Feedforward neural network position control of a piezoelectric actuator based on a BAT search algorithm

Svečko, Rajko; Kusić, Dragan

doi:10.1016/j.eswa.2015.02.061

Cited by 29 publications

(12 citation statements)

References 35 publications

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“…Adaptive Control Law. In this paper, the vibration suppression problem of a smart beam considering the hysteresis property is explored by using a model reference adaptive control (MRAC) [22,25]. For adaptive control, a reference model is assumed as an expected objective model.…”

Section: Model Reference Adaptive Control With the Bouc-wen Modelmentioning

confidence: 99%

“…In addition, feedback or feedforward control may be easily realized with its inverse model and a certain control law. e control laws applied to suppress vibration mainly include PID control [22], adaptive control [6,23], robust control [24], and intelligent control [25,26]. ese control laws have been used to compensate for the hysteresis property or track the expected trajectory.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Hysteresis on the Vibration Control of a Smart Beam with a Piezoelectric Actuator by the Bouc–Wen Model

Zhang

2020

Shock and Vibration

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The hysteresis property in a smart structure has attracted much attention from researchers for several decades. Hysteresis not only affects the response precision of the smart structure but also threatens the stability of the system. This paper focuses on how the hysteresis property influences the control effect of vibration suppression for a smart beam. Furthermore, the Bouc–Wen model is adopted to describe the hysteresis property of a smart beam and the hysteresis parameters of the hysteresis model are identified with a genetic algorithm. Based on the identification results, the hysteresis model is validated to represent the hysteresis property of the smart beam. Based on the hysteresis model, model reference adaptive control is designed to explore the influence of hysteresis on the vibration control of the smart beam. With some simulations and experiments, it is found that the vibration control effect is influenced when the hysteresis item changes. The vibration control effect will be improved when the hysteresis coefficient in the Bouc–Wen model, as the expected objective model of the adaptive reference model, is within a proper numerical range where the control system is stable. Furthermore, when the time delay is considered in the closed-loop control system, the principle of the hysteresis influence is different. The results indicate that the hysteresis property affects not only the control effect but also the stability of the control system for a smart cantilever beam.

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Section: Model Reference Adaptive Control With the Bouc-wen Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Hysteresis on the Vibration Control of a Smart Beam with a Piezoelectric Actuator by the Bouc–Wen Model

Zhang

2020

Shock and Vibration

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“…Neural networks have powerful capabilities such as input-output mapping, function approximation, and adaptability [ 36 , 37 , 38 , 39 ], which can approximate the system dynamics and the hysteresis nonlinear behavior. In [ 40 ], a feedforward neural network controller was proposed to improve the tracking precision of the PEA, and the bat search algorithm was adopted to decrease the computational burden. Simulation results indicated that this developed method could considerably improve the steady-state error and settling time.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Zhang

Gao

et al. 2020

Sensors

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Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.

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“…Hybrid swarm intelligence algorithms are formulated in Chaurasia and Singh (2015) to approach the network management issue. The recent algorithm developed based on bats (Yang, 2010) has a tremendous scope (Parija and Sahu, 2018) beyond the confines of usual non-communication related domains (Gao et al., 2016; Hossein Gandomi and Yang, 2012; Premkumar and Manikandan, 2015; Rahimi et al., 2016; Svecko and Kusic, 2015; Yang and He, 2013).…”

Section: Introductionmentioning

confidence: 99%

Reporting cell planning-based cellular mobility management using a Binary Artificial Bat algorithm

Swayamsiddha

Prateek

Singh

et al. 2019

Heliyon

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This paper attempts to present a novel application of Binary Artificial Bat algorithm for more effective location management in cellular networks. The location management is a mobility management task, which involves tracking of the mobile stations to locate their exact positions so that an incoming call or data can be routed to the intended mobile user. The location management cost comprises of the costs incurred by two processes, namely location registration and location search. This work focuses on network cost optimization, using Binary Artificial Bat algorithm for reporting cell planning strategy, which has not been reported yet. Results of the proposed algorithm have been compared with that of Binary Particle Swarm Optimization (BPSO) and Binary Differential Evolution (BDE) for some reference and realistic networks. The proposed approach is found to perform as good as other state-of-art techniques reported in the literature in terms of accuracy in solution, but it shows perceptible improvement in convergence speed.

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Feedforward neural network position control of a piezoelectric actuator based on a BAT search algorithm

Cited by 29 publications

References 35 publications

Influence of Hysteresis on the Vibration Control of a Smart Beam with a Piezoelectric Actuator by the Bouc–Wen Model

Influence of Hysteresis on the Vibration Control of a Smart Beam with a Piezoelectric Actuator by the Bouc–Wen Model

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Reporting cell planning-based cellular mobility management using a Binary Artificial Bat algorithm

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