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Integration of offset-free control framework with Koopman Lyapunov-based model predictive control
2021 American Control Conference (ACC), 2021Koopman operator is an infinite-dimensional linear operator that governs the evolution of observable functions along trajectories of a given nonlinear dynamical system. Recently, several predictive control methods utilizing data-driven approximation of Koopman operator have been developed and applied in various fields.
Sang Hwan Son +2 more
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ROBUST OFFSET-FREE MODEL PREDICTIVE CONTROL
IFAC Proceedings Volumes, 2002Abstract In this paper a method for designing robust offset-free MPC controllers for (possibly) nonzero targets is presented. The proposed controller is guaranteed to track the controlled variable to its target for any plant that lies in a polytopic region.
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On offset free generalized predictive control
2013 International Conference on Process Control (PC), 2013The paper deals with offset free reference tracking problem for reference signals composed of step and ramp functions. The proposed solution arises from usual design of generalized predictive control supplemented with specific modifications suppressing or ideally removing undesirable offset (steady state error) from required behavior.
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Offset-Free Model Predictive Control of a Heat Pump
Industrial & Engineering Chemistry Research, 2015This work presents an offset-free model predictive control (OF-MPC) design for energy-efficient control of a heat pump. A model developed from a combination of first-principles and empirical components with parameters estimated using real heat pump data is used as a test bed for the implementation of the MPC design.
Matt Wallace +3 more
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Offset-free state-space nonlinear predictive control for Wiener systems
Information Sciences, 2020The paper is very interesting. This is on the high level of mathematical methods used. The authors consider a difficult, but very important control problem. There are also added very interesting simulations, that highlight presented results.
Maciej Lawrynczuk, Piotr Tatjewski
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Adaptive disturbance estimation for offset-free SISO Model Predictive Control
Proceedings of the 2011 American Control Conference, 2011Offset free tracking in Model Predictive Control requires estimation of unmeasured disturbances or the inclusion of an integrator. An algorithm for estimation of an unknown disturbance based on adaptive estimation with time varying forgetting is introduced and benchmarked against the classical disturbance modelling approach, where the system ...
Jakob Kjøbsted Huusom +3 more
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On the Offset-free Nonlinear Model Predictive Control for AUV Docking
2021 WRC Symposium on Advanced Robotics and Automation (WRC SARA), 2021The docking control of AUV is disturbed by model mismatch and sea current. This paper presents a method of AUV docking control based on the application of offset-free nonlinear model predictive control (NMPC) with multiple dynamic constraints. The controller drives the AUV from the initial pose to the entrance of the docking station (DS) through the ...
Kai Shi, Xiaohui Wang, Huixi Xu
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Offset-Free Explicit Hybrid Model Predictive Control of Intravenous Anaesthesia
2015 IEEE International Conference on Systems, Man, and Cybernetics, 2015The paper describes strategies for the control of intravenous depth of anaesthesia for the induction and maintenance phase, based on a detailed compartmental model composed of a pharmacokinetic and a pharma codynamic model. The system can be described in a piece-wise affine fashion, leading to a hybrid model predictive control problem, which is solved ...
Ioana Nascu +2 more
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Modeling and Offset-Free Model Predictive Control of a Hydraulic Mini Excavator
IEEE Transactions on Automation Science and Engineering, 2017During the virtual development and experimental testing of advanced construction machinery, automation approaches for automated task execution can prove very valuable. In this paper, modeling and automation approaches for a hydraulic mini excavator are developed.
Frank A. Bender +3 more
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Offset-Free Nonlinear Model Predictive Control
2017Offset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. A brief formulation of the MPC formulation used is first given, followed by a brief remainder of the case with measured state vector.
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