Stabilization of a Nonlinear Aeroelastic Wing-Section Using Modified Model-Free Control with Self-Improvement of Control Performance

2017 ◽

Cited By ~ 1

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.

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Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

International Journal of Systems Science ◽

10.1080/00207721.2016.1236423 ◽

2016 ◽

Vol 48 (5) ◽

pp. 1071-1083 ◽

Cited By ~ 42

Author(s):

Mircea-Bogdan Radac ◽

Radu-Emil Precup ◽

Raul-Cristian Roman

Keyword(s):

Performance Improvement ◽

Control Performance ◽

Virtual Reference ◽

Q Learning ◽

Model Free ◽

Model Free Control

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Towards an Adaptive Control Concept for Unknown Nonlinear MIMO Systems

Volume 6: 14th International Conference on Multibody Systems, Nonlinear Dynamics, and Control ◽

10.1115/detc2018-85357 ◽

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.

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Modified Model-Free Adaptive Predictive Control Applied to Vibration Reduction of Mechanical Flexible Systems

Volume 2: 16th International Conference on Multibody Systems, Nonlinear Dynamics, and Control (MSNDC) ◽

10.1115/detc2020-22033 ◽

2020 ◽

Author(s):

Hoang Anh Pham ◽

Dirk Söffker

Keyword(s):

Predictive Control ◽

Vibration Reduction ◽

Recursive Least Squares ◽

Projection Algorithm ◽

Effective Control ◽

Control Input ◽

Flexible Systems ◽

Control Performance ◽

Modified Model ◽

Model Free

Abstract Model predictive control (MPC) has become more attractive in control engineering for the last decades because of its efficiency and robustness. In this paper, an effective control strategy is proposed for vibration reduction of mechanical flexible systems in which establishment of a global dynamic model of the controlled system is not necessary. A modified model-free adaptive predictive controller is designed by combination of MPC and model-free control theory. The novel idea of this contribution is that by using the compact-form dynamic linearization technique, the upcoming system outputs within a specified prediction horizon can be predicted in sequence. The data-based prediction model of the system only requires input/output information, and therefore the future control input increments as well as the unknown system parameters called pseudo-jacobian matrix can be estimated. To improve parameter estimation accuracy, another online estimation method namely recursive least-squares algorithm is applied instead of using the conventional projection algorithm. The control performance is verified nummerically for vibration control of a flexible ship-mounted crane represented as a multi-input multi-output (MIMO) system. Simulation results indicate that significant reduction of the crane oscillations and better control performance are observed when using the proposed controller in comparison with other traditional methods.

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