Multi-Objective Evolutionary Optimization of PID Controller by Chaotic Particle Swarm Optimization

Gholipour, Reza; Addeh, Jalil; Mojallali, Hamed; Khosravi, Alireza

doi:10.7763/ijcee.2012.v4.614

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…The sudden change in these voltages is due to compensation of the external disturbance. To have a quantitative comparison, consider the criterion J = ∫ 0 40 [ ∑ i | e i | ] dt (Gholipour et al, 2012, 2013; Chen et al, 2017). For the Fourier series expansion, we have J = 0 . 0195 and for Legendre polynomials we have J = 0 . 0306 .…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust dynamic sliding mode control of robot manipulators using the Fourier series expansion

Shokoohinia

Fateh

2018

Transactions of the Institute of Measurement and Control

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This paper presents a robust dynamic sliding mode control for electrically driven robot manipulators. The control law computes the motor voltages based on the voltage control strategy. Uncertainties are estimated using the Fourier series expansion and the truncation error is compensated. The Fourier coefficients are tuned based on the stability analysis. The contribution of this paper is designing a robust controller using a novel adaptive Fourier series expansion. In comparison with previous related works based on the Fourier series expansion, the superiority of this paper is presenting an adaptation law for the fundamental frequency of the Fourier series expansion and, consequently, relaxing the need for trial and error procedure in its tuning. The case study is a SCARA (Selective Compliant Articulated Robot for Assembly) robot manipulator actuated by permanent magnet DC electrical motors. The influence of uncertainty estimation based on the Fourier series expansion instead of using sign function is studied. Simulation results verify robust and satisfactory performance of the proposed controller.

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Section: Simulation Resultsmentioning

confidence: 99%

Robust dynamic sliding mode control of robot manipulators using the Fourier series expansion

Shokoohinia

Fateh

2018

Transactions of the Institute of Measurement and Control

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“…In which x = sin ( t∕10) . In order to have a quantitative comparison, consider the criterion Fitness = ∫ 10 0 � ∑ i e 2 i � dt [43][44][45][46][47][48][49][50][51][52]. All the controller parameters are the same as those presented in simulation 1.…”

Section: Simulation 2: Comparison Between Different Uncertainty Estimmentioning

confidence: 99%

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

2020

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In this work, an adaptive dynamic sliding mode control approach is proposed for robotic systems via uncertainty estimators with exponential convergence rate. The uncertainties are estimated using various uncertainty estimators such as the Fourier series expansion, Legendre polynomials and adaptive fuzzy systems. Also, for each uncertainty estimator, the approximation error is compensated. The adaptation laws are derived using a stability analysis. Moreover, the asymptotic convergence of the tracking error and the boundedness of all closed-loop signals are guaranteed. The novelty of this paper is proposing a positive exponential function for the convergence rate of the adaptation rules to prevent from initial high voltages originated from large initial tracking errors. Another novelty of this paper is presenting a robust control term for the truncation error that improves the accuracy of the control system. Analysis of simulations reveals the effectiveness of the proposed method in terms of fast disturbance rejection and negligible tracking error.

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“…An optimal nonlinear controller for PMSM has been designed in (Do et al, 2015), which is based on the external disturbance estimation by observers. A PID controller (Gholipour et al, 2012) was proposed in (Verastegui-Galván et al, 2018) for one-degree-of-freedom robot manipulators driven by PMSMs (Verastegui-Galván et al, 2018). In (Ciabattoni et al, 2014), a discrete-time controller based on Neural Networks for PMSMs is proposed.…”

Section: Introductionmentioning

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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Multi-Objective Evolutionary Optimization of PID Controller by Chaotic Particle Swarm Optimization

Cited by 7 publications

References 35 publications

Robust dynamic sliding mode control of robot manipulators using the Fourier series expansion

Robust dynamic sliding mode control of robot manipulators using the Fourier series expansion

Design of an adaptive dynamic sliding mode control approach for robotic systems via uncertainty estimators with exponential convergence rate

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

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