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Proceedings ArticleDOI

Experimental validation of generalized predictive control for active flutter suppression

15 Sep 1996-pp 125-129

AbstractThis paper presents a status report on the experimental results of the transonic wind-tunnel test conducted to demonstrate the use of generalized predictive control for flutter control of a subsonic airfoil. The generalized predictive control algorithm is based on the minimization of a suitable cost function over a finite prediction horizon. The cost function minimizes the sum of the mean square output of the plant predictions using a suitable plant model, weighted square of control increments, and the term which incorporates the input constraints. The characteristics of the subsonic airfoil are such that its dynamics are invariant to low input frequencies. This results in a control surface that drifts within the specified input constraints. An augmentation to the cost function that penalizes this low frequency drift is derived and demonstrated. The initial validation of the controller uses a linear plant predictor model for the computation of the control inputs. The generalized predictive controller based on this model could successfully suppress the flutter for all testable mach numbers and dynamic pressures in the transonic region. The wind-tunnel test results confirmed that the generalized predictive controller is robust to modeling errors. The simulation results that were used to determine the nominal ranges for control parameters before wind-tunnel testing are also included. The wind-tunnel test results were in good agreement with the results of the simulation.

Topics: Model predictive control (57%), Robust control (56%), Control theory (56%), Flutter (55%), Transonic (50%)

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Citations
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01 Jan 2006
Abstract: Flutter is an in-flight vibration of flexible structures caused by energy in the airstream absorbed by the lifting surface. This aeroelastic phenomenon is a problem of considerable interest in the aeronautic industry, because flutter is a potentially destructive instability resulting from an interaction between aerodynamic, inertial, and elastic forces. To overcome this effect, it is possible to use passive or active methodologies, but passive control adds mass to the structure and it is, therefore, undesirable. Thus, in this paper, the goal is to use linear matrix inequalities (LMIs) techniques to design an active state-feedback control to suppress flutter. Due to unmeasurable aerodynamic-lag states, one needs to use a dynamic observer. So, LMIs also were applied to design a state-estimator. The simulated model consists of a classical flat plate in a two-dimensional flow. Two regulators were designed, the first one is a non-robust design for parametric variation and the second one is a robust control design, both designed by using LMIs. The parametric uncertainties are modeled through polytopic uncertainties. The paper concludes with numerical simulations for each controller. The open-loop and closed-loop responses are also compared and the results show the flutter suppression. The perfomance for both controllers are compared and discussed. Keywords : Flutter, active control, LMI, polytopic uncertainties, robustness

13 citations


Journal ArticleDOI
Abstract: Department of Mechanical Design State University of Campinas - UNICAMP Cidade Universitaria, Rua Mendeleiev s/n, 13083-970 Campinas, SP

12 citations


Cites background from "Experimental validation of generali..."

  • ...Haley and Soloway (1996) have made an experimental investigation in a transonic wind-tunnel to demonstrate the use of the generalized predictive control for flutter suppression of a subonic airfoil....

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  • ...The idea is old and it was first tested in 1973 on a B-52-E aircraft that achieved flight velocity above the specified limit, besides some problems with model accuracy and robustness, (Garrick, 1976)....

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Journal ArticleDOI
Abstract: Every object has its own natural frequency when the frequency of a source is equal to the objects natural frequency the object will be tends to vibrate this may result in flutter for an aircraft during its cruise speed.Flutter, an unstable oscillatory aerodynamic condition with high frequency and large amplitude ensuing from fluid structure interaction is of precise interest for many aero elastic researchers. This demon results in a catastrophic failure of structure rapidly. Therefore, there is an immense requirement of predicting the flutter speed accurately considering the various uncertain conditions obviously. The general theory of aero elastic instability and the crux of elementary mechanism of flutter have been explained profoundly by many earlier researchers. Flutter problem has been of great interest since early years of flight these will lead to aerodynamic instability and life reduction of an aircraft wing and its components.So while designing an aircraft these flutter has to be consider. We had analyzed a subsonic passenger aircraft in its cruise speed using optimization tools CFD and FEA tools. In our paper we would like to exposure the results computationally including both fluid and structural interaction problem. By this way we can able to predict accurately the nature of an aircraft during its flutter. The structural deformation and stress distribution will be calculated at various conditions

8 citations



Journal ArticleDOI
Abstract: Aeroelasticity is the study of the mutual interaction that takes place among the inertial, elastic and aerodynamic forces acting on the structural members exposed to an airstream and the influence of this study on the design This review paper deals with the investigation of the aeroelasticity phenomena The effect of the aeroelasticity phenomena occurring while designing the wing of the aircraft are stated in detail Flutter suppression and its techniques are investigated in this paper The aeroelastic testing techniques available in this field and the efficient methods to solve these problems are discussed The aeroelastic optimization techniques processes are reported The fluid and structure interaction of the non-linear flexible wing structure results have been discussed for the various methods The application of the MSC software and finite element methods are discussed The aeroelastic applications are also summarized

1 citations


References
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Journal ArticleDOI
TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.
Abstract: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described. >

