Cautious model predictive control using gaussian process regression
Gaussian process (GP) regression has been widely used in supervised machine learning
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …
Distributionally robust control of constrained stochastic systems
We investigate the control of constrained stochastic linear systems when faced with limited
information regarding the disturbance process, ie, when only the first two moments of the …
information regarding the disturbance process, ie, when only the first two moments of the …
Data-driven safe control of uncertain linear systems under aleatory uncertainty
H Modares - IEEE Transactions on Automatic Control, 2023 - ieeexplore.ieee.org
Safe control of constrained uncertain linear systems under aleatory uncertainty is
considered. Aleatory uncertainty characterizes random noises and is modeled by a …
considered. Aleatory uncertainty characterizes random noises and is modeled by a …
Chance-constrained optimization for nonconvex programs using scenario-based methods
Y Yang, C Sutanto - ISA transactions, 2019 - Elsevier
This paper presents a scenario-based method to solve the chance-constrained optimization
for the nonconvex program. The sample complexity is first developed to guarantee the …
for the nonconvex program. The sample complexity is first developed to guarantee the …
Stochastic model predictive control with discounted probabilistic constraints
This paper considers linear discrete-time systems with additive disturbances, and designs a
Model Predictive Control (MPC) law to minimise a quadratic cost function subject to a …
Model Predictive Control (MPC) law to minimise a quadratic cost function subject to a …
Stochastic MPC with dynamic feedback gain selection and discounted probabilistic constraints
This article considers linear discrete-time systems with additive disturbances and designs a
model predictive control (MPC) law incorporating a dynamic feedback gain to minimize a …
model predictive control (MPC) law incorporating a dynamic feedback gain to minimize a …
Control problems on infinite horizon subject to time-dependent pure state constraints
V Basco - Mathematics of Control, Signals, and Systems, 2024 - Springer
In the last decades, control problems with infinite horizons and discount factors have
become increasingly central not only for economics but also for applications in artificial …
become increasingly central not only for economics but also for applications in artificial …
Output-feedback control of unknown linear discrete-time systems with stochastic measurement and process noise via approximate dynamic programming
JS Wang, GH Yang - IEEE transactions on cybernetics, 2017 - ieeexplore.ieee.org
This paper studies the optimal output-feedback control problem for unknown linear discrete-
time systems with stochastic measurement and process noise. A dithered Bellman equation …
time systems with stochastic measurement and process noise. A dithered Bellman equation …
Data-driven safe control of linear systems under epistemic and aleatory uncertainties
H Modares - arXiv preprint arXiv:2202.04495, 2022 - arxiv.org
Safe control of constrained linear systems under both epistemic and aleatory uncertainties is
considered. The aleatory uncertainty characterizes random noises and is modeled by a …
considered. The aleatory uncertainty characterizes random noises and is modeled by a …
On infinite dimensional linear programming approach to stochastic control
M Kamgarpour, T Summers - IFAC-PapersOnLine, 2017 - Elsevier
We consider the infinite dimensional linear programming (inf-LP) approach for solving
stochastic control problems. The inf-LP corresponding to problems with uncountable state …
stochastic control problems. The inf-LP corresponding to problems with uncountable state …