A bilevel optimization approach for D-invariant set design**The research leading to these results has benefited from the financial support of the European Union’s 7th Framework Programme under EC-GA No. 607957 TEMPO - Training in Embedded Model Predictive Control and Optimization

Abstract A stochastic model predictive control (SMPC) algorithm is developed to solve the problem of three-dimensional spacecraft rendezvous and docking with unbounded disturbance. In particular, we only assume that the mean and variance information of the disturbance is available. In other words, the probability density function of the disturbance distribution is not fully known. Obstacle avoidance is considered during the rendezvous phase. Line-of-sight cone, attitude control bandwidth, and thrust direction constraints are considered during the docking phase. A distributionally robust optimization based algorithm is then proposed by reformulating the SMPC problem into a convex optimization problem. Numerical examples show that the proposed method improves the existing model predictive control based strategy and the robust model predictive control based strategy in the presence of disturbance.

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Automatic deployment of industrial embedded model predictive control using qpOASES

2015 European Control Conference (ECC) ◽

10.1109/ecc.2015.7330930 ◽

2015 ◽

Cited By ~ 9

Author(s):

D. K. M. Kufoalor ◽

B. J. T. Binder ◽

H. J. Ferreau ◽

L. Imsland ◽

T. A. Johansen ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Embedded Model ◽

Automatic Deployment

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Ellipsoid invariant set-based robust model predictive control for repetitive processes with constraints

IET Control Theory and Applications ◽

10.1049/iet-cta.2015.0969 ◽

2016 ◽

Vol 10 (9) ◽

pp. 1018-1026 ◽

Cited By ~ 5

Author(s):

Jingyi Lu ◽

Zhixing Cao ◽

Furong Gao

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Invariant Set ◽

Repetitive Processes ◽

Robust Model Predictive Control ◽

Robust Model

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Embedded model predictive control of unmanned micro aerial vehicles

2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR) ◽

10.1109/mmar.2016.7575273 ◽

2016 ◽

Cited By ~ 26

Author(s):

Tomas Baca ◽

Giuseppe Loianno ◽

Martin Saska

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Micro Aerial Vehicles ◽

Aerial Vehicles ◽

Embedded Model

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Model Predictive Control of a Distributed-Parameter Process Employing a Moving Radiant Actuator

ASME 2010 Dynamic Systems and Control Conference, Volume 1 ◽

10.1115/dscc2010-4033 ◽

2010 ◽

Author(s):

Fan Zeng ◽

Beshah Ayalew

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Process Model ◽

Energy Use ◽

State Parameter ◽

Source Model ◽

Distributed Parameter ◽

Optimization Approach ◽

Radiant Intensity ◽

Process Energy

Many industrial processes employ radiation-based actuators with two or more manipulated variables. Moving radiant actuators, in particular, act on a distributed parameter process where the velocity of the actuator is an additional manipulated variable with its own constraints. In this paper, a model predictive control (MPC) scheme is developed for a distributed-parameter process employing such a moving radiant actuator. The designed MPC controller uses an online optimization approach to determine both the radiant intensity and velocity of the moving actuator based on a linearized process model and a distributed state/parameter estimator. A particular source-model reduction that enables the approach is outlined. The proposed strategy is then demonstrated for a radiative curing process considering different control scenarios with the objective of achieving desired cure level uniformity and minimizing process energy use.

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