Design of a Feedforward-Feedback Controller for a Piezoelectric-Driven Mechanism to Achieve High-Frequency Nonperiodic Motion Tracking

Fan, Yunfeng; Tan, U-Xuan

doi:10.1109/tmech.2019.2899069

Cited by 38 publications

(20 citation statements)

References 38 publications

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“…With equation (6), the gradient of the cost function J( i ρ) with respect to the parameter in the i th iteration i ρ can be derived as…”

Section: A Gradient Calculation and Estimationmentioning

confidence: 99%

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

Zhu

et al. 2020

IEEE/ASME Trans. Mechatron.

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The performance achieved with traditional modelbased control system design approaches typically relies heavily upon accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task; and the usually necessary practical approximations often renders the automation system to operate in a non-optimal condition. This problem can be greatly aggravated in the case of a multi-axis magneticallylevitated (maglev) nanopositioning system where the fully floating behavior and multi-axis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under non-optimal conditions. These motion data essentially contain rich information; therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate towards optimality in a data-driven manner. Along this line then, this paper proposes a data-driven model-free controller optimization approach that learns from the past non-optimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multi-objective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In the work here, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.

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“…With equation (6), the gradient of the cost function J( i ρ) with respect to the parameter in the i th iteration i ρ can be derived as…”

Section: A Gradient Calculation and Estimationmentioning

confidence: 99%

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

Zhu

et al. 2020

IEEE/ASME Trans. Mechatron.

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“…Y. Fan et al purposed a radial basis function neural network (RBFNN) combined with ratedependent PI model, and a disturbance observer was designed for wide-bandwidth tracking control of PEA system with input frequencies from 1 to 100 Hz [12].…”

Section: Introductionmentioning

confidence: 99%

Model Reference Adaptive Control of Cross-Coupling Hysteresis in Piezoceramics With Dynamic Loads

et al. 2022

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During the ultraprecise cutting of micro-structure surface with fast tool servo (FTS), the hysteresis of piezoelectric actuators (PEAs) are affected by dynamic exciting characteristics and real-time cutting force, which declines the servo accuracy and cutting performance. In this paper, for a multi-inputsingle-output (MISO) cutting system, a cross-coupling rate-dependent Prandtl-Ishlinskii (CRPI) model is proposed and identified for the dynamic hysteresis of PEAs under dynamic voltage excitation and external loads. A model reference adaptive control method is then presented to eliminate the positioning nonlinearity of PEAs. The hysteresis modeling accuracy is discussed and the adaptive controller is validated through experiments.

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“…Mao et al 18 proposed a hysteresis compensator using the SVM. Fan et al 19 proposed Radial basis function neural network (RBFNN) model to compensate the rate-dependent hysteretic plant and hybrid with a PI controller to further attenuate tracking error. Wang et al 20 combined a feed-forward hysteresis compensation using a hybrid adaptive neuro-fuzzy and PID feedback control approach.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

2021

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This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement.First, a 3-layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions for selecting mutant coefficient F, crossover coefficient CR, and population size NP, is used to identify the inverse nonlinearity hysteresis structure of the PZT actuator. Second, a feed-forward control based on the identified model is proposed to compensate for the PZT hysteresis effect. Third, the Lyapunov stability principle is used to design and implement an adaptive law-based neural sliding mode model plus the feed-forward compensator to ensure that the whole PZT plant is operated in asymptotical stability. The experiment results demonstrate the proposed AIN controller proves superiority in comparison with other advanced control methods. K E Y W O R D S adaptive inverse neural controller, back-propagation, enhanced differential evolution, hybrid adaptive inverse neural control, Lyapunov stability concept

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Design of a Feedforward-Feedback Controller for a Piezoelectric-Driven Mechanism to Achieve High-Frequency Nonperiodic Motion Tracking

Cited by 38 publications

References 38 publications

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

Model Reference Adaptive Control of Cross-Coupling Hysteresis in Piezoceramics With Dynamic Loads

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

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