Approximate dynamic programming-based approaches for input–output data-driven control of nonlinear processes

Automatica ◽  
2005 ◽  
Vol 41 (7) ◽  
pp. 1281-1288 ◽  
Author(s):  
Jong Min Lee ◽  
Jay H. Lee
Keyword(s):  
Dynamic Programming ◽  
Approximate Dynamic Programming ◽  
Data Driven ◽  
Input Output ◽  
Nonlinear Processes ◽  
Output Data

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.

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