Control of malaria outbreak using a non‐linear robust strategy with adaptive gains

Rajaei, Arman; Vahidi-Moghaddam, Amin; Chizfahm, Amir; Sharifi, Mojtaba

doi:10.1049/iet-cta.2018.5292

Cited by 19 publications

(13 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several mathematical, computational and statistical methods have already been proposed and widely applied in the prediction of infectious diseases worldwide (Rajaei et al. 2019 ; Watkins et al. 2020 ; Martins et al.…”

Section: Introductionmentioning

confidence: 99%

Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data

Gomes

Serra

2021

J Control Autom Electr Syst

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data

Gomes

Serra

2021

J Control Autom Electr Syst

View full text Add to dashboard Cite

“…In the last few years, a broad variety of techniques have also been proposed for controlling nonlinear and complex systems, including adaptive control, a backstepping approach, fuzzy control, optimal control, and sliding mode control [25][26][27][28][29][30][31][32][33][34]. In this regard, the control and synchronization of chaotic systems are also attracting a lot of attention [35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Synchronization of a Non-Equilibrium Four-Dimensional Chaotic System Using a Disturbance-Observer-Based Adaptive Terminal Sliding Mode Control Method

Wang

Yousefpour

Yusuf

et al. 2020

Entropy

View full text Add to dashboard Cite

In this paper, dynamical behavior and synchronization of a non-equilibrium four-dimensional chaotic system are studied. The system only includes one constant term and has hidden attractors. Some dynamical features of the governing system, such as invariance and symmetry, the existence of attractors and dissipativity, chaotic flow with a plane of equilibria, and offset boosting of the chaotic attractor, are stated and discussed and a new disturbance-observer-based adaptive terminal sliding mode control (ATSMC) method with input saturation is proposed for the control and synchronization of the chaotic system. To deal with unexpected noises, an extended Kalman filter (EKF) is implemented along with the designed controller. Through the concept of Lyapunov stability, the proposed control technique guarantees the finite time convergence of the uncertain system in the presence of disturbances and control input limits. Furthermore, to decrease the chattering phenomena, a genetic algorithm is used to optimize the controller parameters. Finally, numerical simulations are presented to demonstrate the performance of the designed control scheme in the presence of noise, disturbances, and control input saturation.

show abstract

“…Sliding mode control (SMC) is one of the most popular controllers due to its unique characteristics for decreasing the tracking error of non‐linear systems with uncertainty and disturbance. Recently, variants of SMC, such as neural SMC [1], event‐triggered SMC [2], second‐order SMC [3], discrete‐time SMC [4], adaptive SMC [5], and integral SMC [6] have been successfully used for robust stabilisation of different systems. However, the basic SMC may not ensure the prescribed finite time convergence of the entire closed‐loop error signals to zero.…”

Section: Introductionmentioning

confidence: 99%

Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization

Vahidi-Moghaddam

Rajaei

Ayati

et al. 2020

IET Control Theory & Appl

Self Cite

View full text Add to dashboard Cite

In this study, adaptive prescribed finite-time stabilisation of uncertain single-input and single-output non-linear systems is considered in the presence of unknown states, unknown parameters, external load disturbance, and non-symmetric input saturation. A prescribed finite time disturbance observer is designed to approximate the unmeasured external disturbance. Also, a non-singular prescribed finite time terminal sliding mode control is proposed for the closed-loop control of the system with the non-symmetric input saturation. Extended Kalman filter algorithm is employed for the real-time estimations of the states and unknown parameters of the system. Moreover, a particle swarm optimisation algorithm is used to obtain the design parameters of the proposed disturbance observer and controller. To show the performance of designed control scheme, the proposed approach is employed to guarantee prescribed finite time stabilisation of non-linear vibration of a non-local strain gradient nanobeam. The Galerkin projection method is used to reduce the non-dimensional form of the governing non-linear partial differential equation of Euler-Bernoulli nanobeam to the ordinary differential equation. Finally, numerical simulations are performed to illustrate the effectiveness and performance of the developed adaptive control scheme for the vibration control of nanobeam in comparison with the conventional sliding mode control.

show abstract

Control of malaria outbreak using a non‐linear robust strategy with adaptive gains

Cited by 19 publications

References 31 publications

Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data

Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data

Synchronization of a Non-Equilibrium Four-Dimensional Chaotic System Using a Disturbance-Observer-Based Adaptive Terminal Sliding Mode Control Method

Adaptive prescribed‐time disturbance observer using nonsingular terminal sliding mode control: Extended Kalman filter and particle swarm optimization

Contact Info

Product

Resources

About