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Robust Tracking as Constrained Optimization by Uncertain Dynamic Plant: Mirror Descent Method and ASG—Version of Integral Sliding Mode Control
A class of controlled objects is considered, the dynamics of which are determined by a vector system of ordinary differential equations with a partially known right-hand side. It is presumed that the state variables and their velocities can be measured. Designing a robust tracking controller under s...
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Published in: | Mathematics (Basel) 2023-10, Vol.11 (19), p.4112 |
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creator | Nazin, Alexander Alazki, Hussain Poznyak, Alexander |
description | A class of controlled objects is considered, the dynamics of which are determined by a vector system of ordinary differential equations with a partially known right-hand side. It is presumed that the state variables and their velocities can be measured. Designing a robust tracking controller under some constraints to admissible state variables is the research goal. This construction, which extends the results for the average subgradient technique (ASG), and is an update of the subgradient descent technique (SDM) and integral sliding mode (ISM) approach, is realized by using the Legendre–Fenchel transform. A two-link robot manipulator with three revolute joints, powered by individual PMDC motors, is presented as an illustrative example of the suggested approach implementation. |
doi_str_mv | 10.3390/math11194112 |
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subjects | Algorithms Constraints Controllers convex constrained optimization Differential equations Mathematical optimization Mathematical research Optimization Ordinary differential equations Process controls Robot arms robot manipulator Robust control sliding mode Sliding mode control State variable subgradient descent method Tracking control trajectory tracking Variables |
title | Robust Tracking as Constrained Optimization by Uncertain Dynamic Plant: Mirror Descent Method and ASG—Version of Integral Sliding Mode Control |
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