Validation of plasma current profile model predictive control in tokamaks via simulations

Ouarit, Hicham; Brémond, S.; Nouailletas, R.; Witrant, Emmanuel; Autrique, Laurent

doi:10.1016/j.fusengdes.2011.03.078

Cited by 17 publications

(16 citation statements)

References 7 publications

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“…The second boundary condition in (2) ∂ψ ∂t (t, 1) = −V 0 (based on a time derivative) can be also found in numerous references Witrant et al (2007), Argomedo et al (2010), Ouarit et al (2011) and Gaye et al (2011).…”

Section: Control Problem Descriptionmentioning

confidence: 92%

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Robust stabilization of the current profile in tokamak plasmas using sliding mode approach in infinite dimension

Gaye¹,

Moulay²,

Brémond³

et al. 2013

Control Engineering Practice

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This paper deals with the robust stabilization of the spatial distribution of tokamak plasmas current profile using a sliding mode feedback control approach. The control design is based on the 1D resistive diffusion equation of the magnetic flux that governs the plasma current profile evolution. The feedback control law is derived in the infinite dimensional setting without spatial discretisation. Numerical simulations are provided and the tuning of the controller parameters that would reject uncertain perturbations is discussed. Closed loop simulations performed on realistic test cases using a physics based tokamak integrated simulator confirm the relevance of the proposed control algorithm in view of practical implementation.

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Section: Control Problem Descriptionmentioning

confidence: 92%

“…The second step is an optimization process which finds the best set of parameters P lh , N lh and V 0 to fit the current density target j target provided by the sliding mode control law in the infinite dimensional setting. The optimisation algorithm miminizes the quadrature (Ouarit et al (2011)…”

Section: Control Problem Descriptionmentioning

confidence: 99%

Robust stabilization of the current profile in tokamak plasmas using sliding mode approach in infinite dimension

Gaye¹,

Moulay²,

Brémond³

et al. 2013

Control Engineering Practice

Self Cite

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“…MPC was first applied to profile control in simulations with simple models and only fixed actuator constraints [17,18]. We presented in [10] a model predictive controller that uses multiple linearized models to control the q-profile while effectively dealing with constraints on actuators and physics limits in ITER simulations.…”

Section: Introductionmentioning

confidence: 99%

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

et al. 2017

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The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

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“…In the literature, a large number of first principle model based feedback control approaches to control the q-profile are proposed of which we mention here the most recent [12][13][14][15][16][17][18][19][20][21][22]. Most of these contributions take fixed actuator constraints passively into account, meaning that they are not included in the controller design, but a posteriori imposed by the use of e.g.…”

Section: Introductionmentioning

confidence: 99%

“…an anti-windup loop. Handling of actuator constraints in the q-profile controller design itself is done in [13,14,16], where actuator constraints are fixed.…”

Section: Introductionmentioning

confidence: 99%

Control of the tokamak safety factor profile with time-varying constraints using MPC

et al. 2015

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show abstract

Validation of plasma current profile model predictive control in tokamaks via simulations

Cited by 17 publications

References 7 publications

Robust stabilization of the current profile in tokamak plasmas using sliding mode approach in infinite dimension

Robust stabilization of the current profile in tokamak plasmas using sliding mode approach in infinite dimension

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

Control of the tokamak safety factor profile with time-varying constraints using MPC

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