Stabilization of nonlinear vibrations of carbon nanotubes using observer-based terminal sliding mode control

Yousefpour, Amin; Vahidi-Moghaddam, Amin; Rajaei, Arman; Ayati, Moosa

doi:10.1177/0142331219881547

Cited by 36 publications

(18 citation statements)

References 50 publications

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“…In most real-world applications, it is rare to found accurate information about the dynamics of the systems, and moreover, they are often in the presence of various disturbances [26][27][28][29][30]. Thus, in these cases it is very beneficial to apply a controller that is robust to unmodeled dynamics and external disturbances [31][32][33]. To this end, previous works have proposed several disturbance-observers with different control schemes for some systems [34,35].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

et al. 2020

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We develop a new robust control scheme for a non-holonomic spherical robot. To this end, the mathematical model of a pendulum driven non-holonomic spherical robot is first presented. Then, a recurrent neural network-based robust nonsingular sliding mode control is proposed for stabilization and tracking control of the system. The designed recurrent neural network is applied to approximate compound disturbances, including external interferences and dynamic uncertainties. Moreover, the controller is designed in a way that avoids the singularity problem in the system. Another advantage of the proposed scheme is its ability for tracking control while there exists control input saturation, which is a serious concern in robotic systems. Based on the Lyapunov theorem, the stability of the closed-loop system has also been confirmed. Lastly, the performance of the proposed control technique for the uncertain system in the presence of an external disturbance, unknown input saturation, and dynamic uncertainties has been investigated. Also, the proposed controller has been compared with a Fuzzy-PID one. Simulation results show the effectiveness and superiority of the developed control technique.

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Section: Introductionmentioning

confidence: 99%

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

et al. 2020

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“…On the other hand, the robustness of the control strategies plays an inevitable role in the performance of the systems. Hence, researchers also propound robust controllers for complex systems [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Optimal Reinforcement Learning-Based Control Algorithm for a Class of Nonlinear Macroeconomic Systems

et al. 2022

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Due to the vital role of financial systems in today’s sophisticated world, applying intelligent controllers through management strategies is of crucial importance. We propose to formulate the control problem of the macroeconomic system as an optimization problem and find optimal actions using a reinforcement learning algorithm. Using the Q-learning algorithm, the best optimal action for the system is obtained, and the behavior of the system is controlled. We illustrate that it is possible to control the nonlinear dynamics of the macroeconomic systems using restricted actuation. The highly effective performance of the proposed controller for uncertain systems is demonstrated. The simulation results evidently confirm that the proposed controller satisfies the expected performance. In addition, the numerical simulations clearly confirm that even when we confined the control actions, the proposed controller effectively finds optimal actions for the nonlinear macroeconomic system.

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“…Early resolution of uncertainty during an epidemic can lead to effective decision making and the rapid prioritization of actions [23] . Accordingly, the need for the development of robust and/or adaptive control methods [24] , [25] , [26] , [27] , [28] emerges to control dynamical systems in the presence of modeling uncertainties. For instance, an adaptive control scheme [29] has been employed for cancer chemotherapy to reduce the tumor volume.…”

Section: Introductionmentioning

confidence: 99%

State estimation-based control of COVID-19 epidemic before and after vaccine development

Rajaei

Raeiszadeh

Azimi

et al. 2021

Journal of Process Control

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In this study, a nonlinear robust control policy is designed together with a state observer in order to manage the novel coronavirus disease (COVID-19) outbreak having an uncertain epidemiological model with unmeasurable variables. This nonlinear model for the COVID-19 epidemic includes eight state variables (susceptible, exposed, infected, quarantined, hospitalized, recovered, deceased, and insusceptible populations). Two plausible scenarios are put forward in this article to control this epidemic before and after its vaccine invention. In the first scenario, the social distancing and hospitalization rates are employed as two applicable control inputs to diminish the exposed and infected groups. However, in the second scenario after the vaccine development, the vaccination rate is taken into account as the third control input to reduce the susceptible populations, in addition to the two objectives of the first scenario. The proposed feedback control measures are defined in terms of the hospitalized and deceased populations due to the available statistical data, while other unmeasurable compartmental variables are estimated by an extended Kalman filter (EKF). In other words, the susceptible, exposed, infected, quarantined, recovered, and insusceptible individuals cannot be identified precisely because of the asymptomatic infection of COVID-19 in some cases, its incubation period, and the lack of an adequate community screening. Utilizing the Lyapunov theorem, the stability and bounded tracking convergence of the closed-loop epidemiological system are investigated in the presence of modeling uncertainties. Finally, a comprehensive simulation study is conducted based on Canada’s reported cases for two defined timing plans (with different treatment rates). Obtained results demonstrate that the developed EKF-based control scheme can achieve desired epidemic goals (exponential decrease of infected, exposed, and susceptible people).

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Stabilization of nonlinear vibrations of carbon nanotubes using observer-based terminal sliding mode control

Cited by 36 publications

References 50 publications

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

Optimal Reinforcement Learning-Based Control Algorithm for a Class of Nonlinear Macroeconomic Systems

State estimation-based control of COVID-19 epidemic before and after vaccine development

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