Robust controller design for systems with probabilistic uncertain parameters using multi-objective genetic programming
Sign Up to like & getrecommendations! Published in 2021 at "Soft Computing"
DOI: 10.1007/s00500-020-05133-x
Abstract: Optimal design of controllers without considering uncertainty in the plant dynamics can induce feedback instabilities and lead to obtaining infeasible controllers in practice. This paper presents a multi-objective evolutionary algorithm integrated with Monte Carlo simulations… read more here.
Keywords: methodology; genetic programming; multi objective; controller ... See more keywords
Computationally efficient implementation of robust controllers in active magnetic bearing systems
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DOI: 10.1016/j.ymssp.2020.106902
Abstract: Abstract This paper proposes a novel method for implementing robust controllers for active magnetic bearing (AMB) systems in a computationally cost-effective manner without affecting robust performance. The method decomposes the single-rate LTI controller into two… read more here.
Keywords: controllers active; dual rate; active magnetic; rate ... See more keywords
Absolutely robust controllers for chemical reaction networks
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DOI: 10.1098/rsif.2020.0031
Abstract: In this work, we design a type of controller that consists of adding a specific set of reactions to an existing mass-action chemical reaction network in order to control a target species. This set of… read more here.
Keywords: absolutely robust; target species; reaction; robust controllers ... See more keywords
Learning Robust LQ-Controllers Using Application Oriented Exploration
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DOI: 10.1109/lcsys.2019.2921512
Abstract: This letter concerns the problem of learning robust LQ-controllers, when the dynamics of the linear system are unknown. First, we propose a robust control synthesis method to minimize the worst-case LQ cost, with probability $1-\delta… read more here.
Keywords: learning robust; controller; exploration; worst case ... See more keywords