Robust Control of Multivariable and Large Scale Systems.

Packard, Andrew; Doyle, John C.

doi:10.21236/ada194250

Cited by 16 publications

(4 citation statements)

References 14 publications

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“…Robustness analysis on perturbed systems has been investigated by various researchers (Doyle 1982, Packard 1988, Balas and Doyle 1994). The augmented system model (equations ( 11)-( 13)) can be represented as the interconnection of a linear fractional transform, as shown in figure 4.…”

Section: Structured Singular Value (µ) Synthesismentioning

confidence: 99%

A coupled robust control/optimization approach for active-passive hybrid piezoelectric networks

Tsai

Wang²

2002

Smart Mater. Struct.

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Active-passive hybrid piezoelectric networks (APPN) have been shown to be very effective in structural control. However, the issue of system robustness of such configurations has yet to be investigated. To ensure that the APPN concept still performs well under uncertainties, a coupled robust control (µ synthesis)/optimization approach is proposed to determine the active and passive parameters simultaneously with the consideration of system uncertainties. An analysis is performed to gain insight into both passive and active-passive hybrid systems. Through µ synthesis, it is also illustrated that the APPN approach can tolerate a much higher level of uncertainty than a purely active system.

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Section: Structured Singular Value (µ) Synthesismentioning

confidence: 99%

A coupled robust control/optimization approach for active-passive hybrid piezoelectric networks

Tsai

Wang²

2002

Smart Mater. Struct.

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“…By exploiting the structure of the state matrices in (A.7), the uncertainty is formulated into a LFT by achieving the smallest possible number of repeated blocks [27]. With this purpose in mind, the matrix J m is formed as…”

Section: Discussionmentioning

confidence: 99%

“…the state-space matrices A, B, C and D of the model are not a function of time. Therefore, we choose to model the time-varying system (27) as a nominal time-invariant model plus a bounded uncertain component. We then seek to design a feedback controller to stabilize the closed-loop system for all allowable uncertain perturbations, i.e.…”

Section: System Manipulation Into a Control Framework And Feedback Co...mentioning

confidence: 99%

Toroidal current profile control during low confinement mode plasma discharges in DIII-D via first-principles-driven model-based robust control synthesis

Barton¹,

Boyer²,

Shi³

et al. 2012

Nucl. Fusion

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In order for ITER to be capable of operating in advanced tokamak operating regimes, characterized by a high fusion gain, good plasma confinement, magnetohydrodynamic stability and a non-inductively driven plasma current, for extended periods of time, several challenging plasma control problems still need to be solved. Setting up a suitable toroidal current density profile in the tokamak is key for one possible advanced operating scenario characterized by non-inductive sustainment of the plasma current. At the DIII-D tokamak, the goal is to create the desired current profile during the ramp-up and early flat-top phases of the plasma discharge and then actively maintain this target profile for the remainder of the discharge. The evolution in time of the toroidal current profile in tokamaks is related to the evolution of the poloidal magnetic flux profile, which is modelled in normalized cylindrical coordinates using a first-principles, nonlinear, dynamic partial differential equation (PDE) referred to as the magnetic diffusion equation. The magnetic diffusion equation is combined with empirical correlations developed from physical observations and experimental data from DIII-D for the electron temperature, the plasma resistivity and the non-inductive current drive to develop a simplified, control-oriented, nonlinear, dynamic PDE model of the poloidal flux profile evolution valid for low confinement mode discharges. In this work, we synthesize a robust feedback controller to reject disturbances and track a desired reference trajectory of the poloidal magnetic flux gradient profile by employing the control-oriented model of the system. A singular value decomposition of the static gain matrix of the plant model is utilized to identify the most relevant control channels and is combined with the dynamic response of system around a given operating trajectory to design the feedback controller. A general framework for real-time feedforward + feedback control of magnetic and kinetic plasma profiles was implemented in the DIII-D Plasma Control System and was used to demonstrate the ability of the feedback controller to control the toroidal current profile evolution in the DIII-D tokamak. These experiments constitute the first time ever a first-principles-driven, model-based, closed-loop magnetic profile controller was successfully implemented and tested in a tokamak device.

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“…This is the model framework which applies to the Hoo/structured singular value design procedure. For more detail on the matching design methodology see [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Experimentally Based Calculation of Robust Control Perturbation Models

Smith

1994

IFAC Proceedings Volumes

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Robust Control of Multivariable and Large Scale Systems.

Cited by 16 publications

References 14 publications

A coupled robust control/optimization approach for active-passive hybrid piezoelectric networks

A coupled robust control/optimization approach for active-passive hybrid piezoelectric networks

Toroidal current profile control during low confinement mode plasma discharges in DIII-D via first-principles-driven model-based robust control synthesis

Experimentally Based Calculation of Robust Control Perturbation Models

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