H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator

This paper is concerned with the robust H ∞ load frequency control (LFC) of power systems with two time delays in the presence of load disturbance. Firstly, the original power systems with the proportional-integral (PI)-type LFC are converted into an output feedback control system with two time delays. Secondly, an improved reciprocally convex inequality is proposed, and a less conservative robust H ∞ criterion is derived, which can be used to design the PI-type LFC with robust H ∞ performance. Then, based on the robust H ∞ criterion, an algorithm is provided to obtain the PI-type LFC gain and the time delay margin, which effectively convert the coupled nonlinear terms into linear items without solving the inverse of the matrix with row full rank. Finally, simulations are shown to verify the superiority of the presented method.

show abstract

“…Then, from (14) to (16), by applying the Jensen integral inequality 25,26 again, one has

\overset{V}{true} ((), t) \leq δ^{T} ((), t) ((), Ω_{1} - Ω_{2} Ω_{3}^{- 1} Ω_{2}^{T}) δ ((), t) - y^{T} ((), t) y ((), t) + γ^{2} ω^{T} ((), t) ω ((), t)

where

δ ((), t) = {[x^{T} (t), x^{T} (t - τ_{f}), x^{T} (t - τ (t)), x^{T} (t - τ_{d}), ω^{T} (t)]}^{T}

.…”

Section: Resultsmentioning

confidence: 99%

Robust load frequency control of power systems with two time delays

Tan

Shi

Liu

et al. 2021

Int Trans Electr Energ Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the former can derive less conservativeness and take more advantages in the practical applications. In recent years, many investigations have been investigated for the delayed neural networks, see, to name a few are References 31-45. Since the above results have not concerned the delay‐dependent criteria.…”

Section: Model Description and Preliminariesmentioning

confidence: 99%

Delay‐dependent criteria for general decay synchronization of discontinuous fuzzy neutral‐type neural networks with time‐varying delays

Kong

Sakthivel

2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This paper aims to investigate the general decay synchronization analysis of discontinuous fuzzy neutral-type neural networks. Under the framework of Filippov solutions, based on functional differential inclusions theory, inequality technique and by constructing a modified delay-dependent Lyapunov-Krasovskii functional, some new delay-dependent criteria are provided to guarantee the general decay synchronization via the effective nonlinear feedback controller. The neural network model considered is more generalized, some previous fuzzy neural networks can be regard as the special cases. Moreover, some recent results on the delay-independent criteria ensuring the general decay synchronization can also be extended and concluded. These can be seen from the given corollaries and remarks which are provided to make the comparisons and show the advantages. Finally, some numerical examples and simulations are provided to illustrate the correctness.

show abstract

“…In detail, the hydraulic system always exists parametric uncertainties, external disturbance, and unmodeled nonlinearities as well as faults [1]- [4]. In order to improve the system performance, the disturbance is not only needfully suppressed but also compensated by assisted techniques such as extended state observer [5], [6], neural network (NN) approximators [7]- [10] fuzzy logic system (FLS) [11], time-delay estimation (TDE) [12], [13], etc. In a certain way, the disturbances or uncertainties can be considered as faults, which seriously affect system performance and safety [14].…”

Section: Introductionmentioning

confidence: 99%

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Phan

Dao

et al. 2021

IEEE Access

View full text Add to dashboard Cite

In this paper, a novel adaptive fault-tolerant controller is proposed for a typical electrohydraulic rotary actuator in the presence of disturbances, internal leakage fault, and sensor fault simultaneously. To construct the suggested controller, a nonlinear unknown input observer is developed to effectively identify the sensor fault, which is unaffected by not only internal leakage fault but also mismatched disturbances/uncertainties. Furthermore, a radial basis function neural network is designed to compensate for the mismatched disturbances/uncertainties caused by payload variation and unknown friction nonlinearities. Besides, an adaptive law based on the projection mapping function is applied to tackle the effect of the internal leakage fault. The integration of the above-mentioned techniques into the adaptive backstepping terminal sliding mode is investigated to obtain high tracking performance, robustness as well as fast convergence. The stability of the closed-loop system is proven by the Lyapunov theory. Finally, the capability and effectiveness of the proposed approach are validated via simulation results under various faulty scenarios.

show abstract

H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator

Cited by 28 publications

References 22 publications

Robust load frequency control of power systems with two time delays

Robust load frequency control of power systems with two time delays

Delay‐dependent criteria for general decay synchronization of discontinuous fuzzy neutral‐type neural networks with time‐varying delays

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

Contact Info

Product

Resources

About