Comparison of Different Model-Free Control Methods Concerning Real-Time Benchmark

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Elmira Madadi ◽  
Yao Dong ◽  
Dirk Söffker
Keyword(s):  
Detailed Comparison ◽  
Underlying Structure ◽  
Control Methods ◽  
Unknown Parameters ◽  
Model Free ◽  
Challenging Tasks ◽  
Tank System ◽  
Model Free Control ◽  
Partial Form ◽  
Performance Behavior

The design of accurate model often appears as the most challenging tasks for control engineers especially focusing to the control of nonlinear system 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 nonuse 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 and related achievable tracking performance. In this contribution, a detailed comparison of three different model-free control methods (intelligent proportional-integral-derivative (iPID) using second-order sliding differentiator and two variations of model-free adaptive control (using modified compact form dynamic linearization (CFDL) as well as modified partial form) is given. Using a three-tank system benchmark, the experimental results are validated concerning the performance behavior. The results obtained demonstrate the effectiveness of the methods introduced.

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.

Model-Free Control Approach of a Three-Tank System Using an Adaptive-Based Control

2017 ◽  
Author(s):  
Elmira Madadi ◽  
Yao Dong ◽  
Dirk Söffker
Keyword(s):  
Tracking Error ◽  
Identification Problem ◽  
Dynamical Structure ◽  
Modified Model ◽  
Control Approach ◽  
Model Free ◽  
Tank System ◽  
Model Free Control ◽  
Complex Nonlinear Systems ◽  
Error Optimization

For improving the dynamics of systems in the last decades model-based control design approaches are continuously developed. The task to design an accurate model is the most relevant and related task for control engineers, which is time consuming and difficult if in the case of complex nonlinear systems a complex modeling or identification problem arises. For this reason model-free control methods become attractive as alternative to avoid modeling. This contribution focuses on design methods of a model-free adaptive-based controller and modified model-free adaptive-based controller. Modified approach is based on the same adaptive model-free control algorithm performing tracking error optimization. Both approaches are designed for non-linear systems with uncertainties and in the presence of disturbances in order to assure suitable performance as well as robustness against unknown inputs. Using this approach, the controller requires neither the information about the systems dynamical structure nor the knowledge about systems physical behaviors. The task is solved using only the system outputs and inputs, which are measurable. The effectiveness of the proposed method is validated by experiments using a three-tank system.

scholarly journals Mechanism, Actuation, Perception, and Control of Highly Dynamic Multilegged Robots: A Review

2020 ◽  
Vol 33 (1) ◽  
Author(s):  
Jun He ◽  
Feng Gao
Keyword(s):  
State Of The Art ◽  
Future Trend ◽  
Future Research ◽  
Control Methods ◽  
Direct Drive ◽  
Model Free ◽  
Key Points ◽  
Parallel Hybrid ◽  
Model Free Control ◽  
And Control

Abstract Multilegged robots have the potential to serve as assistants for humans, replacing them in performing dangerous, dull, or unclean tasks. However, they are still far from being sufficiently versatile and robust for many applications. This paper addresses key points that might yield breakthroughs for highly dynamic multilegged robots with the abilities of running (or jumping and hopping) and self-balancing. First, 21 typical multilegged robots from the last five years are surveyed, and the most impressive performances of these robots are presented. Second, current developments regarding key technologies of highly dynamic multilegged robots are reviewed in detail. The latest leg mechanisms with serial-parallel hybrid topologies and rigid–flexible coupling configurations are analyzed. Then, the development trends of three typical actuators, namely hydraulic, quasi-direct drive, and serial elastic actuators, are discussed. After that, the sensors and modeling methods used for perception are surveyed. Furthermore, this paper pays special attention to the review of control approaches since control is a great challenge for highly dynamic multilegged robots. Four dynamics-based control methods and two model-free control methods are described in detail. Third, key open topics of future research concerning the mechanism, actuation, perception, and control of highly dynamic multilegged robots are proposed. This paper reviews the state of the art development for multilegged robots, and discusses the future trend of multilegged robots.

scholarly journals Author Correction: Reliance on model-based and model-free control in obesity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lieneke K. Janssen ◽  
Florian P. Mahner ◽  
Florian Schlagenhauf ◽  
Lorenz Deserno ◽  
Annette Horstmann
Keyword(s):  
Model Based ◽  
Model Free ◽  
Model Free Control

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Model-Based and Model-Free Control of DC-DC Converters with High-Order Dynamics and Limited Measurements

2020 ◽  
pp. 1-1
Author(s):  
Javier Loranca ◽  
Jonathan Carlos Mayo Maldonado ◽  
Gerardo Escobar ◽  
Carlos Villarreal-Hernandez ◽  
Thabiso Maupong ◽  
...  
Keyword(s):  
High Order ◽  
Model Based ◽  
Model Free ◽  
Model Free Control
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