Servocontrol of the GS16 Turbine Gas Metering Valve by Physics-Based Robust Controller Synthesis

2008 ◽  
Vol 130 (5) ◽  
Author(s):  
Kamran E. Shahroudi ◽  
Peter M. Young

We present a design approach for robust controller synthesis using a μ-synthesis procedure, which returns a controller in an observor/state-feedback form with physically meaningful states. We are also able to (approximately) retain this physical meaning when using balanced truncation to (significantly) reduce the controller order, as is often necessary in practice. The advantages of this physics-based approach are illustrated by a detailed outline of the controller design for Woodward Governor’s GS16 Turbine Gas Metering Valve.

2018 ◽  
Vol 19 (11) ◽  
pp. 691-698 ◽  
Author(s):  
G. L. Degtyarev ◽  
R. N. Faizutdinov ◽  
I. O. Spiridonov

In the paper multiobjective robust controller synthesis problem for nonlinear mechanical system described by Lagrange’s equations of the second kind is considered. Such tasks have numerous practical applications, for example in controller design of robotic systems and gyro-stabilized platforms. In practice, we often have to use uncertain mathematical plant models in controller design. Therefore, ensuring robustness in presence of parameters perturbations and unknown external disturbances is an important requirement for designed systems. Much of modern robust control theory is linear. When the actual system exhibits nonlinear behavior, nonlinearities are usually included in the uncertainty set of the plant. A disadvantage of this approach is that resulting controllers may be too conservative especially when nonlinearities are significant. The nonlinear H∞ optimal control theory developed on the basis of differential game theory is a natural extension of the linear robust control theory. Nonlinear theory methods ensure robust stability of designed control systems. However, to determine nonlinear H∞-control law, the partial differential equation have to be solved which is a rather complicated task. In addition, it is difficult to ensure robust performance of controlled processes when using this method. In this paper, methods of linear parameter-varying (LPV) systems are used to synthesize robust control law. It is shown, that Lagrange system may be adequately represented in the form of quasi-LPV model. From the computational point of view, the synthesis procedure is reduced to convex optimization techniques under constraints expressed in the form of linear matrix inequalities (LMIs). Measured parameters are incorporated in the control law, thus ensuring continuous adjustment of the controller parameters to the current plant dynamics and better performance of control processes in comparison with H∞-regulators. Furthermore, the use of the LMIs allows to take into account the transient performance requirements in the controller synthesis. Since the quasi-LPV system depends continuously on the parameter vector, the LMI system is infinite-dimensional. This infinitedimensional system is reduced to a finite set of LMIs by introducing a polytopic LPV representation. The example of multiobjective robust control synthesis for electro-optical device’s line of sight pointing and stabilization system suspended in two-axes inertially stabilized platform is given.


Author(s):  
Aras Adhami-Mirhosseini ◽  
Mohammad J. Yazdanpanah ◽  
Ali Khaki-Sedigh

In this paper, a new methodology for robust controller design in nonlinear multivariable systems is suggested to guarantee asymptotic output tracking. The systems under consideration are perturbed by functionally bounded matched and unmatched uncertainties/perturbations and assumed to be described in the strict-feedback form. The main idea of the methodology is based on the combination of conventional sliding mode control and backstepping algorithm. The proposed controller called nested sliding mode controller that is obtained through a stepwise algorithm. It has the ability of rejecting nonvanishing perturbations by using dynamic switches, unlike conventional and other hierarchical sliding mode design methods. Performance is studied through theorems and verified by two numerical examples.


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