A fuzzy model predictive controller for stick‐slip type piezoelectric actuators

Cheng, Long; Wang, Ang; Qin, S. Joe

doi:10.1002/oca.2800

Cited by 4 publications

(4 citation statements)

References 43 publications

(66 reference statements)

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“…This method avoids the control based on a complex fixed model and is a successful application of the intelligent control method in PSSAs. In addition to predictive control based on the neural network model, Cheng L et al [ 15 ] proposed a predictive control methodology based on a fuzzy model. The controller aimed to establish the Takagi–Sugeno fuzzy model for the whole PSSA and design a predictive controller based on the model.…”

Section: Introductionmentioning

confidence: 99%

Model-Free Adaptive Positioning Control of the Bidirectional Stick–Slip Piezoelectric Actuator with Coupled Asymmetric Flexure-Hinge Mechanisms

Zhang,

Dong,

et al. 2023

Sensors

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A model-free adaptive positioning control strategy for piezoelectric stick–slip actuators (PSSAs) with uncertain disturbance is proposed. The designed controller consists of a data-driven self-learning feedforward controller and a model-free adaptive feedback controller with a radial basis function neural network (RBFNN)-based observer. Unlike the traditional model-based control methods, the model-free adaptive control (MFAC) strategy avoids the complicated modeling process. First, the nonlinear system of the PSSA is dynamically linearized into a data model. Then, the model-free adaptive feedback controller based on a data model is designed to avoid the complicated modeling process and enhance the robustness of the control system. Simultaneously, the data-driven self-learning feedforward controller is improved to realize the high-precision control performance. Additionally, the convergence of the tracking error and the boundedness of the control output signal are proved. Finally, the experimentally obtained results illustrate the advantages and effectiveness of the developed control methodology on the bidirectional stick–slip piezoelectric actuator with coupled asymmetric flexure-hinge mechanisms. The positioning error through the proposed controller reaches 30 nm under the low-frequency condition and 200 nm under the high-frequency condition when the target position is set to 100 μm. In addition, the target position can be accurately tracked in less than 0.5 s in the presence of a 100 Hz frequency.

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Section: Introductionmentioning

confidence: 99%

Model-Free Adaptive Positioning Control of the Bidirectional Stick–Slip Piezoelectric Actuator with Coupled Asymmetric Flexure-Hinge Mechanisms

Zhang,

Dong,

et al. 2023

Sensors

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“…The experimental results of piezoelectric nano-positioning show that the model can achieve high precision and high bandwidth trajectory tracking. Cheng et al [27] established an adaptive Takagi-Sugeno fuzzy model for the input-output relationship of stick-slip type piezoelectric actuators and realized the accurate control of the end effector. Unfortunately, the stability analysis of the system is based on several specific assumptions, but most of the actual systems can not meet these assumptions.…”

Section: Introductionmentioning

confidence: 99%

Compound Control of Trajectory Errors in a Non-Resonant Piezo-Actuated Elliptical Vibration Cutting Device

Zhang,

Shu,

Yuan

et al. 2023

Micromachines

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To improve the machining quality of the non-resonant elliptical vibration cutting (EVC) device, a compound control method for trajectory error compensation is proposed in this paper. Firstly, by analyzing the working principle of non-resonant EVC device and considering the elliptical trajectory error caused by piezoelectric hysteresis, a dynamic PI (Prandtl-Ishlinskii) model relating to voltage change rate and acceleration was established to describe the piezoelectric hysteresis characteristics of EVC devices. Then, the parameters of the dynamic PI model were identified by using the particle swarm optimization (PSO) algorithm. Secondly, based on the dynamic PI model, a compound control method has been proposed in which the inverse dynamic PI model is used as the feedforward controller for the dynamic hysteresis compensation, while PID (proportion integration differentiation) feedback is used to improve the control accuracy. Finally, trajectory-tracking experiments have been conducted to verify the feasibility of the proposed compound control method.

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“…The first group of papers 1‐7 focuses on data‐based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick‐slip type piezoelectric actuator to realize the precise control of the end effector 1 . A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied 2 .…”

mentioning

confidence: 99%

“…The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector.…”

mentioning

confidence: 99%

Data‐based learning control for optimization of nonlinear systems

Wei

Song

Zhang

et al. 2023

Optim Control Appl Methods

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With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector. 1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. 2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints. 5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly. 6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference. 7 The second group of papers 8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. 8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks. 9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries. 10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances. 11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third...

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A fuzzy model predictive controller for stick‐slip type piezoelectric actuators

Cited by 4 publications

References 43 publications

Model-Free Adaptive Positioning Control of the Bidirectional Stick–Slip Piezoelectric Actuator with Coupled Asymmetric Flexure-Hinge Mechanisms

Model-Free Adaptive Positioning Control of the Bidirectional Stick–Slip Piezoelectric Actuator with Coupled Asymmetric Flexure-Hinge Mechanisms

Compound Control of Trajectory Errors in a Non-Resonant Piezo-Actuated Elliptical Vibration Cutting Device

Data‐based learning control for optimization of nonlinear systems

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