Fixed-Time $\mathcal {H}_{\infty }$ Control for Port-Controlled Hamiltonian Systems

Liu, Xinggui; Liao, Xiaofeng

doi:10.1109/tac.2018.2874768

Cited by 33 publications

(24 citation statements)

References 31 publications

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“…From eorem 1 in [36], to get a finite-time control law based on the PCH model, all parameters about J d (x) and R d (x) should be predetermined or calculated such that match condition (5). For system (2) with the n-dimensional state variable, the number of parameters of J d (x) is about (1/2)n(n + 1) considering the symmetry of it.…”

Section: E Finite-time Control Law Of Cftllsmentioning

confidence: 99%

“…Let us recall the proof procedure of eorem 1 in [36] about k and the settling time function (11). From the form of R d (x) in ( 21), it can be obtained that ρ � min ra 1 , ra 2 , .…”

Section: Theorem 1 (Damping Normalization) Consider System (2)mentioning

confidence: 99%

“…Now, eorem 1 can be obtained. [36], where J(x), R(x), and g(x) are prefixed, and the Hamiltonian function and desired Hamiltonian function are changed; that is to say, these two functions are to be changed at the same time which will lead to the system model to be changed. e damping normalization method in this paper can supply more optimized control laws for the fixed system model; that is to say, J(x), R(x), and g(x) and the Hamiltonian function can all remain unchanged.…”

Section: Complexitymentioning

confidence: 99%

“…where m > 0 and k > 0. From ( 35), (36), and (1), the sliding mode control law can be obtained as Complexity…”

Section: Simulationmentioning

confidence: 99%

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Finite-Time Control for a Coupled Four-Tank Liquid Level System Based on the Port-Controlled Hamiltonian Method

2020

Complexity

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This work investigates the finite-time control problem for a nonlinear four-tank cross-coupled liquid level system by the port-controlled Hamiltonian (PCH) model. A fixed-free methodology is exhibited which can be used to simplify the controller design procedure. To get an adjustable convergent gain of the finite-time control, a feasible technique named damping normalization is proposed. A novel parameter autotuning algorithm is given to clarify the principle of choosing parameters of the PCH method. Furthermore, a finite-time controller is designed by a state-error desired Hamiltonian function, and the relationship between the settling time and a parameter is given, which can be applied in practical engineering easily to adjust the settling time according to the industrial need. Finally, simulation and experimental results verify the effectiveness of the proposed algorithm.

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Section: E Finite-time Control Law Of Cftllsmentioning

confidence: 99%

“…Let us recall the proof procedure of eorem 1 in [36] about k and the settling time function (11). From the form of R d (x) in ( 21), it can be obtained that ρ � min ra 1 , ra 2 , .…”

Section: Theorem 1 (Damping Normalization) Consider System (2)mentioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

“…where m > 0 and k > 0. From ( 35), (36), and (1), the sliding mode control law can be obtained as Complexity…”

Section: Simulationmentioning

confidence: 99%

See 2 more Smart Citations

Finite-Time Control for a Coupled Four-Tank Liquid Level System Based on the Port-Controlled Hamiltonian Method

2020

Complexity

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“…The main features of this nonlinear control are that the DC microgrid network is considered a Port-Hamiltonian (pH) structure and the Hamiltonian function which is related to the Lyapunov function for asymptotic stability purposes. Considering the pH system, it is helpful to describe it as network models of physical components from the energy transfer in its environment via ports point of view [29,30].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Control of Fuel Cell Converter Based on a New Hamiltonian Energy Function for Stabilizing the DC Bus in DC Microgrid Applications

et al. 2020

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DC microgrid applications include electric vehicle systems, shipboard power systems, and More Electric Aircraft (MEA), which produce power at a low voltage level. Rapid developments in hydrogen fuel cell (FC) energy have extended the applications of multi-phase parallel interleaved step-up converters in stabilizing DC bus voltage. The cascade architecture of power converters in DC microgrids may lead to large oscillation and even risks of instability given that the load converters considered as loads feature constant power load (CPL) characteristics. In this article, the output DC bus voltage stabilization and the current sharing of a multi-phase parallel interleaved FC boost converter is presented. The extended Port-Hamiltonian (pH) form has been proposed with the robust controller by adding an integrator action based on the Lyapunov−Energy function, named “Adaptive Hamiltonian PI controller”. The stability and robustness of the designed controller have been estimated by using Mathematica and Matlab/Simulink environments and successfully authenticated by performing experimental results in the laboratory. The results have been obtained using a 2.5 kW prototype FC converter (by two-phase parallel interleaved boost converters) with a dSPACE MicroLabBox platform. The FC main source system is based on a fuel reformer engine that transforms fuel methanol and water into hydrogen gas H2 to a polymer electrolyte membrane FC stack (50 V, 2.5 kW).

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Fixed time synchronization of delayed chaotic neural networks by using active adaptive control

Luo

et al. 2021

Adaptive Control & Signal

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This article aims to investigate the fixed time synchronization of a class of chaotic neural systems by way of adaptive control method. Using Lyapunov stability theory, a new fixed time stability theorem which plays an important role on the synchronization scheme is presented at first. Then, combining the fixed time stability theorem and adaptive control technique, an adaptive control scheme has been developed to achieve the fixed time synchronization of chaotic neural systems. The proposed controllers assure the global convergence of the error dynamics in fixed-time based on the Lyapunov stability theory. Furthermore, the proposed control strategy cannot only provide a fast convergence rate, but also afford a bounded convergence time which is unrelated to the initial values and easy to work out by using the simple time calculation formula. Finally, numerical simulations are presented by taking a typical two-order chaotic neural system as an example to verify and demonstrate the effectiveness of the proposed scheme.

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Fixed-Time $\mathcal {H}_{\infty }$ Control for Port-Controlled Hamiltonian Systems

Cited by 33 publications

References 31 publications

Finite-Time Control for a Coupled Four-Tank Liquid Level System Based on the Port-Controlled Hamiltonian Method

Finite-Time Control for a Coupled Four-Tank Liquid Level System Based on the Port-Controlled Hamiltonian Method

Adaptive Control of Fuel Cell Converter Based on a New Hamiltonian Energy Function for Stabilizing the DC Bus in DC Microgrid Applications

Fixed time synchronization of delayed chaotic neural networks by using active adaptive control

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