Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

Liu, Dongbo; Fang, Yu; Wang, Haibin

doi:10.1109/access.2020.2984645

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…Inversion-based control approach is one of possible solutions to deal with the hysteresis problem, in which the controlled subject can be modeled as two submodels, for instance a linear dynamics submodel and a hysteresis submodel. For the hysteresis submodel, several well-built mathematical models, such as Preisach model [3], Prandtl-Ishlinskii (PI) model [4,5], Bouc-Wen model [6], Duham model [7], and their modified ones [8][9][10][11], can be used to characterize the hysteresis effect. After that, the calculated inverse models are utilized to compensate for the hysteresis nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis inversion-free predictive compensation control for soft pneumatic actuators based on a global Koopman modeling strategy

Peng,

Chen,

Guan

et al. 2023

Phys. Scr.

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Soft pneumatic actuators (SPAs) have increasing applications in soft robotic design owing to their good compliance, excellent adaptability, and high force density characteristics. However, the inherent hysteresis nonlinearity severely degrades the control performance of SPAs. To compensate for the hysteresis effect, one solution is to build an inverse mathematical model. Nevertheless, in this method, the control performance still highly depends on the accuracy of the built inverse model. At the same time, the computational burden of deriving the inverse model is overwhelming. In addition, the physical constraints of the input pressure of SPAs are hardly handled by the inversion-based method. This paper proposes an inversion-free model predictive controller (IFMPC), which is designed based on a global Koopman linear model (GKLM). In the above GKLM-IFMPC strategy, the inverse hysteresis model is not required. Instead, a global hysteresis model can be established without considering the effect of rate-dependent property. Additionally, the control law is derived in an explicit form. With the constrained quadratic programming technique, the proposed method still works well when dealing with the physical constraints of SPAs. To verify the effectiveness of the proposed method, several comparative experiments are performed on a two-dimensional (2D) SPA. The results show that the proposed hysteresis global modeling and control framework has satisfactory tracking performance over some existing strategies even with strong hysteresis nonlinearity.

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Section: Introductionmentioning

confidence: 99%

Hysteresis inversion-free predictive compensation control for soft pneumatic actuators based on a global Koopman modeling strategy

Peng,

Chen,

Guan

et al. 2023

Phys. Scr.

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“…Gan et al (2016) added a quadratic polynomial to classical P-I model for characterizing both rate-dependent and rate-independent hysteresis. A Bouc–Wen model and a linear dynamic model are connected in series to describe the rate-dependent hysteresis characteristic of the PSA platform in many research studies (Gan and Zhang, 2018; Li et al, 2019; Liu et al, 2020; Long et al, 2017; Zhou et al, 2021). However, these models will increase the number of parameters.…”

Section: Introductionmentioning

confidence: 99%

Discrete-time rate-dependent hysteresis modeling and parameter identification of piezoelectric actuators

Shao

Wang

Zhu

2022

Transactions of the Institute of Measurement and Control

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A discrete-time-modified Bouc–Wen model is proposed to describe the non-symmetrical and rate-dependent hysteresis of piezoelectric actuators for micro-vibration control applications. The modified model combines a non-symmetrical Bouc–Wen model and a frequency-dependent dynamic module. A series of experiments are conducted to characterize the rate-dependent hysteresis of piezoelectric stack actuators under sinusoidal excitations at a range of 1 to 20 Hz. The experimental results verify the validity of the modified model. The modified Bouc–Wen model increases the complexity of Bouc–Wen hysteresis nonlinear differential equation, which brings difficulties to parameter identification. To identify the parameters of Bouc–Wen model, an improved hybrid differential evolution and Jaya (DE-Jaya) algorithm is introduced with a hybrid mutant operator and Jaya operator that tried to balance between convergence speed and solution accuracy. The improved algorithm is tested on benchmark functions and compared with other optimizations to prove its effectiveness. The comparison results show that hybrid DE-Jaya algorithm has better performance in convergence speed and solution accuracy. The identified discrete-time-modified Bouc–Wen model is used as the secondary path in a filtered-x variable step-size affine projection algorithm (FXVSSAPA), and experimental verifications are done on a micro-vibration control platform. The experimental results show that the FXVSSAPA algorithm can converge to the steady-state error faster and verify the effectiveness of the proposed discrete-time-modified Bouc–Wen model.

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“…Hysteresis compensation is complicated in these models due to the difficulty they present in deriving the inverse hysteresis model. On the other hand, physiological models, such as the Bouc-Wen [15], [16] and Duhem [17] models, require too many parameters to be identified [18], thereby making the identification process a difficult task. Recently, a System Level Model (SLM) was presented in [19], where hysteresis behavior is described through a single function that links the derivatives of the output and input quantities.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

Ahmed

Yan

2021

IEEE Access

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A modeling and parameter identification method for rate dependent hysteresis of piezoelectric actuated nano-stage is presented in this work. A system level quasi-static hysteresis model is employed to construct a neural network. To better describe the rate dependent behavior of hysteresis in piezoelectric actuated stage, a Nonlinear AutoRegressive Moving Average with eXogenous input (NARMAX) based dynamic model is incorporated with the quasi-static hysteresis model, where the weights of specifically designed neural network corresponds to the model parameters. To handle the multivalued problem of hysteresis, generalized input gradient is proposed to convert multivalued mapping of hysteresis into one-toone mapping. The parameters of the nonlinear rate dependent hysteresis in piezoelectric actuated stage is identified by neural network training, taking advantage of their universal function approximation capabilities. The proposed scheme is also compared with conventional black box and particle swarm optimization identification (PSO) based methods, where simulation and experimental results demonstrate significant performance improvement with the proposed method.

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Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

Cited by 11 publications

References 27 publications

Hysteresis inversion-free predictive compensation control for soft pneumatic actuators based on a global Koopman modeling strategy

Hysteresis inversion-free predictive compensation control for soft pneumatic actuators based on a global Koopman modeling strategy

Discrete-time rate-dependent hysteresis modeling and parameter identification of piezoelectric actuators

Modeling and Identification of Rate Dependent Hysteresis in Piezoelectric Actuated Nano-Stage: A Gray Box Neural Network Based Approach

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