Adaptive control with convex saturation constraints

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166259/1/cth21096.pd

Retrospective Cost Adaptive Control for Systems with Unknown Nonminimum-Phase Zeros

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90721/1/AIAA-2011-6203-626.pd

Adaptive Control of a Seeker-Guided 2D Missile with Unmodeled Aerodynamics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97113/1/AIAA2012-4617.pd

Robust Sampled-Data Adaptive Control of the Rohrs Counterexamples

Abstract-We revisit the Rohrs counterexamples within the context of sampled-data adaptive control. In particular, retrospective cost adaptive control (RCAC) is applied to the sampled continuous-time plant with unmodeled high-frequency dynamics, which involves nonminimum-phase (NMP) sampling zeros. It is shown that, without knowledge of these NMP zeros, RCAC stabilizes the uncertain plant and asymptotically follows the sinusoidal command

Retrospective‐cost‐based adaptive model refinement for the ionosphere and thermosphere

Mathematical models of physical phenomena are of critical importance in virtually all applications of science and technology. This paper addresses the problem of how to use data to improve the fidelity of a given model. We approach this problem using retrospective cost optimization, which uses data to recursively update an unknown subsystem interconnected to a known system. Applications of this technique are relevant to applications that depend on large‐scale models based on first‐principles physics, such as the global ionosphere–thermosphere model (GITM). Using GITM as the truth model, we demonstrate that measurements can be used to identify unknown physics. Specifically, we estimate static thermal conductivity parameters, as well as a dynamic cooling process. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 446–458, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86874/1/10127_ftp.pd

Adaptive Control of a Seeker-Guided 2D Missile with Unmodeled Aerodynamics

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. = Aerodynamic force coefficient along the body frame x-axis C z = Aerodynamic force coefficient along the body frame z-axis C m = Aerodynamic moment coefficient along the body y-axis at the CḠ q = Dynamic pressure δ p = Tail fin angle T = Thrust along the body frame x-axi

Adaptive Output Feedback Control of the NASA GTM Model with Unknown Nonminimum-Phase Zeros

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90722/1/AIAA-2011-6204-387.pd

Adaptive Input Reconstruction with Application to Model Refinement, State Estimation, and Adaptive Control.

Input reconstruction is the process of using the output of a system to estimate its input. In some cases, input reconstruction can be accomplished by determining the output of the inverse of a model of the system whose input is the output of the original system. Inversion, however, requires an exact and fully known analytical model, and is limited by instabilities arising from nonminimum-phase zeros. The main contribution of this work is a novel technique for input reconstruction that does not require model inversion. This technique is based on a retrospective cost, which requires a limited number of Markov parameters. Retrospective cost input reconstruction (RCIR) does not require knowledge of nonminimum-phase zero locations or an analytical model of the system. RCIR provides a technique that can be used for model refinement, state estimation, and adaptive control. In the model refinement application, data are used to refine or improve a model of a system. It is assumed that the difference between the model output and the data is due to an unmodeled subsystem whose interconnection with the modeled system is inaccessible, that is, the interconnection signals cannot be measured and thus standard system identification techniques cannot be used. Using input reconstruction, these inaccessible signals can be estimated, and the inaccessible subsystem can be fitted. We demonstrate input reconstruction in a model refinement framework by identifying unknown physics in a space weather model and by estimating an unknown film growth in a lithium ion battery. The same technique can be used to obtain estimates of states that cannot be directly measured. Adaptive control can be formulated as a model-refinement problem, where the unknown subsystem is the idealized controller that minimizes a measured performance variable. Minimal modeling input reconstruction for adaptive control is useful for applications where modeling information may be difficult to obtain. We demonstrate adaptive control of a seeker-guided missile with unknown aerodynamics.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91520/1/amdamato_1.pd

Extensions of Retrospective Cost Adaptive Control: Nonsquare Plants, and Robustness Modifications.

Controllers that have adjustable parameters, and an update law for adjusting these parameters, are called ``adaptive controllers''. Adaptive controllers typically entail assumptions about the dynamic order, relative degree or transmission zeros of the system, and may not be applicable to systems that are not positive real, passive, or minimum phase. In 1980s, the fragility of adaptive controllers to violations in these assumptions has been demonstrated through various counterexamples. This motivated the development of robustness modifications for adaptive controllers, which is the main goal of this dissertation. In this dissertation, we focus on retrospective cost adaptive control (RCAC), which is a direct, digital adaptive control algorithm. RCAC is applicable to MIMO, nonminimum-phase (NMP) systems, but it assumes that the NMP zeros of the plant, if any, are known. The main contribution of this work includes theory and analysis for retrospective cost adaptive control of nonsquare systems and development of a modified, robust RCAC update law for maintaining stability and convergence in the presence of unmodeled NMP zeros.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97926/1/dogan_1.pd