scholarly journals Neural Network Self-Tuning Control for a Piezoelectric Actuator

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3342 ◽  
Author(s):  
Wenjun Li ◽  
Chen Zhang ◽  
Wei Gao ◽  
Miaolei Zhou
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Input Voltage ◽  
Rate Dependency ◽  
Output Displacement ◽  
Control Approach ◽  
Adaptive Parameter ◽  
Hysteresis Nonlinearity ◽  
Self Tuning ◽  
Control Methodology

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.

Model and Control on Hysteresis of Piezoelectric Actuator

2011 ◽  
Vol 179-180 ◽  
pp. 635-640
Author(s):  
Hua Wei Ji ◽  
Yong Qing Wen
Keyword(s):  
Piezoelectric Actuator ◽  
Vibration Suppression ◽  
Control Method ◽  
Input Voltage ◽  
Fast Response ◽  
System Response ◽  
Output Displacement ◽  
Proposed Model ◽  
Hysteresis Nonlinearity ◽  
And Control

Piezoelectric actuator is being widely used in vibration suppression and micro positioning applications for fast response, nanometer resolution, no backlash, no friction and bigger driving force. However, its inherent hysteresis characteristics between the input voltage and output displacement limit its control accuracy. An efficient way to eliminate this limitation is to model and control this hysteresis. In order solve the problem, the characteristic of piezoelectric actuator was introduced, and its static hysteresis was studied by experiment. A Preisach model was put forward to describe the hysteresis nonlinearity; a model feedforword controller was used to quicken system response. Control experiment results indicate that the proposed model and control method has good performance for precision control

Active Noise Hybrid Feedforward/Feedback Control Using Neural Network Compensation*

2001 ◽  
Vol 124 (1) ◽  
pp. 100-104 ◽  
Author(s):  
Zhang Qizhi ◽  
Jia Yongle
Keyword(s):  
Neural Network ◽  
Noise Control ◽  
Control Method ◽  
Active Noise Control ◽  
Equivalent Model ◽  
Gradient Descent Method ◽  
Nonlinear Simulation ◽  
Control Approach ◽  
Active Noise ◽  
Nonlinear Noise

The nonlinear active noise control (ANC) is studied. The nonlinear ANC system is approximated by an equivalent model composed of a simple linear sub-model plus a nonlinear sub-model. Feedforward neural networks are selected to approximate the nonlinear sub-model. An adaptive active nonlinear noise control approach using a neural network enhancement is derived, and a simplified neural network control approach is proposed. The feedforward compensation and output error feedback technology are utilized in the controller designing. The on-line learning algorithm based on the error gradient descent method is proposed, and local stability of closed loop system is proved based on the discrete Lyapunov function. A nonlinear simulation example shows that the adaptive active noise control method based on neural network compensation is very effective to the nonlinear noise control, and the convergence of the NNEH control is superior to that of the NN control.

Intelligent Motion Planning and Control for Robotic Joints Using Bio-Inspired Spiking Neural Networks

2019 ◽  
Vol 16 (04) ◽  
pp. 1950012 ◽  
Author(s):  
Mircea Hulea ◽  
Adrian Burlacu ◽  
Constantin-Florin Caruntu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Motion Planning ◽  
Control Method ◽  
Inhibitory Neurons ◽  
Robotic Arm ◽  
Control Approach ◽  
Planning And Control ◽  
Robotic Joints ◽  
And Control

This paper details an intelligent motion planning and control approach for a one-degree of freedom joint of a robotic arm that can be used to implement anthropomorphic robotic hands. This intelligent control method is based on bio-inspired electronic neural networks and contractile artificial muscles implemented with shape memory alloy (SMA) actuators. The spiking neural network (SNN) includes several excitatory neurons that naturally determine the contraction force of the actuators, and unevenly distributed inhibitory neurons that regulate the excitatory activity. To validate the proposed concept, the experiments highlight the motion planning and control of a single-joint robotic arm. The results show that the electronic neural network is able to intelligently activate motion and hold with high precision the mobile link to the target positions even if the arm is slightly loaded. These results are encouraging for the development of improved biologically plausible neural structures that are able to control simultaneously multiple muscles.

