FAT-based robust adaptive control of cooperative multiple manipulators without velocity measurement

Deylami, Ali; Izadbakhsh, Alireza

doi:10.1017/s0263574721001338

Cited by 15 publications

(10 citation statements)

References 32 publications

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“…indicates tracking error of the motors' position. Replacing (34) with (33), using (31), and noticing that…”

Section: Fictitious Controller Design 𝛉 1dmentioning

confidence: 99%

“…The function approximation technique (FAT) is a new algorithm to approximate a large number of functions, involving uncertainties. Differential equations, 28,29 Legendre polynomials, 30 and Fourier series 31 are some of these FAT-based algorithms. The present study recommends an adaptive control algorithm for collaborative multiple elastic-joint robots on the basis of FAT.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…indicates tracking error of the motors' position. Replacing (34) with (33), using (31), and noticing that…”

Section: Fictitious Controller Design 𝛉 1dmentioning

confidence: 99%

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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“…Due to the approximation error, σ-modification is used for the adaptation rules (Izadbakhsh and Nikdel, 2021; Izadbakhsh, 2021; Deylami and Izadbakhsh, 2021). Therefore, consider the adaptation laws given by equations (57) and (58).…”

Section: Stability Analysis and Performance Evaluationmentioning

confidence: 99%

Adaptive control of robot manipulators driven by permanent magnet synchronous motors using orthogonal functions theorem

Khorashadizadeh

Zirkohi²,

Eliasi

et al. 2022

Journal of Vibration and Control

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This paper is devoted to designing a simple tracking controller for robot manipulators driven by permanent magnet synchronous motors (PMSMs) using orthogonal functions. The whole control system including robot manipulator and PMSMs is formulated in such a way that the voltages along the “d” and “q” axes of the PMSMs are considered as the control inputs. In the core of the proposed approach, uncertainties including external disturbances and system dynamics are estimated and compensated using orthogonal functions. Based on the Lyapunov stability theory, the unknown coefficients of the basis functions are tuned online using the derived adaptation laws. A performance evaluation is presented to guarantee the satisfactory operation of the proposed controller in the transient state. The case study is a spherical manipulator driven by PMSMs. The comparative simulation results confirm that the proposed control approach shows a superior performance regarding tracking objectives with a less computational burden.

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“…One of the model-free control approaches that have been considered in recent years to confront the drawbacks of fuzzy or neural network approaches is the function approximation technique (FAT). In this approach, Szász−Mirakyan operators, Fourier series, and polynomials are used for approximating functions such as uncertainty [23, 24, 25, 26]. These approaches have simpler structures in comparison with fuzzy or neural network methods, and therefore, are ideal choices for controlling industrial systems.…”

Section: Introductionmentioning

confidence: 99%

FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

2022

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This article proposes a robust and adaptive controller for industrial robot arms with multiple degrees of freedom without the need for velocity measurement. Many of the controllers designed for manipulators are model-based and require detailed knowledge of the system model. In contrast to these methods, this paper proposes a model-free controller using the Philips q-Bernstein operator as universal approximator. The designed controller can approximate uncertainties including external disturbances and unmodeled dynamics based on its universal approximation capability. Besides, most of the controllers revealed for robot arms are torque-based, which is not a realistic presumption from a practical point of view. In the proposed control method, the voltage applied to the actuator is considered as the control signal. However, unlike many voltage-based methods, the need to know the exact models of the system and the actuator has been eliminated in the presented method. Also, adaptive rules are extracted during the Lyapunov analysis to ensure system stability. Finally, to analyze the performance of the presented controller, this method is simulated for an industrial robot arm, and the results are analyzed. The proposed methodology is also compared to those of a strong state-of-the-art approximator, the Chebyshev neural network.

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FAT-based robust adaptive control of cooperative multiple manipulators without velocity measurement

Cited by 15 publications

References 32 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

Adaptive control of robot manipulators driven by permanent magnet synchronous motors using orthogonal functions theorem

FAT-based robust adaptive controller design for electrically direct-driven robots using Phillips q-Bernstein operators

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