Searched for: subject%3A%22Function%255C+approximation%22
(1 - 12 of 12)
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He, K. (author), Shi, S. (author), van den Boom, A.J.J. (author), De Schutter, B.H.K. (author)
Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies...
journal article 2024
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Lubbers, Seymour (author)
Greenhouses allow production of crops that would otherwise be impossible. Permitting more local, fresher and nutrient richer crop production. Eorts are taken to minimize societal harm due to energy and resource consumption by greenhouse production systems. One way to control such systems is by using model predictive control. Optimal crop yield...
master thesis 2023
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Dai, Pengcheng (author), Yu, Wenwu (author), Wang, He (author), Baldi, S. (author)
Actor-critic (AC) cooperative multiagent reinforcement learning (MARL) over directed graphs is studied in this article. The goal of the agents in MARL is to maximize the globally averaged return in a distributed way, i.e., each agent can only exchange information with its neighboring agents. AC methods proposed in the literature require the...
journal article 2023
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Zhou, Y. (author), Ho, H.W. (author)
Hierarchical Reinforcement Learning (HRL) provides an option to solve complex guidance and navigation problems with high-dimensional spaces, multiple objectives, and a large number of states and actions. The current HRL methods often use the same or similar reinforcement learning methods within one application so that multiple objectives can...
journal article 2022
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Meyer, Johann (author)
Aircraft are complex systems with, in some cases, high-dimensional nonlinear interactions between control surfaces. When a failure occurs, adaptive flight control methods can be utilised to stabilise and make the aircraft controllable. Adaptive flight control methods, however, require accurate aerodynamic models - where first-order continuity is...
master thesis 2021
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Becker, Midas (author)
<br/>Being a safe and healthy alternative for polluting and space-inefficient motorised vehicles, cycling can strongly improve living conditions in urban areas. Idling in front of traffic lights is seen as one of the major inconveniences of commuting by bicycle. By giving personalised speed advice, the probability of catching a green light can...
master thesis 2021
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Karagöz, Ridvan (author)
B-splines are basis functions for the spline function space and are extensively used in applications requiring function approximation. The generalization of B-splines to multiple dimensions is done through tensor products of their univariate basis functions. The number of basis functions and weights that define a multivariate B-spline surface,...
master thesis 2020
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Dai, Pengcheng (author), Yu, Wenwu (author), Wen, Guanghui (author), Baldi, S. (author)
In this article, the dynamic economic dispatch (DED) problem for smart grid is solved under the assumption that no knowledge of the mathematical formulation of the actual generation cost functions is available. The objective of the DED problem is to find the optimal power output of each unit at each time so as to minimize the total generation...
journal article 2020
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Dorscheidt, Joost (author)
Reinforcement Learning (RL) is a learning paradigm that learns by interacting with the environment. In practice, a RL agent needs to perform many actions to sample rewards and state transitions from their environments. Recent advances in using deep neural networks as function approximators reduce the sample complexity in very high dimensional...
master thesis 2018
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Buşoniu, Lucian (author), de Bruin, T.D. (author), Tolić, Domagoj (author), Kober, J. (author), Palunko, Ivana (author)
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the...
review 2018
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Langenkamp, W.H. (author)
Reinforcement learning is a machine learning paradigm that deals with optimisation and learns by interacting with its environment. Tabular reinforcement learning methods are popular because of their relative simplicity combined with good guarantees of finding an optimal solution. The downside is that they suffer from an exponentially growing...
master thesis 2016
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De Visser, C.C. (author), Chu, Q.P. (author), Mulder, J.A. (author)
The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and...
journal article 2011
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