Theory and Experiments on the Compliance Control of Redundant Robot Manipulators

1990 ◽  
Vol 112 (4) ◽  
pp. 653-660 ◽  
Author(s):  
H. Kazerooni ◽  
K. G. Bouklas ◽  
J. Guo
Keyword(s):  
Degrees Of Freedom ◽  
Control Method ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Redundant Robot ◽  
Control Approach ◽  
Six Degrees Of Freedom ◽  
The Stability ◽  
Control Methodology ◽  
Theory And Experiments

This work presents a control methodology for compliant motion in redundant robot manipulators. This control approach takes advantage of the redundancy in the robot’s degrees of freedom: while a maximum six degrees of freedom of the robot control the robot’s endpoint position, the remaining degrees of freedom impose an appropriate force on the environment. To verify the applicability of this control method, an active end-effector is mounted on an industrial robot to generate redundancy in the degrees of freedom. A set of experiments are described to demonstrate the use of this control method in constrained maneuvers. The stability of the robot and the environment is analyzed.

Adaptive Integral Base Sliding Mode Tracking Control of Piezoelectric Actuators

2009 ◽  
Author(s):  
Mohammad Sheikh Sofla ◽  
Seyed Mehdi Rezaei ◽  
Mohammad Zareinejad
Keyword(s):  
Sliding Mode ◽  
Piezoelectric Actuators ◽  
Uncertain System ◽  
Hysteretic Behavior ◽  
Control Approach ◽  
Practical Applications ◽  
Hysteresis Nonlinearity ◽  
Control Scheme ◽  
Integral Sliding Surface ◽  
Control Methodology

This paper presents an adaptive integral sliding mode control scheme for precise trajectory tracking of piezoelectric actuators (PEAs). This control methodology is proposed considering the problems of unknown or uncertain system parameters, hysteresis nonlinearity, and external load disturbances. The hysteretic behavior is represented by Bouc–Wen hysteresis model. It is shown that the nonlinear response of the model due to the hysteresis effect, acts as a bounded disturbance. Then base on this fact an adaptive robust controller is proposed, where an integral sliding surface is utilized to achieve the desired tracking performance. By using the proposed control approach the asymptotical stability in displacement tracking and robustness to the dynamic load disturbance can be provided. Finally, Experimental results are illustrated to verify the efficiency of the proposed method for tracking in various range of frequency and load which are common in practical applications.

Feedback Control of a Flexible Micro-Displacement Manipulator

2011 ◽  
Vol 480-481 ◽  
pp. 1167-1172
Author(s):  
Hua Wei Ji ◽  
Yong Qing Wen ◽  
Chen Ming Fu
Keyword(s):  
Mathematical Method ◽  
Closed Loop ◽  
Input Voltage ◽  
Flexure Hinge ◽  
Output Displacement ◽  
Control Precision ◽  
Hysteresis Nonlinearity ◽  
Vibration Experiment ◽  
The Relationship ◽  
Control Accuracy

Micro-displacement manipulator consists of piezoelectric actuator and flexure hinge is being widely used in precision positioning technology for its high resolution of displacement, high stiffness and fast frequency response. However, the hysteresis nonlinearity of actuator and vibration limited its control accuracy. In order to improve the positioning precision, the relationship between input voltage and output displacement was studied, the hysteresis nonlinearity was described by mathematical method, and a closed-loop controller was proposed to control the hysteresis and vibration. Experiment results revealed the proposed closed-loop controller can enhance the control precision of micro-displacement manipulator.

scholarly journals Neural Network Control for the Probe Landing Based on Proportional Integral Observer

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yuanchun Li ◽  
Tianhao Ma ◽  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Landing Site ◽  
Network Control ◽  
Control Law ◽  
Neural Network Control ◽  
Control Approach ◽  
Proportional Integral ◽  
The Neural Network

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

Concise leader-follower formation control of underactuated unmanned surface vehicle with output error constraints

