Combined H&lt;inf&gt;&amp;#x221E;&lt;/inf&gt;-feedback and eterative learning control design with application to nanopositioning systems

Helfrich, B.E.; Lee, C.; Bristow, Douglas A.; Xiao, Xiang; Dong, Jingyan; Alleyne, Andrew G.; Salapaka, Srinivasa M.; Ferreira, Placid M.

doi:10.1109/acc.2008.4587101

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…The disturbance observer-based controls find extensive application in motion control applications. The disturbance observer determines the error in the displacement of the piezoelectric device based on the reference input and the output displacement [180,[197][198][199][200]. The control value of the disturbance observer is the position value of the piezoelectric actuator.…”

Section: Disturbance Observer-based Pid Controllermentioning

confidence: 99%

Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review

Kanchan,

Santhya,

Bhat

et al. 2023

Technologies

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Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects.

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Section: Disturbance Observer-based Pid Controllermentioning

confidence: 99%

Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review

Kanchan,

Santhya,

Bhat

et al. 2023

Technologies

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“…The latest works have shown the superiority of the ILC algorithm as the feedforward controller of the industrial manipulator. Helfrich et al proposed an ILC approach that works together with the H ∞ feedback controller [19] to improve control performance. A dual-loop ILC controller is designed by Meng et al to compensate the vibrations during tracking [20].…”

Section: Introductionmentioning

confidence: 99%

Learning Feedforward Control for Industrial Manipulators

Liu

Popov

Turner

et al. 2021

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)

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In this work, an iterative learning control (ILC) algorithm is proposed for industrial manipulators. The proposed ILC algorithm works coordinately with the inverse dynamics of the manipulator and a feedback controller. The entire control scheme has the ability of compensating both repetitive and non-repetitive disturbances; guaranteeing the control accuracy of the first implementation; and improving the control accuracy of the manipulator progressively with successive iterations. In order to build the the convergence of the proposed ILC algorithm, a composite energy function is developed. A case study on a four degree of freedom industrial manipulator is demonstrated to illustrate the effectiveness of the proposed control scheme. By implementing the ILC algorithm, the maximum root mean square error of the control accuracy is improved from 0.0262 rad to 0.0016 rad within ten iterations.

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“…Recently, targeting at the FFC of industrial manipulators, several works have been reported to show the superiority of ILC. For instance, an ILC approach combined with a H ∞ FBC is proposed in [15] to improve the control performance of nanopositioning system. In [16], the authors establish an ILC scheme in combination with low-gain FBC to improve tracking accuracy for robotic manipulators.…”

Section: Introductionmentioning

confidence: 99%

“…By fully utilising the system knowledge and the system repetition, the proposed controller possesses the ability to achieve high-precision tracking tasks despite the presence of various system uncertainties and external disturbances. 2) In contrast to the existing works [15]- [19] where the ILC is utilised to replace the inverse dynamics directly, the proposed ILC scheme is designed to work in parallel with the inverse dynamics to fully use the available system information. Hence, it is able to enhance the control performance of the FFC by nullifying the effect of the system uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator

Liu

Wang

et al. 2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunovlike composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78 • to 1.09 • , and 21.09% to 3.99%, respectively, within three iterations.

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Combined H<inf>∞</inf>-feedback and eterative learning control design with application to nanopositioning systems

Cited by 12 publications

References 15 publications

Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review

Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review

Learning Feedforward Control for Industrial Manipulators

Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator

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