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Event-triggered model predictive control with deep reinforcement learning for autonomous driving
Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …
aim to alleviate the computation and/or communication burden of MPC. However, it …
Data-driven stochastic model predictive control for DC-coupled residential PV-storage systems
This paper develops a stochastic model predictive control (SMPC) based framework for the
real-time operation of residential-scale DC-coupled PV-storage systems. The proposed …
real-time operation of residential-scale DC-coupled PV-storage systems. The proposed …
[HTML][HTML] Chance-constrained sets approximation: A probabilistic scaling approach
In this paper, a sample-based procedure for obtaining simple and computable
approximations of chance-constrained sets is proposed. The procedure allows to control the …
approximations of chance-constrained sets is proposed. The procedure allows to control the …
Sampling complexity of path integral methods for trajectory optimization
The use of random sampling in decision-making and control has become popular with the
ease of access to graphic processing units that can generate and calculate multiple random …
ease of access to graphic processing units that can generate and calculate multiple random …
Prediction error quantification through probabilistic scaling
In this letter, we address the probabilistic error quantification of a general class of prediction
methods. We consider a given prediction model and show how to obtain, through a sample …
methods. We consider a given prediction model and show how to obtain, through a sample …
Adaptive stochastic predictive control from noisy data: A sampling-based approach
In this work, an adaptive predictive control scheme for linear systems with unknown
parameters and bounded additive disturbances is proposed. In contrast to related adaptive …
parameters and bounded additive disturbances is proposed. In contrast to related adaptive …
Learning-based rigid tube model predictive control
This paper is concerned with model predictive control (MPC) of discrete-time linear systems
subject to bounded additive disturbance and mixed constraints on the state and input …
subject to bounded additive disturbance and mixed constraints on the state and input …
Chance constrained sets approximation: A probabilistic scaling approach--EXTENDED VERSION
In this paper, a sample-based procedure for obtaining simple and computable
approximations of chance-constrained sets is proposed. The procedure allows to control the …
approximations of chance-constrained sets is proposed. The procedure allows to control the …
Robust Embedded Control using Randomized Switching Algorithms
Multiple model adaptive control (MMAC) is an adaptive control method designed for plant
parameter uncertainty given both linear and non-linear plant models. For a system subject to …
parameter uncertainty given both linear and non-linear plant models. For a system subject to …
[کتاب][B] Towards a resilient and intelligent energy management system design for distribution networks with high renewable energy penetration
A Shirsat - 2022 - search.proquest.com
With rapidly plummeting costs of renewable distributed generation and their enabling-
technologies such as energy storage, the integration of highly uncertain and non …
technologies such as energy storage, the integration of highly uncertain and non …