Nonlinear Piezo-Actuator Control by Learning Self-Tuning Regulator

1993 ◽  
Vol 115 (4) ◽  
pp. 720-723 ◽  
Author(s):  
C. James Li ◽  
Homayoon S. M. Beigi ◽  
Shengyi Li ◽  
Jiancheng Liang
Keyword(s):  
Parameter Estimation ◽  
Tracking Error ◽  
Diamond Turning ◽  
Piezo Actuator ◽  
Positioning System ◽  
Tool Positioning ◽  
Repetitive Tasks ◽  
Self Tuning ◽  
Learning Parameter ◽  
Actuator Control

This paper presents a learning self-tuning (LSTR) regulator which improves the tracking performance of itself while performing repetitive tasks. The controller is a self-tuning regulator based on learning parameter estimation. Experimentally, the controller was used to control the movement of a nonlinear piezoelectric actuator which is a part of the tool positioning system for a diamond turning lathe. Experimental results show that the controller is able to reduce the tracking error through the repetition of the task.

Piezo Actuator Hybrid Modeling for Nonlinear Inverse Control in Diamond Turning

1999 ◽  
Author(s):  
Dongwoo Song ◽  
C. James Li
Keyword(s):  
Pid Control ◽  
Inverse Dynamics ◽  
Feedforward Control ◽  
Diamond Turning ◽  
Piezo Actuator ◽  
Positioning System ◽  
Preisach Model ◽  
Hysteresis Model ◽  
Inverse Control ◽  
Spring Mechanism

Abstract This paper describes a micro-positioning system based on piezo actuators and a spring mechanism, and a hybrid hysteresis model integrating a neural network and Preisach model to identify the inverse dynamics of the micro-positioning system. To improve the workpiece form accuracy in diamond turning, feedforward control using the hybrid inverse model and feedback PID control were applied individually and in combination. The performance of these controllers are compared in actual cutting tests.

Positioning System with Progressive Wave-Type Ultrasonic Motor under Self-Tuning Fuzzy Control

1995 ◽  
Vol 7 (1) ◽  
pp. 63-68 ◽  
Author(s):  
Junji Fukumi ◽  
◽  
Takuya Kamano ◽  
Takayuki Suzuki ◽  
Yu Kataoka ◽  
...  
Keyword(s):  
Feedback Loop ◽  
Fuzzy Controller ◽  
Lower Layer ◽  
Tracking Error ◽  
Ultrasonic Motor ◽  
The Self ◽  
Positioning System ◽  
Wave Type ◽  
Nonlinear Characteristics ◽  
Self Tuning

This paper considers the use of a self-tuning fuzzy controller for a positioning system with a progressive wavetype ultrasonic motor. The system consists of a feedback loop with a conventional controller and a self tuning fuzzy controller. The objective of the self tuning fuzzy controller is to restrain the adverse effect of nonlinear characteristics of the motor and to improve the tracking performance. The self-tuning fuzzy controller is functionally divided into two layers. The fuzzy rules are automatically adjusted by a tuning algorithm so that the tracking error is minimized in the upper layer. In lower layer, the output signal of the self tuning fuzzy controller is obtained by fuzzy reasoning procedure. After the tuning process is completed, the tracking error almost converges to zero, and the ultrasonic motor is no longer controlled by the fixed gain feedback controller but by the self-tuning fuzzy controller. The effectiveness of the proposed self-tuning fuzzy controller is demonstrated by an experiment.

scholarly journals A New Fuzzy Iterative Learning Control Algorithm for Single Joint Manipulator

2016 ◽  
Vol 26 (3) ◽  
pp. 297-310 ◽  
Author(s):  
Meng Wang ◽  
Guangrong Bian ◽  
Hongsheng Li
Keyword(s):  
Fuzzy Control ◽  
Iterative Learning Control ◽  
Control Algorithm ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
Repetitive Tasks ◽  
Initial Control ◽  
Self Tuning ◽  
Tracking Trajectory

Abstract This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times’ iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

Iterative learning parameter estimation and design of SMB processes

2010 ◽  
Vol 161 (1-2) ◽  
pp. 223-233 ◽  
Author(s):  
Junghui Chen ◽  
Minghui Chen ◽  
Lester Lik Teck Chan
Keyword(s):  
Parameter Estimation ◽  
Iterative Learning ◽  
Learning Parameter

DESIGN AND ANALYSIS OF SUPER TWISTING SLIDING MODE CONTROL FOR MACHINE TOOLS

2016 ◽  
Vol 78 (10-3) ◽  
Author(s):  
Chiew Tsung Heng ◽  
Zamberi Jamaludin ◽  
Ahmad Yusairi Bani Hashim ◽  
Nur Aidawaty Rafan ◽  
Lokman Abdullah ◽  
...  
Keyword(s):  
Machine Tools ◽  
Sliding Mode ◽  
Tracking Error ◽  
Control Algorithms ◽  
Sliding Mode Controller ◽  
Control Input ◽  
Positioning System ◽  
Direct Drive ◽  
Chattering Effect

High demands of precision on machine tools are hardly cope by using existing classic control algorithms. This paper focuses on the design, analysis and validation of a super twisting sliding mode controller on a single axis direct drive positioning system for improved tracking performances. The second order positioning system parameters were determined using input and output of measured data. Effects of two gain parameters in control algorithm on the quality of the control input and tracking error were analysed experimentally. The gain parameters were selected based on magnitude reduction in chattering during practical application. The performance of tuned super twisting sliding mode controller was compared with a traditional sliding mode controller using sigmoid-like function. Results showed that super twisting sliding mode controller reduced the chattering effect and improved the performance of system in terms of tracking error by 16.5%.  

Adaptive robust iterative learning control with application to a Delta robot

2020 ◽  
pp. 095965182093853
Author(s):  
Chems Eddine Boudjedir ◽  
Djamel Boukhetala
Keyword(s):  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Iterative Learning ◽  
External Disturbances ◽  
Repetitive Tasks ◽  
Control Scheme ◽  
Derivative Type ◽  
Delta Robot ◽  
Proportional Derivative Controller

In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

Compensation for the Residual Error of the Voltage Drive of the Charge Control of a Piezoelectric Actuator

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Shih-Tang Liu ◽  
Jia-Yush Yen ◽  
Fu-Cheng Wang
Keyword(s):  
Piezoelectric Actuator ◽  
Voltage Drop ◽  
Tracking Error ◽  
Nonlinear Behavior ◽  
Piezo Actuator ◽  
Charge Control ◽  
In Series ◽  
Series Of Experiments ◽  
The One ◽  
Positional Control

One very effective approach to suppress hysteresis from the piezoelectric actuator is to use the charge control across the associated capacitance. The charge driver often uses an additional capacitor connected to the piezo-actuator in series for the charge sense feedback control. When this charge sense is used with a voltage drive for the charge control, the applied voltage will include two parts. The one is the voltage drop across the useful electro-mechanical part and effectively converted to the driving force, whereas the other part indicates the equivalent voltage drop due to the hysteresis. In our research, we noticed that it is possible to use a simple estimator as the hysteresis voltage observer and use it to precompensate for the voltage drop. Comparing to the conventional hysteresis suppression achieved by the closed-loop positional control, we show significant improvement of the control performance. For dynamic applications, we also proposed a combination of the Preisach model with the hysteresis estimator to better suppress the nonlinear behavior. A series of experiments were conducted to demonstrate the performance improvement of the proposed compensator. A 10 nm and 25 nm maximum tracking error can be maintained while tracking a staircase reference and a 30 Hz sinusoidal signal, respectively.

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