Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller

Zhang, F.; Söffker, Dirk

doi:10.1007/s11431-011-4346-8

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The stability criterion is setted up with a data-driven quadratic stability criterion [3], which can establish a relationship between the system states and the stability of motion of the system in real time. In the framework above, state feedback control is utilized with the equation…”

Section: State Feedbackmentioning

confidence: 99%

Stabilization of unknown nonlinear systems using a cognition‐based framework

Shen

Zhang

Söffker

2011

Proc Appl Math and Mech

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This paper considers an adaptive control method based on a cognition-based framework to stabilize unknown nonlinear systems. In order to fulfill the task of stabilization, neither the information about the systems dynamical structure nor the knowledge about system physical behaviors, but the system states, which are assumed as measurable, are required. The structure of the proposed controller consists of three parts. The first part is based on a recurrent neural network (RNN) to be used for local identification of the unknown nonlinear system in real time. The network can be utilized as system characteristics, which is further used to design the controller within the third part. In the second part, the set of the given input values leading to stable behavior of the closed-loop system will be calculated numerically with a geometrical criterion based on a suitable definition of quadratic stability. In the third part, a suitable control input value is chosen accordingly to a time-relevant criteria from the set of input values generated in the second part of the controller. These three parts and their internal connections are arranged within a so-called cognition framework. The proposed cognitive controller is able to gain useful knowledge (with local validity) and define autonomously a suitable control input with respect to the requirements of the time-relevant criteria. Numerical examples are shown to demonstrate the successful application and performance of the method.

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Section: State Feedbackmentioning

confidence: 99%

Stabilization of unknown nonlinear systems using a cognition‐based framework

Shen

Zhang

Söffker

2011

Proc Appl Math and Mech

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“…Recently, neural-network-based (i.e., model-free) control approaches have been proposed in [20] and [21] to stabilize a nonlinear aeroelastic wing section. However, there is very little work (e.g., [3], [22]) dealing with the aeroelastic vibration suppression for a supersonic wing section in the presence of both structural and aerodynamic nonlinearities.…”

Section: Introductionmentioning

confidence: 99%

A Continuous Robust Control Strategy for the Active Aeroelastic Vibration Suppression of Supersonic Lifting Surfaces

Zhang

Wang

Behal

et al. 2012

International Journal of Aeronautical and Space Sciences

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The model-free control of aeroelastic vibrations of a non-linear 2-D wing-flap system operating in supersonic flight speed regimes is discussed in this paper. A novel continuous robust controller design yields asymptotically stable vibration suppression in both the pitching and plunging degrees of freedom using the flap deflection as a control input. The controller also ensures that all system states remain bounded at all times during closed-loop operation. A Lyapunov method is used to obtain the global asymptotic stability result. The unsteady aerodynamic load is considered by resourcing to the non-linear Piston Theory Aerodynamics (PTA) modified to account for the effect of the flap deflection. Simulation results demonstrate the performance of the robust control strategy in suppressing dynamic aeroelastic instabilities, such as non-linear flutter and limit cycle oscillations.

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A Model‐Free Stability‐Based Adaptive Control Method for Unknown Nonlinear Systems

Shen

Söffker

2013

Proc Appl Math and Mech

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This contribution considers an adaptive control method based on a cognition-based framework to stabilize unknown nonlinear systems online. This method requires only the system outputs, which are assumed as measurable. The structure of the framework consists of three parts. The first part is based on a dynamic recurrent neural network (DRNN) to be used for local identification, analysis and multi-step-ahead prediction of the system. In the second part, a set of given input values will be calculated numerically with a geometrical criterion based on a suitable definition of quadratic stability. In the third part, the most suitable control input value is chosen for the next predefined time interval according to a suitable cost function. The proposed controller is able to gain useful local knowledge and define autonomously suitable local control input according to the stability criterion. Numerical examples using inverted pendulum system and Lorenz system are shown to demonstrate the successful application and performance of the method.

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Quadratic stabilization of a nonlinear aeroelastic system using a novel Neural-Network-based controller

Cited by 5 publications

References 26 publications

Stabilization of unknown nonlinear systems using a cognition‐based framework

Stabilization of unknown nonlinear systems using a cognition‐based framework

A Continuous Robust Control Strategy for the Active Aeroelastic Vibration Suppression of Supersonic Lifting Surfaces

A Model‐Free Stability‐Based Adaptive Control Method for Unknown Nonlinear Systems

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