2021 ◽  
pp. 014233122110471
Author(s):  
Meijiao Zhao ◽  
Yan Peng ◽  
Yueying Wang ◽  
Dan Zhang ◽  
Jun Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Formation Control ◽  
Control Method ◽  
Dynamic Surface Control ◽  
Small Range ◽  
Dynamic Surface ◽  
Unmanned Surface Vehicle ◽  
Control Approach ◽  
Output Error ◽  
Ocean Environment

In this paper, a concise leader-follower formation control approach is presented for a group of underactuated unmanned surface vehicle with dynamic system uncertainties and external environment disturbances, where the output errors are required to be within constraints. To settle the output error constraints, a standard barrier Lyapunov function (BLF) is incorporated into the backstepping control method. Furthermore, the “differential explosion” problem of virtual control laws is avoided by introducing the dynamic surface control. To estimate the unknown dynamic terms, an adaptive neural network is designed and a nonlinear disturbance observer is adopted to compensate for the approximation errors of neural network and ocean environment disturbances. Under the constraint of output error, the presented controller based on standard BLF has simpler structure and better control performance than depended on tan-type BLF. The presented controller can ensure that the formation errors converge to a small range around zero, while the output error constraint requirements are met. All signals in the closed-loop system are bounded, and the numerical simulation further shows the effectiveness of the presented control scheme.

Experimental Study on Hysteresis Nonlinearity of Piezoelectric Actuator

2010 ◽  
Vol 44-47 ◽  
pp. 2968-2972
Author(s):  
Hua Wei Ji ◽  
Yong Qing Wen
Keyword(s):  
Piezoelectric Actuator ◽  
Vibration Suppression ◽  
Nonlinear Models ◽  
Nonlinear Behavior ◽  
Input Voltage ◽  
Fast Response ◽  
Output Displacement ◽  
Input Method ◽  
Hysteresis Nonlinearity ◽  
Output Characteristics

In recent years, piezoelectric actuator is being widely used in vibration suppression and micro positioning applications for its fast response, nanometer resolution, no backlash, no friction and bigger driving force. However, its inherent hysteresis nonlinear characteristics between the input voltage and output displacement limit its control accuracy. To optimize the performance of piezoelectric actuator, it is essential to understand the hysteresis nonlinear behavior. In this work, the hysteresis nonlinear behavior was studied by experiment; the dependence of output characteristics on voltage under different electric field conditions in piezoelectric actuator was discussed. It was found that the input method and frequency of loading voltage has a great effect on the hysteresis nonlinearity of piezoelectric actuator. At last, some different hysteresis nonlinear models were introduced.

Suppression of nonlinear aeroelastic vibrations by learned neural network controller

2019 ◽  
Vol 91 (3) ◽  
pp. 420-427
Author(s):  
Franciszek Dul
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Robust Methods ◽  
Content Type ◽  
Strongly Nonlinear ◽  
Neural Network Controller ◽  
Network Controller ◽  
Hysteresis Nonlinearity ◽  
Highly Nonlinear ◽  
Aeroelastic Vibrations

PurposeThe purpose of the paper is to analyze the active suppression of the aeroelastic vibrations of ailerons with strongly nonlinear characteristics by neural network/reinforcement learning (NN/RL) control method and comparing it with the classic robust methods of suppression.Design/methodology/approachThe flexible wing and aileron with hysteresis nonlinearity is treated as a plant-controller system and NN/RL and robust controller are used to suppress the nonlinear aeroelastic vibrations of aileron. The simulation approach is used for analyzing the efficiency of both types of methods in suppressing of such vibrations.FindingsThe analysis shows that the NN/RL controller is able to suppress the nonlinear vibrations of aileron much better than linear robust method, although its efficiency depends essentially on the NN topology as well as on the RL strategy.Research limitations/implicationsOnly numerical analysis was carried out; thus, the proposed solution is of theoretical value, and its application to the real suppression of aeroelastic vibrations requires further research.Practical implicationsThe work shows the NN/RL method has a great potential in improving suppression of highly nonlinear aeroelastic vibrations, opposed to the classical robust methods that probably reach their limits in this area.Originality/valueThe work raises the questions of controllability of the highly nonlinear aeroelastic systems by means of classical robust and NN/RL methods of control.

Sign in / Sign up

Export Citation Format

Share Document