Micro-positioning of linear-piezoelectric motors based on a learning nonlinear PID controller

Tan, Kok Kiong; Lee, Tong Heng; Zhou, Huixing

doi:10.1109/3516.974856

Cited by 110 publications

(46 citation statements)

References 18 publications

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“…5, statistical performance of GSA. Our research shows the combination of elevation and azimuth i.e., vertical and horizontal which is better than the errors reported in previous researches [15][16][17], where the error values are reported separately (Vertical or Horizontal). Hence from the results it is observed that by optimizing the α and δ, the results are better than in previous researches [12,13] reported without optimizing α and δ. PSO converges faster in the least optimal parameter in all the cases.…”

Section: Resultscontrasting

confidence: 76%

“…Utilizing Nonlinear PID controller for tuning Linear Piezoelectric motors & Superconducting Magnetic Energy Storage have been discussed [15,16] and their research reveals that Nonlinear PID controller shows better performance than conventional linear PID controller. But the nonlinear constants used in the above research findings are fixed and not optimized [14][15][16]. Therefore in our study, tuning of Nonlinear PID controller for MIMO systems is considered and EC methods are used to optimize the nonlinear PID controller parameters.…”

Section: Introductionmentioning

confidence: 99%

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Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

2018

Teh. vjesn.

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Abstract:In this paper, the Twin rotor MIMO system (TRMS) is tuned by Nonlinear PID controller using Evolutionary Computation methods. The proposed Nonlinear PID controller, used to tune TRMS, improves the system performance with additional degrees of freedom. Evolutionary Computation methods such as Differential Search Algorithm (DSA), real coded Genetic Algorithm (RGA) with simulated binary crossover (SBX) and Particle Swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are used to determine the optimal parameters of the proposed controller by minimizing Integral Square Error (ISE) for rotor response of TRMS. SIMULINK MATLAB software is used for simulating the system. The statistical performance of the controller is analysed among twenty independent trials by taking best, worst, mean and standard deviations of ISE. Simulation results reveal that TRMS system tuned by nonlinear PID controller using Particle Swarm optimization (PSO) is better than the other methods

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Section: Resultscontrasting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

2018

Teh. vjesn.

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“…Various PID tuning techniques has been reported in PEA positioning control applications, e.g. by trial and error [20], by grey relational analysis [105], using an optimal linear quadratic regulation method [106], by a semiautomatic tuning technique [107], and by an automatic tuning technique [108]. However, in broadband operations with large system uncertainties including modeling errors, nonlinearities, external loads, etc., advanced control techniques are required because PID control is limited in bandwidth while dealing with uncertainties [109].…”

Section: Feedbackmentioning

confidence: 99%

A Survey of Modeling and Control of Piezoelectric Actuators

Peng¹,

Xiongbiao²

2013

MME

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Piezoelectric actuators (PEAs) have been widely used in micro-and nanopositioning applications due to their fine resolution, fast responses, and large actuating forces. However, the existence of nonlinearities such as hysteresis makes modeling and control of PEAs challenging. This paper reviews the recent achievements in modeling and control of piezoelectric actuators. Specifically, various methods for modeling linear and nonlinear behaviors of PEAs, including vibration dynamics, hysteresis, and creep, are examined; and the issues involved are identified. In the control of PEAs as applied to positioning, a review of various control schemes of both model-based and non-model-based is presented along with their limitations. The challenges associated with the control problem are also discussed. This paper is concluded with the emerging issues identified in modeling and control of PEAs for future research.

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“…The PID control is widely used because of its simple construction [52]. Tan [53] proposed a learning type of PID controller, and tried to enhance the robustness of the system.…”

Section: Control Methods Without the Inverse Hysteresis Modelmentioning

confidence: 99%

Recent Advances in the Control of Piezoelectric Actuators

Chi

2014

International Journal of Advanced Robotic Systems

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The micro/nano positioning field has made great progress towards enabling the advance of micro/nano technology. Micro/nano positioning stages actuated by piezoelectric actuators are the key devices in micro/nano manipulation. The control of piezoelectric actuators has emerged as a hot topic in recent years. Piezoelectric materials have inherent hysteresis and creep nonlinearity, which can reduce the accuracy of the manipulation, even causing the instability of the whole system. Remarkable efforts have been made to compensate for the nonlinearity of piezoelectric actuation through the mathematical modelling and control approaches. This paper provides a review of recent advances on the control of piezoelectric actuators. After a brief introduction of basic components of typical piezoelectric micro/nano positioning platforms, the working principle and modelling of piezoelectric actuators are outlined in this paper. This is followed with the major control method and recent progress is presented in detail. Finally, some open issues and future work on the control of piezoelectric actuators are extensively discussed.

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Micro-positioning of linear-piezoelectric motors based on a learning nonlinear PID controller

Cited by 110 publications

References 18 publications

Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

A Survey of Modeling and Control of Piezoelectric Actuators

Recent Advances in the Control of Piezoelectric Actuators

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