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pro vyhledávání: '"Krupa, Pablo"'
The main benefit of model predictive control (MPC) is its ability to steer the system to a given reference without violating the constraints while minimizing some objective. Furthermore, a suitably designed MPC controller guarantees asymptotic stabil
Externí odkaz:
http://arxiv.org/abs/2406.16496
Autor:
Krupa, Pablo, Köhler, Johannes, Ferramosca, Antonio, Alvarado, Ignacio, Zeilinger, Melanie N., Alamo, Teodoro, Limon, Daniel
This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization pro
Externí odkaz:
http://arxiv.org/abs/2406.06157
Publikováno v:
in IEEE Control Systems Letters, vol. 8, pp. 1499-1504, 2024
Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function. However, in practice, these constraints can result in feasibility issue
Externí odkaz:
http://arxiv.org/abs/2403.04601
Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even when abrupt changes in the reference are produced. As a drawback, it i
Externí odkaz:
http://arxiv.org/abs/2402.09912
This work presents a nonlinear MPC framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which compensates for input disturbances and model-plant mismatch. Our approach
Externí odkaz:
http://arxiv.org/abs/2312.11409
Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased
Externí odkaz:
http://arxiv.org/abs/2310.16723
Publikováno v:
IEEE Control Systems Letters, 2023
Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point when considering its implementation in embedded systems. To reduce the c
Externí odkaz:
http://arxiv.org/abs/2309.07996
Publikováno v:
Automatica, 2024
In safety-critical applications that rely on the solution of an optimization problem, the certification of the optimization algorithm is of vital importance. Certification and suboptimality results are available for a wide range of optimization algor
Externí odkaz:
http://arxiv.org/abs/2303.16786
Publikováno v:
IEEE Transactions on Automatic Control, 2022
Harmonic model predictive control (HMPC) is a model predictive control (MPC) formulation which displays several benefits over other MPC formulations, especially when using a small prediction horizon. These benefits, however, come at the expense of an
Externí odkaz:
http://arxiv.org/abs/2202.06629
Autor:
Krupa, Pablo
This Ph.D. dissertation contains results in two different but related fields: the implementation of model predictive control (MPC) in embedded systems using first order methods, and restart schemes for accelerated first order methods (AFOM). We start
Externí odkaz:
http://arxiv.org/abs/2109.02140