A Nonlinear Self-tuning Control Method Based on Neural Wiener Model

2018 ◽  
Author(s):  
Bi Zhang ◽  
Xin-Gang Zhao ◽  
Zhuang Xu ◽  
Ming Zhao
Keyword(s):  
Control Method ◽  
Wiener Model ◽  
Self Tuning

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.

Performance Enhancement of Pneumatic Vibration Isolators Using Self-Tuning PID Feedback and Time-Delay-Control (TDC)-Based Feedforward Scheme

2018 ◽  
Author(s):  
Jin-Wei Liang ◽  
Hung-Yi Chen ◽  
Lyu-Cyuan Zeng
Keyword(s):  
Mathematical Model ◽  
Performance Enhancement ◽  
Control Method ◽  
Hybrid Control ◽  
Model Performance ◽  
Experimental Investigations ◽  
Isolation System ◽  
Control Scheme ◽  
Self Tuning ◽  
Hybrid Control Scheme

A hybrid control scheme that combines a self-tuning PID-feedback loop and TDC-based feedforward scheme is proposed in this study to cope with an active pneumatic vibration isolator. In order to establish an effective TDC feedforward control a reliable mathematical model of the pneumatic isolator is required and developed firstly. Numerical and experimental investigations on the validity of the mathematical model are performed. It is found that although slight discrepancy exists between predicted and observed behaviors of the system, the overall model performance is acceptable. The resultant model is then applied in the design of the TDC feedforward scheme. A neuro-based adaptive PID control is integrated with the TDC feedforward algorithm to form the hybrid control. Numerical and experimental isolation tests are carried out to examine the suppression performances of the proposed hybrid control scheme. The results show that the proposed hybrid control method outperforms solely TDC feedforward while the latter outperforms the passive isolation system. Moreover, the proposed hybrid control scheme can suppress the vibration near the system’s resonance.

Smith Prediction Based on Genetic Algorithm Self-Tuning PID Control

2014 ◽  
Vol 602-605 ◽  
pp. 1186-1189
Author(s):  
Dong Sheng Wu ◽  
Qing Yang
Keyword(s):  
Genetic Algorithm ◽  
Time Delay ◽  
Pid Control ◽  
Pid Controller ◽  
Control Method ◽  
Delay System ◽  
Time Delay System ◽  
Self Tuning ◽  
On Line ◽  
Turning Control

Aiming at the phenomena of big time delay are normally existing in industry control, this paper proposes an intelligent GA-Smith-PID control method based on genetic algorithm and Smith predictive compensation algorithm and traditional PID controller. This method uses the ability of on line-study, a self-turning control strategy of GA, and better control of Smith predictive compensation to deal with the big time delay. This method overcomes the limitation of traditional PID control effectively, and improves the system’s robustness and self-adaptability, and gets satisfactory control to deal with the big time delay system.

Joint Self-Tuning With Cartesian Setpoints

1986 ◽  
Vol 108 (2) ◽  
pp. 146-150 ◽  
Author(s):  
P. G. Backes ◽  
G. G. Leininger ◽  
Chun-Hsien Chung
Keyword(s):  
Real Time ◽  
Control Method ◽  
Hybrid Control ◽  
Pole Placement ◽  
Real Time Control ◽  
Time Control ◽  
Self Tuning ◽  
Cause Of Error ◽  
Time Position ◽  
Manipulator Control

A joint coordinate self-tuning manipulator control method is presented which uses Cartesian setpoints. The method is capable of both position and hybrid control. Position and force errors are transformed from Cartesian coordinates to position and force errors at the joints. The position and force errors at each joint are combined into one hybrid error that is eliminated using pole-placement self-tuning. Real time position and hybrid control results are given. No prior knowledge of manipulator or load dynamics is required and real time control results show that the goal of consistent control with changing load dynamics is achieved. The major cause of error in position and hybrid control is the large friction effects in the joints.

Application of Q Learning-Based Self-Tuning PID with DRNN in the Strip Flatness and Gauge System

2014 ◽  
Vol 494-495 ◽  
pp. 1377-1380
Author(s):  
Yu Lian Jiang ◽  
Jian Chang Liu ◽  
Shu Bin Tan
Keyword(s):  
Control Method ◽  
Output Control ◽  
Control Laws ◽  
Q Learning ◽  
Learning Agent ◽  
Automatic Gauge Control ◽  
Agent Simulation ◽  
Flatness Control ◽  
Self Tuning ◽  
Multi Agent

In view of the process of automatic flatness control and automatic gauge control that is a nonlinear system with multi-dimensions, multi-variables, strong coupling and time variation, a novel control method called self-tuning PID with diagonal recurrent neural network (DRNN-PID) based on Q learning is proposed. It is able to coordinate the coupling of flatness control and gauge control agents to get the satisfactory control requirements without decoupling directly and amend output control laws by DRNN-PID adaptively. Decomposition-coordination is utilized to establish a novel multi-agent system for coordination control including flatness agent, gauge agent and Q learning agent. Simulation result demonstrates the validity of our proposed method.

scholarly journals Iterative Self-Tuning Minimum Variance Control of a Nonlinear Autonomous Underwater Vehicle Maneuvering Model

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2686
Author(s):  
Maria Tomas-Rodríguez ◽  
Elías Revestido Herrero ◽  
Francisco J. Velasco
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Control Method ◽  
Linear Time ◽  
Control Design ◽  
Underwater Vehicle ◽  
Minimum Variance ◽  
Use Of Self ◽  
Self Tuning ◽  
Heading Angle ◽  
Iteration Technique

This paper addresses the problem of control design for a nonlinear maneuvering model of an autonomous underwater vehicle. The control algorithm is based on an iteration technique that approximates the original nonlinear model by a sequence of linear time-varying equations equivalent to the original nonlinear problem and a self-tuning control method so that the controller is designed at each time point on the interval for trajectory tracking and heading angle control. This work makes use of self-tuning minimum variance principles. The benefit of this approach is that the nonlinearities and couplings of the system are preserved, unlike in the cases of control design based on linearized systems, reducing in this manner the uncertainty in the model and increasing the robustness of the controller. The simulations here presented use a torpedo-shaped underwater vehicle model and show the good performance of the controller and accurate tracking for certain maneuvering cases.

scholarly journals Neuro-PID Control for Electric Vehicle

2011 ◽  
Vol 15 (7) ◽  
pp. 846-853 ◽  
Author(s):  
Shigeru Omatu ◽  
◽  
Michifumi Yoshioka ◽  
Toru Fujinaka ◽  
◽  
...  
Keyword(s):  
Electric Vehicle ◽  
Pid Control ◽  
Control Method ◽  
Torque Control ◽  
Learning Ability ◽  
Control Problems ◽  
Process Industries ◽  
The Neural Network ◽  
Control Scheme ◽  
Self Tuning

In this paper we consider the neuro-control method and its application to control problems of an electric vehicle. The neuro-control methods adopted here is based on Proportional-plus-Integral-plus-Derivative (PID) control, which has been adopted to solve process control or intelligent control problems. In Japan about eighty four percent of the process industries have used the PID control. After deriving the self-tuning PID control scheme (neuro-PID) using the learning ability of the neural network, we will show the control results by using the speed and torque control of an electric vehicle.

Sign in / Sign up

Export Citation Format

Share Document