7,508 citations


Journal ArticleDOI
David Clarke, C. Mohtadi, P S Tuffs1
TL;DR: A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement and to be a contender for general self-tuning applications.
Abstract: Current self-tuning algorithms lack robustness to prior choices of either dead-time or model order. A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement. This receding-horizon method depends on predicting the plant's output over several steps based on assumptions about future control actions. One assumption—that there is a “control horizon” beyond which all control increments become zero—is shown to be beneficial both in terms of robustness and for providing simplified calculations. Choosing particular values of the output and control horizons produces as subsets of the method various useful algorithms such as GMV, EPSAC, Peterka's predictive controller (1984, Automatica, 20, 39–50) and Ydstie's extended-horizon design (1984, IFAC 9th World Congress, Budapest, Hungary). Hence GPC can be used either to control a “simple” plant (e.g. open-loop stable) with little prior knowledge or a more complex plant such as nonminimum-phase, open-loop unstable and having variable dead-time. In particular GPC seems to be unaffected (unlike pole-placement strategies) if the plant model is overparameterized. Furthermore, as offsets are eliminated by the consequence of assuming a CARIMA plant model, GPC is a contender for general self-tuning applications. This is verified by a comparative simulation study.

3,522 citations


"Experimental validation of generali..." refers methods in this paper

  • ...This controller is also robust with respect to modeling errors, over and under parameterization, and sensor noise [ 4 ]....

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  • ...GPC was introduced by Clarke and his co-workers in 1987 and it belongs to a class of Model-Based Predictive Control (MBPC) [ 4 ][5][6]....

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Book
01 Jan 1994
Abstract: Advances in model-based predictive control Matching the uncertainty of the model given by global identification techniques to the robustness of a model-based predictive controller Stability and output terminal constraints in predictive control Use of qualitative models for the choice of design parameters of model-based predictive controllers Artificial neural network model-based control Neural network based adaptive predictive control Fuzzy generalized predictive controller A game theoretic approach to moving horizon control Pre-tuning of self-tuners Stabilizing predictive control: the singular transition-matrix case Robust adaptive predictive control Continuous-time generalised predictive control (CGPC): Implementation issues Evaluation of stochastic characteristics for a constrained GPC algorithm Model-based predictive control for two-dimensional dynamic processes Model-based predictive controller with Kalman filtering for state estimation On the relationship between GPC and PIP controllers A comparative study of GPC and DMC controllers Constrained generalized predictive control with dynamic programming Min-max model-based predictive control Stability and robustness of constrained model predictive control New sufficient conditions for global stability of receding horizon control for discrete-time nonlinear systems Nonlinear model-based predictive control Model-based predictive control methods based on non-linear and bilinear parametric system descriptions Stability results for constrained model predictive control Stability in constrained predictive control Stability of constrained MBPC using an internal model control structure Actuator nonlinearities in predictive control Advances in constrained generalized predictive control with application to a dynamometer model Application of constrained GPC for improving performance of controlled plants Generalised predictive control in clinical anaesthesia Modelling control in a large water treatment works Design and realization of a MIMO predictive controller for a 3-tank system Predictive control for target tracking Predictive control application in the machine-tool field Application of GPC to a solar power plant

329 citations


01 Feb 1997
TL;DR: This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm and results show convergence to a good solution within two iterations and timing data show that real-time control is possible.
Abstract: An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant''s nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm''s implementation are also included.

106 citations


"Experimental validation of generali..." refers background or methods in this paper

  • ...' For more information on the software implementation and timing specifications see [ 7 ]....

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  • ...Equations (4) and (5) should be added to the Jacobian and Hessian equations of [ 7 ] for a complete solution to the GPC control input....

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  • ...The computational issues of Newton-Raphson are also addressed in [ 7 ]....

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  • ...To include this filter in the derivation of the CFM iterative solution found in [ 7 ], the Jacobian and the Hessian of the filter are needed....

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Proceedings ArticleDOI
01 Jan 1992
TL;DR: Results obtained from a second wind tunnel test of the first model in the Benchmark Models Program are described, which consisted of a rigid semispan wing having a rectangular planform and a NACA 0012 airfoil shape which was mounted on a flexible two degree of freedom mount system.
Abstract: The Structural Dynamics Division at NASA Langley Research Center has started a wind tunnel activity referred to as the Benchmark Models Program. The primary objective of this program is to acquire measured dynamic instability and corresponding pressure data that will be useful for developing and evaluating aeroelastic type computational fluid dynamics codes currently in use or under development. The program is a multi-year activity that will involve testing of several different models to investigate various aeroelastic phenomena. This paper describes results obtained from a second wind tunnel test of the first model in the Benchmark Models Program. This first model consisted of a rigid semispan wing having a rectangular planform and a NACA 0012 airfoil shape which was mounted on a flexible two degree of freedom mount system. Experimental flutter boundaries and corresponding unsteady pressure distribution data acquired over two model chords located at the 60 and 95 percent span stations are presented.

90 citations