Model-free control of unknown nonlinear systems using an iterative learning concept: theoretical development and experimental validation

2018 ◽  
Vol 94 (2) ◽  
pp. 1151-1163 ◽  
Author(s):  
Elmira Madadi ◽  
Dirk Söffker
Keyword(s):  
Nonlinear Systems ◽  
Experimental Validation ◽  
Iterative Learning ◽  
Theoretical Development ◽  
Model Free ◽  
Model Free Control ◽  
Learning Concept

Model-Free Approaches Applied to the Control of Nonlinear Systems: A Brief Survey With Special Attention to Intelligent PID Iterative Learning Control

2015 ◽  
Author(s):  
Elmira Madadi ◽  
Dirk Söffker
Keyword(s):  
Nonlinear Systems ◽  
Iterative Learning Control ◽  
Iterative Learning ◽  
Model Free ◽  
Different Types ◽  
Detailed Simulation ◽  
Intelligent Pid ◽  
Model Free Control ◽  
Simulation Results ◽  
And Performance

Model-based control is one of the popular solutions for designing a controller used to control nonlinear systems. However, the difficulty of obtaining an accurate model is a challenge for control designers. For this reason model-free control (MFC) methods are attractive. This contribution gives an overview on different types of model-free control. It includes an investigation about model-free techniques applied to nonlinear systems. In detail the iPID iterative learning-based method is expressed more detailed. Simulation results also illustrate a successful application and performance of the proposed method.

Experimental validation of robot-assisted cardiovascular catheterization: model-based versus model-free control

2018 ◽  
Vol 13 (6) ◽  
pp. 797-804
Author(s):  
Xiaomei Wang ◽  
Kit-Hang Lee ◽  
Denny K. C. Fu ◽  
Ziyang Dong ◽  
Kui Wang ◽  
...  
Keyword(s):  
Experimental Validation ◽  
Robot Assisted ◽  
Model Based ◽  
Model Free ◽  
Versus Model ◽  
Model Free Control

Towards an Adaptive Control Concept for Unknown Nonlinear MIMO Systems

2018 ◽  
Author(s):  
Elmira Madadi ◽  
Dirk Söffker
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Control Method ◽  
Mimo Systems ◽  
Tracking Error ◽  
Underlying Structure ◽  
Unknown Parameters ◽  
Modified Model ◽  
Model Free ◽  
Model Free Control

The design of an accurate model often appears as the most challenging tasks for control engineers especially focusing to the control of nonlinear systems with unknown parameters or effects to be identified in parallel. For this reason, development of model-free control methods is of increasing importance. The class of model-free control approaches is defined by the non-use of any knowledge about the underlying structure and/or related parameters of the dynamical system. Therefore the major criteria to evaluate model-free control performance are aspects regarding robustness against unknown inputs and disturbances to achieve a suitable tracking performance including ensuring stability. Consequently it is assumed that the system plant model to be controlled is unknown, only the inputs and outputs are used as measurements. In this contribution a modified model-free adaptive approach is given as the extended version of existing model-free adaptive control to improve the performance according to the tracking error at each sample time. Using modified model-free adaptive controller, the control goal can be achieved efficiently without an individual control design process for different kinds unknown nonlinear systems. The main contribution of this paper is to extend the modified model-free adaptive control method to unknown nonlinear multi-input multi-output (MIMO) systems. A numerical example is shown to demonstrate the successful application and performance of this method.

Sign in / Sign up

Export Citation Format

Share Document