Control of time-varying systems based on forward Riccati formulation in hybrid functions domain

Saiery, Milad; Katebi, Javad; Lakestani, Mehrdad

doi:10.1080/00207179.2022.2099979

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…Besides, a linear decentralized time-varying model predictive control of unmanned aerial vehicles was proposed in [9]. For the applications of time-varying Riccati equations, a time-varying control system is investigated with the help of forward Riccati equation and hybrid functions in [10].…”

Section: Introductionmentioning

confidence: 99%

Robust decentralized regulation for linear time-varying interconnected systems via time-varying Riccati equations

Mao

Lin

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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This paper introduces a robust decentralized regulation approach of linear interconnected time-varying systems. If the optimal optimization problems of time-varying systems are considered, the solution of time-varying Riccati differential equation should be involved and pre-calculated. The weighting parameters of the optimal performance index in this paper are all time varying. The optimal solution relies on the initial conditions of the solutions of Riccati differential equations, and these initial conditions are derived by the backward Euler’s method. Then the obtained optimal control gain is modified to accommodate the interactions among the subsystems. The overall system is mathematically proved to be exponentially stable with a prescribed degree of stability. Computer simulations of a time-varying example composed of three subsystems are conducted to demonstrate the feasibility of this approach.

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Section: Introductionmentioning

confidence: 99%

Robust decentralized regulation for linear time-varying interconnected systems via time-varying Riccati equations

Mao

Lin

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations

et al. 2022

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One of the most often used approaches for approximating various matrix equation problems is the hyperpower family of iterative methods with arbitrary convergence order, whereas the zeroing neural network (ZNN) is a type of neural dynamics intended for handling time-varying problems. A family of ZNN models that correlate with the hyperpower iterative methods is defined on the basis of the analogy that was discovered. These models, known as higher-order ZNN models (HOZNN), can be used to find real symmetric solutions of time-varying algebraic Riccati equations. Furthermore, a noise-handling HOZNN (NHOZNN) class of dynamical systems is introduced. The traditional ZNN and HOZNN dynamic flows are compared theoretically and numerically.

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Control of time-varying systems based on forward Riccati formulation in hybrid functions domain

Cited by 2 publications

References 26 publications

Robust decentralized regulation for linear time-varying interconnected systems via time-varying Riccati equations

Robust decentralized regulation for linear time-varying interconnected systems via time-varying Riccati equations

Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations

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