Robust Nonlinear Controller of the Speed for Double Star Induction Machine in the Presence of a Sensor Fault

Oumar, Aichetoune; Chakib, Rachid; Labbadi, Moussa; Cherkaoui, Mohamed

doi:10.22266/ijies2020.0630.12

Cited by 4 publications

(6 citation statements)

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“…The number of DFIG pole pairs is p, and the slip is g. The stator, rotor, and magnetizing inductances are Ls, Lr, and M, respectively. The electromagnetic torque can be given by [19]: The active and reactive powers of the stator and rotor are represented as follows [3,19]:…”

Section: Doubly-fed Induction Generator Modelingmentioning

confidence: 99%

Design of Optimal Backstepping Control for a Wind Power Plant System Using the Adaptive Weighted Particle Swarm Optimization

2021

IJIES

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Section: Doubly-fed Induction Generator Modelingmentioning

confidence: 99%

Design of Optimal Backstepping Control for a Wind Power Plant System Using the Adaptive Weighted Particle Swarm Optimization

2021

IJIES

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“…The controller can be divided into two main parts, which are the discontinues part ( ) that used to design suitable tracking performance based on linear methodology requiring very fast switching. The second part is equivalent controller ( ) which is the effect of nonlinear terms that induced reliability and used to fine-tune the sliding surface slopes , sometimes causes system instability and chattering phenomenon [7]. Let us define the nonlinear input single for dynamic system as follow:…”

Section: Design Of Mathematical Model Of Smcmentioning

confidence: 99%

“…Several techniques have been suggested to reduce chattering in sliding mode controller, to minimize the output error and increased the system dynamic response. The Integral sliding mode controller ISMC was suggested in this work to boost the manipulator output to accomplish the desired tasks with high stability [7]. With the emergence of smart algorithm and optimization theories, modern smart optimization techniques have played an increasingly important role in tuning and modification of robot manipulator parameters.…”

Section: Introductionmentioning

confidence: 99%

Design Stable Controller for PUMA 560 Robot with PID and Sliding Mode Controller Based on PSO Algorithm

Akkar¹,

Haddad²

2020

IJIES

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The most significant challenge facing the researcher in the field of robotics is to control the robot manipulator with appropriate overall performance. This paper focuses mainly on the novel Intelligent Particle Swarm Optimization (PSO) algorithm that was used for optimizing and tuning the gain of conventional Proportional Integral Derivative (PID), and improve the parameters of dynamic design in Sliding Mode Control (SMC), which is considered a strong nonlinear controller for controlling highly nonlinear systems, particularly for multi-degree serial link robot manipulator. Additional modified Integral Sliding Mode Controller (ISMC) was implemented to the design of dynamic system with high control theory of sliding mode controller. Intelligent Particle Swarm Optimization (PSO) algorithm was introduced for developing the nonlinear controller. The algorithm demonstrates superior performance in determining the appropriate gains and parameters value in harmony with robot scheme dynamic layout in order to achieve suitable and stable nonlinear controller, besides reduce the chattering phenomenon. PUMA robot manipulator that was used as study case in this work, shows perfect result in step response, with acceptable steady state, and overshoot, besides, eliminating the disadvantage of chattering in conventional SMC. Matlab / Simulink presents to increase the speed of matrix calculation in forward, inverse kinematics and dynamic model of manipulator. Comparison was made between the proposed method with existing methods. Result shows that integral sliding mode with PSO (ISMC/PSO) gave best result for stable step response, minimum mean square error with best objective function, and stable torque.

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“…Generally, the strategies for handling nonlinearity are divided into 2 ways. First, a nonlinear control algorithm is directly applied [8][9][10][11]. Although it can approach the nonlinear characteristics, it has more difficult design processes because of the complexity of that system itself.…”

Section: A Introductionmentioning

confidence: 99%

T-S Fuzzy Tracking Control Based on H∞ Performance with Output Feedback for Pendulum-Cart System

Rosalinda

Agustinah

Alfathdyanto³

2023

ijcs

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In some practices, not all state variables are available because of limited or noisy measurements. Thus, via output feedback, an observer is used to estimate the unmeasured states. To apply linear controllers to the pendulum-cart system, the Takagi-Sugeno fuzzy model is utilized by linearizing the system in more than one operating point. The effect of disturbances on tracking performance is reduced to the prescribed attenuation level by H∞ performance. The stability of the whole closed-loop system is investigated using the Lyapunov function. Sufficient conditions are derived in terms of a set of Linear Matrix Inequality (LMI) to obtain the controller and observer gain. Simulation results show that the proposed control method can make the system track the sinusoidal reference signal, maintain stability, and attenuate the effect of disturbances to less than the prescribed attenuation level measured by L2 gain. In the implementation process, an adjustment is needed to move the observer’s pole and speed up the observer’s responses.

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Robust Nonlinear Controller of the Speed for Double Star Induction Machine in the Presence of a Sensor Fault

Cited by 4 publications

References 0 publications

Design of Optimal Backstepping Control for a Wind Power Plant System Using the Adaptive Weighted Particle Swarm Optimization

Design of Optimal Backstepping Control for a Wind Power Plant System Using the Adaptive Weighted Particle Swarm Optimization

Design Stable Controller for PUMA 560 Robot with PID and Sliding Mode Controller Based on PSO Algorithm

T-S Fuzzy Tracking Control Based on H∞ Performance with Output Feedback for Pendulum-Cart System

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