Robust aeroelastic control of flapped wing systems using a sliding mode observer

Na, Sungsoo; Librescu, Liviu; Kim, Myung‐Hyun; Jeong, In Joo; Marzocca, Pier

doi:10.1016/j.ast.2005.10.002

Cited by 32 publications

(14 citation statements)

References 9 publications

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“…Step (9) Output global optimum: if the condition, such as iteration number is not reached, go back to Otherwise, output global optimum by NLJ search in GNPSO algorithm. e global optimum is the nal optimized control parameter of IMC-PID controller for the wing vibration system.…”

Section: Procedures Of Hybrid Gnpso Based Control Optimizationmentioning

confidence: 99%

“…In the wing vibration control system, the proportionalintegral-derivative (PID) controller [6,8] has been developed and most widely used to suppress the vibrations for linear wing vibration system. For nonlinear wing vibration systems, sliding mode control [9,10], robust control [11,12] and H ∞ control [13] have been studied to control aeroelastic vibrations under unsteady ow and nonlinear spring. To deal with multipleinput-multiple-output (MIMO) wing vibration system, a state estimation based adaptive output feedback controller [14] was designed for vibration suppression on a nonlinear wing section.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Grey Particle Swarm Optimization and New Luus‐Jaakola Hybrid Algorithm Optimized IMC‐PID Controller for Diverse Wing Vibration Systems

Yang

2019

Complexity

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The PID control plays important role in wing vibration control systems. However, how to efficiently optimize the PID parameters for different kinds of wing vibration systems is still an open issue for control designers. The problem of PID control optimization is first converted into internal mode control based PID (IMC-PID) parameters optimization problem for complex wing vibration systems. To solve this problem, a novel optimization technique, called GNPSO is proposed based on the hybridization of improved grey particle swarm optimization (GPSO) and new Luus-Jaakola algorithm (NLJ). The original GPSO is modified by using small population size/iteration number, employing new grey analysis rule and designing new updating formula of acceleration coefficients. The hybrid GNPSO benefits improved global exploration of GPSO and strong local search of new Luus-Jaakola (NLJ), so as to avoid arbitrary and inefficient search of global optimum and prevent the trap in local optimum. Diverse wing vibration systems, including linear system, nonlinear system and multiple-input-multiple-output system are considered to verify the effectiveness of proposed method. Simulation results show that GNPSO optimized method obtains improved vibration control performance, stronger robustness and wide applicability on all system cases, compared to existing evolutionary algorithm based tuning methods. Enhanced optimization convergence and computation efficiency obtained by GNPSO tuning technique are also verified by statistical analysis.

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Section: Procedures Of Hybrid Gnpso Based Control Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Grey Particle Swarm Optimization and New Luus‐Jaakola Hybrid Algorithm Optimized IMC‐PID Controller for Diverse Wing Vibration Systems

Yang

2019

Complexity

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“…The aerodynamic forces are derived from the Theodorsen equations. In Na et al [27] the aeroelastic response and control of 3-DOF flapped-wing system in an incompressible fluid flow and exposed to external pressure pulse was studied.…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of flow-induced nonlinear vibrations of an airfoil with three degrees of freedom

et al. 2011

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“…In other words, all of the system states are assumed to be measurable. To the authors' knowledge, there are few investigations on the observer‐controller for aeroelastic system . A LQG control strategy using a sliding mode observer (SMO) was proposed and the performance was demonstrated by simulation in .…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Observer Controller Design for a Two Dimensional Aeroelastic System with Gust Load

Yuan¹,

Qiu²,

Wang³

2018

Asian Journal of Control

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The aim of this paper is to develop state estimation and sliding mode control schemes for the vibration suppression of an underactuated wing aeroelastic system in the presence of a gust load disturbance. Ignoring structural elastic deformation and using the concentrated elastic system (spring) to simulate the overall elastic deformation, this aeroelastic model consists of a straight wing and spring system, describing flap and pitch freedoms. The corresponding dynamic motion equation is established using the Lagrange method, and the gust is modeled as a typical "1-cosine" gust. The aerodynamic lift and moment on the wing are computed by strip theory. The open loop system exhibits the limit cycle oscillations (LCOs) at a certain freestream velocity. The objective is to design a control system for suppressing the LCOs. For the purpose of control, a single trailing-edge control surface is used. It is assumed that only the pitch angle is measured and the remaining state variables needed for full state feedback are estimated by the designed observer. Then an integral sliding surface is put forward on the estimation space; a new continuous reaching law is proposed to reduce the chattering phenomena. The finite-time reachability of the predesigned sliding surface is proved and guaranteed by the designed sliding mode control law. The sufficient condition for the asymptotic stability of the closed-loop system composed of the sliding mode dynamics and the error dynamical system is derived in terms of linear matrix inequality (LMI). The effectiveness of the proposed strategy is finally demonstrated by simulation results.

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Robust aeroelastic control of flapped wing systems using a sliding mode observer

Cited by 32 publications

References 9 publications

Improved Grey Particle Swarm Optimization and New Luus‐Jaakola Hybrid Algorithm Optimized IMC‐PID Controller for Diverse Wing Vibration Systems

Improved Grey Particle Swarm Optimization and New Luus‐Jaakola Hybrid Algorithm Optimized IMC‐PID Controller for Diverse Wing Vibration Systems

Numerical analysis of flow-induced nonlinear vibrations of an airfoil with three degrees of freedom

Sliding Mode Observer Controller Design for a Two Dimensional Aeroelastic System with Gust Load

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