Disturbance suppression via robust MPC using prior disturbance data: Low computational complexity method

Sato, Masayuki; Yokoyama, Nobuhiro; Satoh, Akira

doi:10.1109/icca.2009.5410203

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Finally, CPU time to solve the conditions in Proposition 2 is shown in Table I, where the simulation is conducted with Matlab R using SeDuMi [19] Thus, the reduction of numerical complexity for Proposition 2 is to be investigated, and a simple methodology with full exploitation of prior disturbance data has been proposed in [20].…”

Section: B Simulation Resultsmentioning

confidence: 99%

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Disturbance suppression via robust MPC using prior disturbance data application to flight controller design for Gust Alleviation

Sato

Yokoyama

Satoh

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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This paper addresses disturbance suppression problem for uncertain plant systems using prior disturbance data which contain some measurement errors. We tackle optimal control input design problem using Model Predictive Control (MPC) scheme in which a priori measured disturbance data are exploited. We show that if the uncertainties of the plant systems are expressed by bounded but time-invariant uncertain delays at the control input, then we only have to consider finitely many plant models instead of the original uncertain plant systems. Furthermore, we also show that if the measurement errors in prior disturbance data are expressed as affine with respect to some constant uncertain vector, whose elements are bounded, then we only have to evaluate the measurement errors at the vertices of the vector. Using these, we propose a robust MPC design with finitely many conditions for our addressed problem. Finally, we apply our proposed method to flight controller design problem for suppressing the vertical acceleration driven by turbulence, i.e. Gust Alleviation (GA) flight controller design problem.

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Section: B Simulation Resultsmentioning

confidence: 99%

“…The remaining number N have a great impact to controller performance. On this issue, we have proposed a further result with reducing the numerical complexity of Proposition 2 in [20], and demonstrate the trade-off between controller performance and the numerical complexity.…”

Section: B Measurement Error Casementioning

confidence: 97%

Disturbance suppression via robust MPC using prior disturbance data application to flight controller design for Gust Alleviation

Sato

Yokoyama

Satoh

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

Self Cite

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Active Noise Control for Harmonic and Broadband Disturbances Using RLS-Based Model Predictive Control

Mohseni

Nguyen

Islam

et al. 2020

2020 American Control Conference (ACC)

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Envelopes for Flight Through Stochastic Gusts

Richardson

Atkins

Kabamba

et al. 2013

Journal of Guidance, Control, and Dynamics

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We present a technique to guarantee, with a specified probability, that a steady flight state can be maintained when stochastic wind gusts act upon an airplane. First we linearize the airplane dynamic equations and present them as a linear time invariant system with the airplane's velocity and angular velocity as the state and gust velocity as the input. We then treat the gust velocity as a stationary random process and append the dynamics of a coloring filter to the linearized airplane equations so that the input is Gaussian white noise. We analyze the effect of the noise on the airplane's dynamics, using the resulting equations to quantify the effects of stochastic wind gusts on an airplane's steady flight envelope. During the analysis we introduce the notion of stationary flight and a stationary flight envelope to guarantee, within a specified probability, steady flight under such conditions. We also present a numerical example of a general aviation aircraft in steady level flight through moderate turbulence. In the example, the airplane's cruise speed fluctuates with a variance of 1.2 m 2 /s 2 and we show examples of stationary flight envelopes for this case.v c Center of mass velocity of the airplane, u v w Ṫ v c Dotting a vector denotes a time derivative V c×Matrix formed from v c to replace a cross product with an inner product, see Ω ×

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Disturbance suppression via robust MPC using prior disturbance data: Low computational complexity method

Cited by 4 publications

References 8 publications

Disturbance suppression via robust MPC using prior disturbance data application to flight controller design for Gust Alleviation

Disturbance suppression via robust MPC using prior disturbance data application to flight controller design for Gust Alleviation

Active Noise Control for Harmonic and Broadband Disturbances Using RLS-Based Model Predictive Control

Envelopes for Flight Through Stochastic Gusts

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