Nonlinear Adaptive Model Predictive Controller for a Flexible Manipulator: An Experimental Study

Pradhan, Santanu Kumar; Subudhi, Bidyadhar

doi:10.1109/tcst.2013.2294545

Cited by 74 publications

(31 citation statements)

References 34 publications

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“…Considering the error between the pseudo-linear system, consisted of ANN-Inversion and manipulator system, and the ideal linear system, which is resulted from the ANN's approximation error, we can describe the two composite pseudo-linear systems as the linear system with disturbances (3).…”

Section: Problem Formulationmentioning

confidence: 99%

“…The key to high dynamic and static performance of industrial robots is the good control algorithm. The most commonly used control schemes for the industrial robot include impedance control [1] , inverse control [2] , adaptive control [3] , PID control and so on.However,the above methods have certain limitations in actual use. In the impedance control scheme, the control accuracy depends on the operator precise understanding of environmental knowledge.…”

Section: Introductionmentioning

confidence: 99%

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ANN-inversion based fractional-order sliding control for the industrial robot

Huang

Zhou

2015

2015 34th Chinese Control Conference (CCC)

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To improve the control performance of the industrial robot, an ANN-inversion based fractional-order sliding mode control(FOSMC) scheme is proposed. Firstly, the BP neural network is used for approximating the inversion of the industrial robot to implement decoupling and linearization of the industrial robot. Secondly, the composite pseudo linear system, which is composed of the ANN-inversion system and the controlled industrial robot, is equivalent to a linear system with disturbance in view of the uncertainties of the industrial robot and the approximation error of the BP neural network. Then, two FOSMCs are designed respectively based on the SMC theory and fractional calculus for the two subsystems, and the stability analysis is given. Finally, case study is fulfilled under different conditions, and results show the effectiveness of the proposed control scheme.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ANN-inversion based fractional-order sliding control for the industrial robot

Huang

Zhou

2015

2015 34th Chinese Control Conference (CCC)

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“…Afshari et al [11] utilized a prediction error method to obtain the ARMAX model of a flexible beam bonded with piezoceramic actuator/sensor, and implemented a model reduction method to find the amenable reduced order. Pradhan and Ubudhi [12] developed a nonlinear ARMAX model of a planar two-link flexible manipulator system, and designed a nonlinear adaptive controller based on the identified model. Flexible structures are distributed parameter systems, thus have multiple vibration modes, which display highly resonant behavior near to the structures' natural frequencies.…”

Section: Introductionmentioning

confidence: 99%

Experimental Identification and Vibration Control of A Piezoelectric Flexible Manipulator Using Optimal Multi-Poles Placement Control

et al. 2017

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This paper presents experimental identification and vibration suppression of a flexible manipulator with piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined where the move distance of the closed loop poles is the shortest. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Accordingly, multi-mode vibrations of the manipulator are well attenuated.

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“…Afshari et al [11] utilized a prediction error method to obtain the ARMAX model of a flexible beam bonded with piezoceramic actuator/sensor, and implemented a model reduction method to find the amenable reduced order. Pradhan and ubudhi [12] developed a nonlinear ARMAX model of a planar two-link flexible manipulator system, and designed a nonlinear adaptive controller based on the identified model. As flexible structures are distributed parameter systems, thus have multiple vibration modes, which display highly resonant behavior near to the structures' natural frequencies.…”

Section: Introductionmentioning

confidence: 99%

Experimental Identification and Vibration Control of A Non-Collocated Piezoelectric Flexible Manipulator Using Optimal Multi-Poles Placement Control

Lou

Liao

Yang

et al. 2017

Preprint

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This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multipoles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

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Nonlinear Adaptive Model Predictive Controller for a Flexible Manipulator: An Experimental Study

Cited by 74 publications

References 34 publications

ANN-inversion based fractional-order sliding control for the industrial robot

ANN-inversion based fractional-order sliding control for the industrial robot

Experimental Identification and Vibration Control of A Piezoelectric Flexible Manipulator Using Optimal Multi-Poles Placement Control

Experimental Identification and Vibration Control of A Non-Collocated Piezoelectric Flexible Manipulator Using Optimal Multi-Poles Placement Control

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