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of 234
pro vyhledávání: '"Alamo, Teodoro"'
Chance constraints ensure the satisfaction of constraints under uncertainty with a desired probability. This scheme is unfortunately sensitive to assumptions of the probability distribution of the uncertainty, which are difficult to verify. The uncer
Externí odkaz:
http://arxiv.org/abs/2409.01177
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
The main objective of tracking control is to steer the tracking error, that is the difference between the reference and the output, to zero while the plant's operation limits are satisfied. This requires that some assumptions on the evolution of the
Externí odkaz:
http://arxiv.org/abs/2403.02973
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
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
Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerical approximating nature of machine learning. The data analyst may minim
Externí odkaz:
http://arxiv.org/abs/2309.04627
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