Comments on ‘output feedback adaptive command following and disturbance rejection for nonminimum phase uncertain dynamical systems’

We provide numerical examples and analysis to show that the adaptive controller given by Theorem 3.1 of Yucelen et al. 1 may fail to stabilize plants under the stated conditions. Copyright © 2011 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83465/1/1235_ftp.pd

Markov-parameter-based adaptive control of 3-axis angular velocity in a six-degree-of-freedom Stewart platform

Abstract-Stewart platforms are complex mechanical devices used throughout industry for vibration testing and precision pointing applications. These platforms are nonlinear, strongly coupled MIMO systems. For a six-degree-of-freedom Stewart platform, we consider the problem of three-degree-of-freedom angular-velocity command following. Static nonlinearity inherent in the platform is analyzed, and a closed-loop setup for adaptive command-following control is described. A review of the Markov-parameter-based adaptive control algorithm is given, along with the OKID system identification algorithm, test procedures, and experimental results

Retrospective Cost Adaptive Flow Control Using a Dielectric Barrier Discharge Actuator

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76651/1/AIAA-2009-5857-706.pd

Adaptive Flow Control of Low Reynolds Number Aerodynamics Using a Dielectric Barrier Discharge Actuator

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77022/1/AIAA-2009-378-858.pd

Adaptive Control Based on Retrospective Cost Optimization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83558/1/AIAA-46741-507.pd

Adaptive Control Based on Retrospective Cost Optimization.

This dissertation studies adaptive control of multi-input, multi-output, linear, time-invariant, discrete-time systems that are possibly unstable and nonminimum phase. We consider both gradient-based adaptive control as well as retrospective-cost-based adaptive control. Retrospective cost optimization is a measure of performance at the current time based on a past window of data and without assumptions about the command or disturbance signals. In particular, retrospective cost optimization acts as an inner loop to the adaptive control algorithm by modifying the performance variables based on the difference between the actual past control inputs and the recomputed past control inputs based on the current control law. We develop adaptive control algorithms that are effective for systems that are nonminimum phase. We consider discrete-time adaptive control since these control laws can be implemented directly in embedded code without requiring an intermediate discretization step. Furthermore, the adaptive controllers in this dissertation are developed under minimal modeling assumptions. In particular, the adaptive controllers require knowledge of the sign of the high-frequency gain and a sufficient number of Markov parameters to approximate the nonminimum-phase zeros (if any). No additional modeling information is necessary. The adaptive controllers presented in this dissertation are developed for full-state-feedback stabilization, static-output-feedback stabilization, as well as dynamic compensation for stabilization, command following, disturbance rejection, and model reference adaptive control. Lyapunov-based stability and convergence proofs are provided for special cases. We present numerical examples to illustrate the algorithms' effectiveness in handling systems that are unstable and/or nonminimum phase and to provide insight into the modeling information required for controller implementation.Ph.D.Aerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63826/1/santillo_1.pd