An outlook on robust model predictive control algorithms: Reflections on performance and computational aspects
MB Saltık, L Özkan, JHA Ludlage, S Weiland… - Journal of Process …, 2018 - Elsevier
In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …
Real-time optimization strategy for single-track high-speed train rescheduling with disturbance uncertainties: A scenario-based chance-constrained model predictive …
To improve the operational efficiency of high-speed railway system with disturbance
uncertainties, a real-time optimization rescheduling strategy is designed based on the …
uncertainties, a real-time optimization rescheduling strategy is designed based on the …
[HTML][HTML] Scenario-based defense mechanism against vulnerabilities in Lagrange-based DMPC
In this paper, we present an analysis of the vulnerability of a distributed model predictive
control (DMPC) scheme in the context of cyber-security. We consider different types of the so …
control (DMPC) scheme in the context of cyber-security. We consider different types of the so …
Nonlinear scenario‐based model predictive control for quadrotors with bidirectional thrust
The control of quadrotor vehicles under state and parameter uncertainty is a well studied
problem that is vitally important to the deployment of these systems under real world …
problem that is vitally important to the deployment of these systems under real world …
Multi‐scenario robust online optimization and control of fed‐batch systems via dynamic model‐based scenario selection
The manuscript proposes a novel robust methodology for the model‐based online
optimization/optimal control of fed‐batch systems, which consists of two different interacting …
optimization/optimal control of fed‐batch systems, which consists of two different interacting …
Long Duration Stochastic MPC With Mission-Wide Probabilistic Constraints Using Waysets
V Raghuraman, JP Koeln - IEEE Control Systems Letters, 2022 - ieeexplore.ieee.org
A stochastic Model Predictive Control (MPC) formulation is presented for systems operating
for a finite time subject to constraints on the Mission-Wide Probability of Safety (MWPS). For …
for a finite time subject to constraints on the Mission-Wide Probability of Safety (MWPS). For …
Distributed scenario model predictive control for driver aided intersection crossing
The automation of road intersections has significant potential to improve traffic throughput
and efficiency. While the related control problem is usually addressed assuming fully …
and efficiency. While the related control problem is usually addressed assuming fully …
Distributed stochastic model predictive control synthesis for large-scale uncertain linear systems
V Rostampour, T Keviczky - 2018 Annual American Control …, 2018 - ieeexplore.ieee.org
This paper presents an approach to distributed stochastic model predictive control (SMPC)
of large-scale uncertain linear systems with additive disturbances. Typical SMPC …
of large-scale uncertain linear systems with additive disturbances. Typical SMPC …
Scenario MPC for fuel economy optimization of hybrid electric powertrains on real-world driving cycles
Optimization based energy management strategies for hybrid electric vehicles require a
reliable forecast of the future driver torque demand to yield an appropriate performance in …
reliable forecast of the future driver torque demand to yield an appropriate performance in …
Stochastic NMPC/DRTO of batch operations: Batch-to-batch dynamic identification of the optimal description of model uncertainty
The effectiveness of stochastic online process optimization strongly depends on the choice
of the uncertain parameters, which are used to characterize the uncertainty embedded in the …
of the uncertain parameters, which are used to characterize the uncertainty embedded in the …