Adaptive Neural Observer-Based Nonsingular Terminal Sliding Mode Controller Design for a Class of Nonlinear Systems

Karami, Hamede; Ghasemi, Reza; Mohammadi, Fazel

doi:10.1109/iccia49288.2019.9030958

Cited by 8 publications

(7 citation statements)

References 34 publications

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“…Among the classical approaches, we can list the Proportional-Integral-Derivative (PID) control [4][5][6], adaptive control [7,8], backstepping control [9,10] and Sliding Mode Control (SMC) [11]. Among the intelligent methods, neural networks [12][13][14] and fuzzy-logic approaches [15][16][17] are the most popular. In the approaches that combine the intelligent and classic methods, we can list adaptive neural network [18] and fuzzy sliding mode control [19].…”

Section: Background and Motivationmentioning

confidence: 99%

Adaptive Integral-Type Terminal Sliding Mode Tracker Based on Active Disturbance Rejection for Uncertain Nonlinear Robotic Systems With Input Saturation

Karami

Alattas

Mobayen³

et al. 2021

IEEE Access

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This paper proposes an adaptive integral-type terminal sliding mode tracking control approach based on the active disturbance rejection for uncertain nonlinear systems subject to input saturation and external disturbances. Its main objective is to achieve zero tracking error in the presence of external disturbances, parametric uncertainties and input saturation; ubiquitous problems in most practical engineering systems. The proposed approach combines the robustness and chattering-free dynamics of adaptive integraltype sliding mode control with the estimation properties of a nonlinear extended state observer. It also assumes the bounds of the input saturation to be unknown. The asymptotic stability of the closed-loop system in the presence of disturbances, uncertainties and input saturation is proven using the Lyapunov theorem. The effectiveness of the proposed approach is assessed using a flexible-link robotic manipulator. The obtained results confirmed the robustness and god tracking performance of the proposed approach. Robustness, chattering-free dynamics and good tracking performance albeit input saturation are among the main features INDEX TERMS Active disturbance rejection; nonlinear system; terminal sliding mode control; input saturation; flexible-link manipulator.

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Section: Background and Motivationmentioning

confidence: 99%

Adaptive Integral-Type Terminal Sliding Mode Tracker Based on Active Disturbance Rejection for Uncertain Nonlinear Robotic Systems With Input Saturation

Karami

Alattas

Mobayen³

et al. 2021

IEEE Access

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“…The initial population size for each technique is considered as 1000. It should be noted that finite-time and fast convergence is an important capability of any algorithm in practical tests [37][38][39]. Figures 11-14 show a summary of the comparisons among the proposed method and the multi-objective PSO, DE, and MINLP methods.…”

Section: Performance Evaluation Using Different Heuristic Techniquesmentioning

confidence: 99%

Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm

Nguyen

Mohammadi

2020

Sustainability

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Electricity demand has been growing due to the increase in the world population and higher energy usage per capita as compared to the past. As a result, various methods have been proposed to increase the efficiency of power systems in terms of mitigating congestion and minimizing power losses. Power grids operating limitations result in congestion that specifies the final capacity of the system, which decreases the conventional power capabilities between coverage areas. Flexible AC Transmission Systems (FACTS) can help to decrease flows in heavily loaded lines and lead to lines loadability improvements and cost reduction. In this paper, total power loss reduction and line congestion improvement are assessed by determining the optimal locations and compensation rates of Thyristor-Controlled Series Compensator (TCSC) devices using the Multi-Objective Genetic Algorithm (MOGA). The results of applying the proposed method on the IEEE 30-bus test system confirmed the efficiency of the proposed procedure. In addition, to check the performance, applicability, and effectiveness of the proposed method, different heuristic algorithms, such as the multi-objective Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, and Mixed-Integer Non-Linear Program (MINLP) technique, are used for comparison. The obtained results show the accuracy and fast convergence of the proposed method over the other heuristic techniques.

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“…Where C is the standard capacitor on the DC grid side and v c is the voltage across the common capacitor, now by taking the derivatives out from the above equations, we can define the state equations as [15,16],…”

Section: Dc-dc Converter Modellingmentioning

confidence: 99%

“…Design of robust higher order sliding mode control strategy for hybrid microgrids were discussed by Cucuzzella et al and Baghaee et al [13,14]. Mohammadi et al also reported some works on power management strategy in multi-terminal VSC-HVDC system, adaptive neural observer-based nonsingular terminal sliding mode controller & intelligent controller design for a class of nonlinear systems, bidirectional power charging control strategy for plug-in hybrid EVs for MT-HVDC grids [15][16][17][18][19][20][21][22]. In proposed work, the constant switching SMC is described in which the switching losses are reduced as compared to the primitive SMC by maintaining a constant switching frequency.…”

Section: Introductionmentioning

confidence: 99%

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

Panda

Ghosh

2020

Technol Econ Smart Grids Sustain Energy

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Power electronics converter and inverter systems for microgrid operations are always stochastic. When the system is operating in grid-tied mode, the attached load is not constant, so the current which drawn from the DC grid also varies leads to a voltage drop and that causes steady-state error in between the actual and desired output voltage in microgrid. DC-DC bidirectional converters are widely used in microgrid application that facilitates bidirectional energy convertion which having boost mode operation in one direction and bucking mode in other operation. The Interfacing converter is controlled by using the Maximum Power Point Tracking (MPPT) control technique for obtaining maximum power during different atmospheric conditions. In this work, a Model Predictive Control (MPC) strategy is proposed which having an improved cost function, single prediction horizon, and an observer to keep tracking the output voltage. Here, MPC is proposed for cumulative control in between the DC grid side converters and AC grid side inverter, and for improving the power quality and reliability of the utility grid. Both continuous time and discrete time domain models are formed for the whole system for applying different control strategies. Along with MPC, the system has been controlled by using other controllers like as PI controller, Sliding Mode Controller (SMC). Luenberger observer is used to check the DC grid voltage when disturbances are implemented. Here, all the computational results are compared for justifying and validate a better controlling method for microgrid application. These kind of work is first time reported in this manuscript.

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Adaptive Neural Observer-Based Nonsingular Terminal Sliding Mode Controller Design for a Class of Nonlinear Systems

Cited by 8 publications

References 34 publications

Adaptive Integral-Type Terminal Sliding Mode Tracker Based on Active Disturbance Rejection for Uncertain Nonlinear Robotic Systems With Input Saturation

Adaptive Integral-Type Terminal Sliding Mode Tracker Based on Active Disturbance Rejection for Uncertain Nonlinear Robotic Systems With Input Saturation

Optimal Placement of TCSC for Congestion Management and Power Loss Reduction Using Multi-Objective Genetic Algorithm

A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application

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