We develop a multi-input, multi-output direct adaptive controller for discrete-time, possibly nonminimum-phase, systems with unknown nonminimum-phase zeros. The adaptive controller requires limited modeling information about the system, specifically, Markov parameters from the control input to the performance variables. Often, only a single Markov parameter is required, even in the nonminimum-phase case. We demonstrate the algorithm on command-following and disturbance-rejection problems, where the command and disturbance spectra are unknown. This controller is based on a retrospective performance objective, where the controller is updated using either batch or recursive least squares.

We develop a method for obtaining state estimates for a possibly nonminimum-phase system in the presence of an unknown harmonic input. We construct a state estimator based on the system model, and then introduce an estimator input provided by an adaptive feedback model whose goal is to drive the estimated output to the measured output despite the presence of the unknown harmonic input. Using input reconstruction based on a retrospective surrogate cost, we reconstruct the unknown harmonic input. Using the reconstructed input we update the parameters of the adaptive model using recursive least squares identification. We then extend the method to nonlinear systems. The performance of this method is compared with the Kalman filter for linear examples, as well as with the extended and unscented Kalman filters for nonlinear examples.

Data-based model refinement improves the fidelity of a model based on empirical observations. The model refinement structure is a closed-loop system consisting of an initial model, which is assumed to be known, and a feedback correction, which is unknown. We take advantage of the similarity between the feedback structure of model refinement and adaptive feedback control by replacing the unknown correction term with a retrospectivecost-based adaptive control law. The adaptive control law thus estimates the unknown subsystem. We investigate scenarios in which the initial model is derived from physical principles, such as mass-spring-damper systems, in order to obtain estimates of stiffness and damping coefficients. Furthermore, we identify dynamic subsystems for a linear initial model and a nonlinear initial model.

A cumulative retrospective cost adaptive control algorithm is augmented to a gain-scheduled three loop autopilot to assess performance in a nonlinear three-degree-of-freedom missile model. The cumulative retrospective cost adaptive controller requires minimal model information; specifically estimates of the relative degree, 1st nonzero Markov parameter, and nonminimum phase zeros of the linearized missile model. A rate gyro sensor failure is applied to the model and the performance of the control techniques is investigated through an analysis of air-to-air intercept scenarios. The results indicate that an adaptive controller can, to some degree, improve the missile’s ability to engage a target when such a failure occurs. Nomenclature m Missile mass Iyy Missile Inertia g Acceleration due to gravity X;Z Inertial position in the X-Z plane U Inertial velocity component along the body frame x-axis of the center of gravity (CG) W Inertial velocity component along the body frame z-axis of the CG Vm Magnitude of missile velocity M Mach number Az;P Acceleration component along body frame z-axis at pointP along the body x-axis Angle of attack Pitch angle q Body rate Air density Sref Reference area dref Reference length Cx Aerodynamic force coefficient along the body frame x-axis Cz Aerodynamic force coefficient along the body frame z-axis Cm Aerodynamic moment coefficient along the body y-axis at the CG q Dynamic pressure p Tail fin angle T Thrust along body frame x-axis