Investigation of Cumulative Retrospective Cost Adaptive Control for Missile Application

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83634/1/AIAA-2010-7577-578.pd

Retrospective-Cost Adaptive Control of Uncertain Hammerstein Systems Using a NARMAX Controller Structure

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97107/1/AIAA2012-4448.pd

Retrospective Cost Optimization for Adaptive State Estimation, Input Estimation, and Model Refinement

AbstractRetrospective cost optimization was originally developed for adaptive control. In this paper, we show how this technique is applicable to three distinct but related problems, namely, state estimation, input estimation, and model refinement. To illustrate these techniques, we give two examples. In the first example, retrospective cost model refinement is used with synthetic data to estimate the cooling dynamics that are missing from a model of the ionosphere-thermosphere. In the second example, retrospective cost adaptive state estimation is used with data from a satellite to estimate a solar driver in the ionosphere- thermosphere, with performance gauged by using data from a second satellite

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

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

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

Robust hybrid estimation and rejection of multi-frequency signals

We consider the problem of output regulation for LTI systems in the presence of unknown exosystems. The knowledge about the multi-frequency signals exosystem consists in the maximum number of frequencies and their maximal value. The control scheme relies on two main components: an estimation algorithm, to reconstruct the signal generated by the exosystem, and a controller, to enforce the output regulation property to the closed-loop system. To tackle the first task, we propose a hybrid observer for the estimation of the (possibly piece-wise continuous) number and values of the frequencies contained in the exogenous signal. The hybrid observer is particularly appealing for numerical implementations, and it is combined with a self-tuning algorithm of the free parameters (gains), thus improving its performance even in case of noisy measurements. Semi-global exponential convergence of the estimation error is provided. As far as the second task is concerned, a robust hybrid regulator is designed for practical rejection of the multi-frequency disturbance signal acting on the plant. The result is achieved by exploiting the frequencies estimated by the hybrid observer. The effectiveness of the proposed control scheme is shown by means of numerical simulations

Adaptive Control Based on Retrospective Cost Optimization

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