Retrospective Cost Adaptive Control of the NASA GTM Model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83658/1/AIAA-2010-8404-930.pd

A Computational Study of the Performance and Robustness Properties of Retrospective Cost Adaptive Control

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83646/1/AIAA-2010-8011-332.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 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

Model reference adaptive control for nonminimum phase aerospace systems

Adaptive control techniques are often avoided in aerospace systems due to stringent plant structural requirements and validation difficulties. This dissertation seeks to broaden the range of aerospace engineering applications that can utilize an adaptive controller through the development of an extended model reference adaptive control (MRAC) design. First, a partitioned control framework is presented that permits the combined use of an adaptive control law and a nonadaptive control law. The partitioned framework is used to shift full control authority away from the adaptive portion of the system. Next, two MRAC variations that can accommodate the nonminimum phase zeros often seen in aerospace applications are discussed for use as the adaptive system. The parallel feedforward compensator approach proposes inclusion of a user--defied fictitious model in parallel with the plant that is designed to make the plant appear nonminimum phase. The surrogate tracking error approach modifies the typical MRAC structure to handle nonminimum phase plants by requiring knowledge of its nonminimum phase zeros. A tracking error convergence proof is provided for this continuous-time MRAC variant. The partitioned design using the surrogate tracking error approach is applied to the control tasks of an experimental, flexible wing aircraft. A simulation is used to demonstrate much improved flight path angle command tracking when compared to use of the aircraft's existing nonadaptive control law, even in the presence of large--scale modeling error. A second simulation is used to show the design applied to flexible motion control of the same aircraft model and exhibits similarly improved performance.Aerospace Engineerin

Tools for Nonlinear Control Systems Design

This is a brief statement of the research progress made on Grant NAG2-243 titled "Tools for Nonlinear Control Systems Design", which ran from 1983 till December 1996. The initial set of PIs on the grant were C. A. Desoer, E. L. Polak and myself (for 1983). From 1984 till 1991 Desoer and I were the Pls and finally I was the sole PI from 1991 till the end of 1996. The project has been an unusually longstanding and extremely fruitful partnership, with many technical exchanges, visits, workshops and new avenues of investigation begun on this grant. There were student visits, long term.visitors on the grant and many interesting joint projects. In this final report I will only give a cursory description of the technical work done on the grant, since there was a tradition of annual progress reports and a proposal for the succeeding year. These progress reports cum proposals are attached as Appendix A to this report. Appendix B consists of papers by me and my students as co-authors sorted chronologically. When there are multiple related versions of a paper, such as a conference version and journal version they are listed together. Appendix C consists of papers by Desoer and his students as well as 'solo' publications by other researchers supported on this grant similarly chronologically sorted

Nonlinear and sampled data control with application to power systems

Sampled data systems have come into practical importance for a variety of reasons. The earliest of these had primarily to do with economy of design. A more recent surge of interest was due to increase utilization of digital computers as controllers in feedback systems. This thesis contributes some control design for a class of nonlinear system exhibition linear output. The solution of several nonlinear control problems required the cancellation of some intrinsic dynamics (so-called zero dynamics) of the plant under feedback. It results that the so-dened control will ensure stability in closed-loop if and only if the dynamics to cancel are stable. What if those dynamics are unstable? Classical control strategies through inversion might solve the problem while making the closed loop system unstable. This thesis aims to introduce a solution for such a problem. The main idea behind our work is to stabilize the nonminimum phase system in continuous- time and undersampling using zero dynamics concept. The overall work in this thesis is divided into two parts. In Part I, we introduce a feedback control designs for the input-output stabilization and the Disturbance Decoupling problems of Single Input Single Output nonlinear systems. A case study is presented, to illustrate an engineering application of results. Part II illustrates the results obtained based on the Articial Intelligent Systems in power system machines. We note that even though the use of some of the AI techniques such as Fuzzy Logic and Neural Network does not require the computation of the model of the application, but it will still suer from some drawbacks especially regarding the implementation in practical applications. An alternative used approach is to use control techniques such as PID in the approximated linear model. This design is very well known to be used, but it does not take into account the non-linearity of the model. In fact, it seems that control design that is based on nonlinear control provide better performances

Cumulative retrospective cost adaptive control with RLS-based optimization,” in

Abstract-We present a discrete-time adaptive control algorithm that is effective for multi-input, multi-output systems that are either minimum phase or nonminimum phase. The adaptive control algorithm requires limited model information, specifically, the first nonzero Markov parameter and the nonminimum-phase zeros of the transfer function from the control signal to the performance measurement. Furthermore, the adaptive control algorithm is effective for stabilization as well as command following and disturbance rejection, where the command and disturbance spectrum is unknown. The novel aspect of this adaptive controller is the use of a retrospective performance function which is optimized using a recursive leastsquares algorithm