Chaotic motion control of a vibro-impact system based on data-driven control method

2021 ◽  
pp. 107754632110433
Author(s):  
Xiao-juan Wei ◽  
Ning-zhou Li ◽  
Wang-cai Ding
Keyword(s):  
Motion Control ◽  
Data Model ◽  
Chaotic Motion ◽  
Control Method ◽  
Data Driven ◽  
Damping Coefficient ◽  
Input Output ◽  
Output Data ◽  
Controlled System ◽  
Impact System

For the chaotic motion control of a vibro-impact system with clearance, the parameter feedback chaos control strategy based on the data-driven control method is presented in this article. The pseudo-partial-derivative is estimated on-line by using the input/output data of the controlled system so that the compact form dynamic linearization (CFDL) data model of the controlled system can be established. And then, the chaos controller is designed based on the CFDL data model of the controlled system. And the distance between two adjacent points on the Poincaré section is used as the judgment basis to guide the controller to output a small perturbation to adjust the damping coefficient of the controlled system, so the chaotic motion can be controlled to a periodic motion by dynamically and slightly adjusting the damping coefficient of the controlled system. In this method, the design of the controller is independent of the order of the controlled system and the structure of the mathematical model. Only the input/output data of the controlled system can be used to complete the design of the controller. In the simulation experiment, the effectiveness and feasibility of the proposed control method in this article are verified by simulation results.

Data driven controller based on fuzzy rule adaptive network: with experimental validation

2019 ◽  
Vol 38 (6) ◽  
pp. 1782-1799
Author(s):  
Chidentree Treesatayapun
Keyword(s):  
Characteristic Curve ◽  
Tracking Error ◽  
Design Procedure ◽  
Output Characteristic ◽  
Data Driven ◽  
Superior Performance ◽  
Input Output ◽  
Output Data ◽  
Content Type ◽  
Experimental Systems

Purpose The purpose of this paper is to design an online-data driven adaptive control scheme based on fuzzy rules emulated network (FREN) for a class of unknown nonlinear discrete-time systems. Design/methodology/approach By using the input-output characteristic curve of controlled plant and the set of IF-THEN rules based on human knowledge inspiration, the adaptive controller is established by an adaptive FREN. The learning algorithm is established with convergence proof of the closed-loop system and controller’s parameters are directly designed by experimental data. Findings The convergence of tracking error is verified by the theoretical results and the experimental systems. The experimental systems and comparison results show that the proposed controller and its design procedure based on input-output data can achieve superior performance. Practical implications The theoretical aspect and experimental systems with the light-emitting diode (LED) current control and the robotic system prove that the proposed controller can be designed by using only input-output data of the controlled plants when the tracking error can be affirmed the convergence. Originality/value The proposed controller has been theoretically developed and used through experimental systems by using only input-output data of the controlled plant. The novel design procedure has been proposed by using the input-output characteristic curve for both positive and negative control directions.

scholarly journals A Data-Driven Control Design Approach for Freeway Traffic Ramp Metering with Virtual Reference Feedback Tuning

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Shangtai Jin ◽  
Zhongsheng Hou ◽  
Ronghu Chi ◽  
Jiangen Hao
Keyword(s):  
Design Methodology ◽  
Control Design ◽  
Ramp Metering ◽  
Data Driven ◽  
Output Data ◽  
Virtual Reference ◽  
Controlled System ◽  
Freeway Traffic ◽  
Traffic System ◽  
Input And Output

ALINEA is a simple, efficient, and easily implemented ramp metering strategy. Virtual reference feedback tuning (VRFT) is most suitable for many practical systems since it is a “one-shot” data-driven control design methodology. This paper presents an application of VRFT to a ramp metering problem of freeway traffic system. When there is not enough prior knowledge of the controlled system to select a proper parameter of ALINEA, the VRFT approach is used to optimize the ALINEA's parameter by only using a batch of input and output data collected from the freeway traffic system. The extensive simulations are built on both the macroscopic MATLAB platform and the microscopic PARAMICS platform to show the effectiveness and applicability of the proposed data-driven controller tuning approach.

scholarly journals Tracking and Synchronization with Inversion-Based ILC for a Multi-Actuator-Driven Wafer Inspection Cartridge Transport Robot System

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2904
Author(s):  
Dong Jun Oh ◽  
Seung Guk Baek ◽  
Kyung-Tae Nam ◽  
Ja Choon Koo
Keyword(s):  
Iterative Learning Control ◽  
Control Method ◽  
Iterative Learning ◽  
Synchronization Control ◽  
System Control ◽  
Input Output ◽  
Semiconductor Wafer ◽  
Robot System ◽  
Output Data ◽  
Wafer Inspection

This paper proposes a simple tracking and synchronization control of a dual-drive system using inversion-based iterative learning control (IILC), which reformulates the model at each iteration based on input/output data. By the power of the IILC, this work simplifies the dual-actuator-driven dynamic system control problem that is normally addressed with a MIMO method. This work also shows the potential of the IILC for nonlinear system applications by reformulating the model at each iteration based on the input/output data. An analytical representation of the iteration-varying IILC followed by simulations is provided. A set of physical system testings with a dual-motor gantry and a semiconductor wafer inspection robotic system are carried out to verify the control method.

scholarly journals A New Model-Free Predictive Control Method Using Input and Output Data

2014 ◽  
Vol 1042 ◽  
pp. 182-187 ◽  
Author(s):  
Shigeru Yamamoto
Keyword(s):  
Mathematical Model ◽  
Predictive Control ◽  
Control Method ◽  
Just In Time ◽  
Control Input ◽  
Input Output ◽  
Output Data ◽  
Online Data ◽  
Model Free ◽  
The Mathematical Model

The purpose of this paper is to present a new predictive control utilizing online data and stored data of input/output of the controlled system. The conventional predictive control methods utilize the mathematical model of the control system to predict an optimal future input to control the system. The model is usually obtained by a standard system identification method from the measured input/output data. The proposed method does not require the mathematical model to predict the optimal future control input to achieve the desired output. This control strategy, called just-in-time, was originally proposed by Inoue and Yamamoto in 2004. In this paper, we proposed a simplified version of the original just-in-time predictive control method.

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