Fuzzy Sliding Mode Controller Design for a Cooperative Robotic System With Uncertainty for Handling an Object

Farahmandrad, Maryam; Ganjefar, Soheil; Talebi, Heidar Ali; Bayati, Mahdi

doi:10.1115/1.4042742

Cited by 12 publications

(9 citation statements)

References 13 publications

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“…q 𝜌 = 0.9 and p 𝜌 = 1 are parameters of the approximator. The learning rate matrices are elected as 18 with I (⋅) denoting the identity matrix. Here, the error of the approximations is assumed to be neglectable.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Bayat

Izadbakhsh

2023

Intl J Robust & Nonlinear

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This paper presents the position-force tracking control problem of cooperative multiple elastic-joint robotic arms, encountering model nonlinearities, unknown perturbations, and dynamic uncertainties. To this mean, an adaptive function approximation technique (FAT) is proposed, authorizing the reference trajectory to be tracked by the object. This potential originates from the universal approximation feature of the FAT-based methodologies. The (p, q)-analogue of the Bernstein operators is utilized for this purpose. Since the systems' parameters are not precisely known, adaptive laws are employed for adjusting the uncertainty coefficients. The Lyapunov stability theorem assures that all signals in the error space remain uniformly ultimately bounded (UUB). In the end, two elastic-joint arms carrying a rigid object are utilized for validation of the theoretical outcomes. The suggested strategy is also compared to the Chebyshev neural network (CNN). The results show the benefit of the existing method, managing the system even in the presence of disturbances and uncertainties with lower tuning parameters compared with CNN.

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Section: Simulation Resultsmentioning

confidence: 99%

“…A fuzzy sliding mode control strategy has been advised for the problem of moving an item by the cooperative arms. 18 Reference 19 established an admission model for cooperative robotic systems. Next, an adaptive neural network is hired for uncertainty estimation.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Bayat

Izadbakhsh

2023

Intl J Robust & Nonlinear

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“…When the LTR is equal to 0, the vehicle has a stable roll dynamic, and the risk becomes higher with the increase of LTR [25]. The threshold of LTR is defined as Q, then the switch control strategy can be illustrated in Equation (8). In order to ensure sufficient time for the controller to react before the rollover danger occurs, Q is set to 0.8 in this paper.…”

Section: Switch Control Strategymentioning

confidence: 99%

Sliding Mode Switch Control of Adjustable Hydro-Pneumatic Suspension based on Parallel Adaptive Clonal Selection Algorithm

Zhou

Liu

et al. 2020

Applied Sciences

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The hydro-pneumatic suspension, as a widely used suspension for heavy vehicles, has been taken seriously by researchers for a long time because it is crucial in terms of handling stability, riding comfort, and driving safety of these vehicles. Most previous studies only discussed the control of ride comfort or vehicle handling stability of the suspension system separately. This article proposes a dynamic switch control strategy which can switch between ride comfort and handling stability controllers under different road surfaces and driving conditions. The load transfer ratio (LTR) is selected as the switch performance index, and it is calculated through a six-degrees-of-freedom (6-DOF) model. The ride comfort and handling stability controller of the hydro-pneumatic suspension are designed based on the sliding mode control theory. The objective functions of parameters optimization of the sliding mode controller (SMC) are obtained by means of analytic hierarchy process (AHP), and then the controller's parameters are optimized by the parallel adaptive clonal selection algorithm (PACSA). The simulation results based on MATLAB/Simulink show that: (1) the PACSA performs better than a genetic algorithm in terms of the parameters optimization of the SMC; (2) the proposed switch control strategy can simultaneously improve the ride comfort and handling stability under several typical steering maneuvers and various road profiles compared with the conventional SMC-controlled suspension. thus reduce the rollover propensity. Awad, Magdy Naeem [5] designed a hydro-pneumatic energy harvesting suspension system based on a PID-PSO (particle swarm optimization) controller, which achieved the maximum riding comfort as the peak values of the acceleration.However, past research on adjustable hydro-pneumatic suspension mainly focused on the analysis of vehicles' ride comfort or handling stability under different operating conditions separately, because it is difficult to achieve optimality in both aspects of ride comfort and handling stability due to the conflicting relationship between them. Hence, it is urgent to concentrate on the dynamic switch control strategy between the vehicle's ride comfort and handling stability to meet different requirements of working conditions.As a powerful controller, the sliding mode controller (SMC) has a great use in control systems because of its good robustness to external disturbance and fast dynamic response. Pradhan, Subarni [6] applied a composite SMC for wind power extraction in a remotely located solar PV-wind hybrid system. Biricik, Samet [7] used an SMC three-phase dynamic voltage restorer with adaptive notch filter to protect sensitive loads. Farahmandrad, Maryam [8] designed a fuzzy SMC for a cooperative robotic system with uncertainty for handling an object. Abtahi, S. Mahdi [9] suppressed chaotic vibrations in a suspension system of vehicle dynamics based on the optimal SMC. Inspired by the previous studies mentioned above, an SMC is used to control the hydro-pneumatic suspension system...

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“…Sliding mode approach can be easily combined by other controllers and mathematical tools. For example, in [8][9][10], fuzzy sliding mode controllers for cooperative robots have been introduced. A sliding mode controller based on low-pass filter has been presented in [11].The performance of integral sliding mode on cooperative robot manipulators has been investigated in [12].…”

Section: Introductionmentioning

confidence: 99%

Observer based versus Non-Observer based Adaptive Control of Electrically Driven Cooperative Manipulators using q-analogue of Bernstein-Schurer-Stancu operator as Uncertainty approximator

Izadbakhsh

Deylami

Khorashadizadeh

2022

Preprint

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In this paper, q-analogue of Bernstein-Schurer-Stancu operator is proposed for uncertainty estimation in adaptive control of cooperative electrically driven manipulators. The contribution of this paper is reducing the number of signals needed in construction of the regressor vector for uncertainty estimation due to the simplicity of q-analogue of Bernstein-Schurer-Stancu operator in comparison with other alternatives. It is verified that uniformly ultimately bounded stability of the tracking/approximation errors can be assured if the q-analogue of Bernstein-Schurer-Stancu operator is utilized as regressors. Moreover, it has been assumed that velocity signals are not available and an observer is needed to estimate these signals. Different trajectories including simple and complicated paths have been tested in simulation results. The results are also compared with the case in which velocity signals are in hand to show the accuracy and efficiency of the proposed observer-controller structure

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Fuzzy Sliding Mode Controller Design for a Cooperative Robotic System With Uncertainty for Handling an Object

Cited by 12 publications

References 13 publications

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Cooperative multiple elastic‐joint arms control using the (p, q)‐analogue of Bernstein operators as the uncertainty approximator

Sliding Mode Switch Control of Adjustable Hydro-Pneumatic Suspension based on Parallel Adaptive Clonal Selection Algorithm

Observer based versus Non-Observer based Adaptive Control of Electrically Driven Cooperative Manipulators using q-analogue of Bernstein-Schurer-Stancu operator as Uncertainty approximator

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