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

D’Amato, Anthony; Sumer, E. Dogan; Bernstein, Dennis S.

doi:10.2514/6.2011-6203

Cited by 16 publications

(26 citation statements)

References 10 publications

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“…In [12], RCAC is extended to remove the need to know the NMP zeros, as well as to reduce the number of required Markov parameters. In particular, it is shown in [12] that in many cases, a single nonzero Markov parameter suffices to achieve convergence of the adaptive controller.…”

Section: Introductionmentioning

confidence: 99%

Robustness of retrospective cost adaptive control to Markov-parameter uncertainty

Sumer

D’Amato

Морозов

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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In this paper we investigate the robustness of an extended version of retrospective cost adaptive control (RCAC), in which less modeling information is required than in prior versions of this method. RCAC is applicable to MIMO possibly nonminimum-phase (NMP) plants without the need to know the locations of the NMP zeros. The only required modeling information is an FIR approximation of the plant, which may be based on a limited number of Markov parameters. In this paper we investigate the effect of phase mismatch between the true plant and the FIR approximation. Numerical examples demonstrate the relationship between phase mismatch at the command and disturbance frequencies as well as the required level of regularization in the controller update.

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Section: Introductionmentioning

confidence: 99%

Robustness of retrospective cost adaptive control to Markov-parameter uncertainty

Sumer

D’Amato

Морозов

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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“…We define our system using this state vector and the rewritten error dynamics in (33) and (34) combined with the reaction wheel dynamics in (3). We linearize the system by computing a Jacobian about the equilibrium x T e = ω T er T e ν T e T .…”

Section: A Baseline Markov Parametermentioning

confidence: 99%

Adaptive spacecraft attitude control with reaction wheel actuation

Cruz

Bernstein

2013

2013 American Control Conference

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We apply retrospective cost adaptive control (RCAC) to spacecraft attitude control using reaction-wheel actuators. The controller is applied to the motion-to-rest (M2R) maneuver, where the spacecraft has an arbitrary initial attitude and angular-velocity and the objective is to bring the body to rest at a specified inertial attitude. First, we show that RCAC can complete the M2R maneuver using full information of the mass properties of the spacecraft. Then, we remove the spacecraft and reaction-wheel inertia information from the controller and examine the closed-loop settling time for offnominal spacecraft inertias. Thus, the controller uses limited knowledge of the system. Finally, the algorithm is tested in the presence of disturbances, unknown actuator misalignment, and noise and bias in the angular-velocity measurement.

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“…The algorithm in [6] requires knowledge of a limited number of Markov parameters of the plant, and thus simplifies earlier versions of RCAC described in [7][8][9]. Therefore, the algorithm in [6] improves the model refinement technique described in [1,4,10]. Furthermore, the present paper encompasses multiple versions of the model refinement problem, including system emulation and subsystem identification.…”

Section: Introductionmentioning

confidence: 98%

“…First, the model refinement algorithm described in Section II is based on the extension of the retrospective cost adaptive control (RCAC) algorithm described in [6]. The algorithm in [6] requires knowledge of a limited number of Markov parameters of the plant, and thus simplifies earlier versions of RCAC described in [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Retrospective-cost-based model refinement for system emulation and subsystem identification

Морозов

Ali

D’Amato

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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We consider the problem of data-based model refinement, where we assume the availability of an initial model, which may incorporate both physical laws and empirical observations. With this initial model as a starting point, our goal is to use additional measurements to refine the model. In particular, components of the model that are poorly modeled can be updated, thereby resulting in a higher fidelity model. We consider two special cases, namely, system emulation and subsystem identification. In the former case, the main system is assumed to be uncertain and we seek an estimate of the unknown subsystem that allows the overall model to approximate the true system. In this case, there is no expectation that the constructed subsystem model approximates the unknown subsystem. In the latter case, we assume that the main system is accurately modeled and we seek an estimate of the unknown subsystem that approximates the unknown subsystem.

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

Cited by 16 publications

References 10 publications

Robustness of retrospective cost adaptive control to Markov-parameter uncertainty

Robustness of retrospective cost adaptive control to Markov-parameter uncertainty

Adaptive spacecraft attitude control with reaction wheel actuation

Retrospective-cost-based model refinement for system emulation and subsystem identification

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