Variable Structure Control Design for a Magnetic Levitation System

Al-Samarraie, Shibly Ahmed

doi:10.31026/j.eng.2018.12.08

Cited by 6 publications

(14 citation statements)

References 15 publications

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“…In terms of friction compensation, there are two types of schemes [23]: friction model-based and friction non-model-based schemes. The concluding methodology is employed when precise friction modeling is complicated or unnecessary, such as for variable structure control [24], PD control [25], and neural network control [26]. The model-based methodology [27] may be used if the friction parameter can be accurately identified to a certain degree.…”

Section: Introductionmentioning

confidence: 99%

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

et al. 2022

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In robotic manipulators, feedback control of nonlinear systems with fast finite-time convergence is desirable. However, because of the parametric and model uncertainties, the robust control and tuning of the robotic manipulators pose many challenges related to the trajectory tracking of the robotic system. This research proposes a state-of-the-art control algorithm, which is the combination of fast integral terminal sliding mode control (FIT-SMC), robust exact differentiator (RED) observer, and feedforward neural network (FFNN) based estimator. Firstly, the dynamic model of the robotic manipulator is established for the n degrees of freedom (DoFs) system by taking into account the dynamic LuGre friction model. Then, a FIT-SMC with friction compensation-based nonlinear control has been proposed for the robotic manipulator. In addition, a RED observer is developed to get the estimates of robotic manipulator joints' velocities. Since the dynamic friction state of the LuGre friction model is unmeasurable, FFNN is established for training and estimating the friction torque. The Lyapunov method is presented to demonstrate the finite-time sliding mode enforcement and state convergence for a robotic manipulator. The proposed control approach has been simulated in the MATLAB/Simulink environment and compared with the system with no observer to characterize the control performance. Simulation results obtained with the proposed control strategy affirm its effectiveness for a multi-DoF robotic system with model-based friction compensation having an overshoot and a settling time less than 1.5% and 0.2950 seconds, respectively, for all the joints of the robotic manipulator.INDEX TERMS robotic manipulator, robust exact differentiator, feedforward neural network, fast integral sliding mode control, LuGre friction model, autonomous articulated robotic educational platform.

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Section: Introductionmentioning

confidence: 99%

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

et al. 2022

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“…Fuzzy-PI is designed to replace the discontinuous term to alleviate the chattering of SMC. Al-Samarraie [7] designed a positive continuous VSC with a predetermined steady-state error to sustain the steel ball of a Maglev in the presence of an external disturbance and parameter uncertainty. The control signal is the squared value of the electric current, which is considered in the design as a positive input constraint.…”

Section: Introductionmentioning

confidence: 99%

“…AVSCbf is compared to adaptive only VSC (AVSC) without the barrier function. It is also compared to the performance of the classical VSC designed earlier in Al-Samarraie [7]. The latter's design procedure was based on the knowledge of the upper bounds of the perturbations.…”

Section: Introductionmentioning

confidence: 99%

A Barrier Function-Based Variable Structure Control for Maglev System

Ridha¹,

Kadhim²

2022

JESA

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Magnetic levitation (Maglev) systems are widely employed in the industry especially in mechatronics systems for precise positioning and suspension. They are inherently unstable having nonlinear models with uncertain parameters and exposed to external disturbances. Therefore, high-performance robust control designs are recommended for these systems. An Adaptive Variable Structure Controller based on barrier function (AVSCbf) is designed for the first time in this work to control the displacement of the ball position of a disturbed Maglev system. This approach does not require prior knowledge of the disturbance upper bounds in the design procedure. The state space region defined by the barrier function is designed to be attractive and invariant. This feature is essential to reject disturbances and handle parametric uncertainties. The adaptive law is activated when the state trajectory is initiated outside the invariant set defined by the barrier function. The gain of the VSC is adapted according to an adaptation law, which considers the system input constraints. The control input is constrained to be a bounded positive quantity. The adaptive VSC is only applied during the reaching phase. Once the state reaches the invariant set, the barrier-function-based VSC is applied to confine the state inside it. The resulting overall controller is a chattering-free VSC since the barrier-function based VSC is continuous. The steady-state error is limited to a minimal value by only specifying the barrier function parameter. Numerical simulations are conducted to show the efficiency of the new approach. Three types of VSC controllers for the Maglev system are compared. AVSCbf is compared to the performance of adaptive only VSC without the barrier function (AVSC) and both are designed in this work. AVSCbf is also compared to the classical VSC performance from previous work in the literature. The results of the comparison showed the efficiency of the proposed controller.

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“…The author used variable structure control scheme based on Lyapunov function method to attenuate the chattering effect and therefore, pushing the position of the ball to different desired places. The researcher obtained acceptable steady-state error value for the ball position [5]. Developed of a control system for a single degree of freedom MSS to achieve a stable suspending of a steel ball at specified distance using IMC-based PID controller.…”

mentioning

confidence: 99%

Tuning of different controlling techniques for magnetic suspending system using an improved bat algorithm

Abbas¹

2020

IJECE

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In this paper, design of proportional- derivative (PD) controller, pseudo-derivative-feedback (PDF) controller and PDF with feedforward (PDFF) controller for magnetic suspending system have been presented. Tuning of the above controllers is achieved based on Bat algorithm (BA). BA is a recent bio-inspired optimization method for solving global optimization problems, which mimic the behavior of micro-bats. The weak point of the standard BA is the exploration ability due to directional echolocation and the difficulty in escaping from local optimum. The new improved BA enhances the convergence rate while obtaining optimal solution by introducing three adaptations namely modified frequency factor, adding inertia weight and modified local search. The feasibility of the proposed algorithm is examined by applied to several benchmark problems that are adopted from literature. The results of IBA are compared with the results collected from standard BA and the well-known particle swarm optimization (PSO) algorithm. The simulation results show that the IBA has a higher accuracy and searching speed than the approaches considered. Finally, the tuning of the three controlling schemes using the proposed algorithm, standard BA and PSO algorithms reveals that IBA has a higher performance compared with the other optimization algorithms

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Variable Structure Control Design for a Magnetic Levitation System

Cited by 6 publications

References 15 publications

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation

A Barrier Function-Based Variable Structure Control for Maglev System

Tuning of different controlling techniques for magnetic suspending system using an improved bat algorithm

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