Gain Scheduling

We consider the application of a conditional integrator based sliding mode control design for robust regulation of minimum-phase nonlinear systems to the control of the longitudinal flight dynamics of an F-16 aircraft. The design exploits the modal decomposition of the linearized dynamics into its short-period and phugoid approximations. The control design is based on linearization, but is implemented on the nonlinear multiple-input multiple-output longitudinal model of the F-16 aircraft. We consider model following for the angle-of- attack, with the regulation of the aircraft velocity (or the Mach- hold autopilot) as a secondary objective. It is shown through extensive simulations that the inherent robustness of the SMC design provides a convenient way to design controllers without gain scheduling, with transient performance that is far superior to that of a conventional gain-scheduled approach with integral control.

En el artículo se aborda la automatización de una autoclave utilizada en el proceso de esterilización. Se diseñaron algoritmos de control para la temperatura, la presión y el nivel del agua. Para realizar el control de la misma se trabajó inicialmente con un controlador PI diseñado con el método Ciancone-Marlin y luego se planteó una solución basada en la implementación de un controlador PI con ganancia programable sintonizado con la misma técnica y utilizando ecuaciones de regresión para el cálculo de sus parámetros. El método de ganancia programable se puede considerar un control adaptativo no lineal, en el sentido que se realiza con un controlador lineal cuyos parámetros cambian en función de las condiciones de operación según una ecuación o una tabla precalculada. Los algoritmos de control fueron programados en plataforma Labview

Abstract: A rotorcraft-based unmanned aerial vehicle exhibits more complex properties compared to its full-size counterparts due to its increased sensitivity to control inputs and disturbances and higher bandwidth of its dynamics. As an aerial vehicle with vertical take-off and landing capability, the helicopter specifically poses a difficult problem of transition between forward flight and unstable hover and vice versa. The LPV control technique explicitly takes into account the change in performance due to the real-time parameter ...

Bu makalede akış kontrolü problemlerinin modellemesi için
yeni bir yöntem geliştirilmiştir. Bu yöntemde akışın
hesaplamalı akışkanlar dinamiği (HAD) benzetimleri ile elde
edilen anlık görüntülerden bir uygun dikgen ayrışımı (UDA)
elde edilir ve bu ayrışımın zaman katsayılarına sistem tanılama
yöntemleri uygulanarak bir doğrusal durum uzayı sistemi
oluşturulur. Bu işlem, kesme noktası tabir edilen birkaç
çalışma noktası için tekrarlanır ve kesme noktalarında elde
edilen dinamik modeller, bir çıkış harmanlama tekniği ile
birleştirilir. Oluşturulan modeller birkaç doğrusal zamanla
değişmeyen (DZD) sistemin bileşiminden oluşması
bakımından basit bir yapıya sahip olmakla birlikte, verilen bir
akış zarfı içindeki çok farklı akış koşullarını temsil edebilecek
güce sahiptir. Makalenin sonunda önerilen modelleme
yöntemi, Navier-Stokes denklemleri ile yönetilen ve kinematik
akmazlık değerinin değişkenlik gösterdiği bir akış probleminin
kontrolü üzerinde örneklendirilmiş ve başarılı sonuçlar elde
edildiği görülmüştür.

The area of the analysis and control of linear parameter-varying (LPV) systems has received much recent attention because of its importance in developing systematic techniques for gain-scheduling. An LPV system resembles a linear system that nonlinearly depends on time-varying parameters. Typical approaches for the analysis and control of LPV systems are the scaled small-gain and the dissipative systems approach using smooth parameter-dependent Lyapunov functions (PDLFs). The dissipative systems approach is the more desirable of the two techniques because it can directly treat time-varying parameters. and yield a general LPV-type controller. Furthermore, this approach attractively formulates analysis and synthesis problems as convex optimization problems involving linear matrix inequalities (LMIs) which are now very efficiently solved by computer. However, this approach has two major potential difficulties in selecting an optimal PDLF in order to reduce conservatism of the analysis and synthesis, and solving exactly convex optimization problems involving an infinite number of LMIs. The thesis presents new analysis and control design techniques to avoid these potential drawbacks of the smooth dissipative systems approach. The thesis focuses on a piecewise-affine parameter-dependent linear parameter-varying system and a nonsmooth piecewise-affine parameter-dependent Lyapunov function. To address the non-differential nature of both the LPV system and PDLF, the thesis develops a nonsmooth dissipative systems framework. Then the thesis fully characterizes several important analysis and synthesis problems of LPV systems with this nonsmooth framework. The new approach is shown to yield a less conservative, guaranteed result than previously published LPV approaches. The improvement is direct results of using a more accurate model in the analysis and control, and using a very general class of PDLFs. The derived analysis and synthesis formulations are finite-dimensional convex optimization problems. The new approach also provides a trade-off between conservatism and computational effort of the design technique. Several benchmark problems are used to demonstrate the usefulness, reliability, and feasibility of the proposed new approaches.

In this chapter a gain scheduled optimal controller is designed to solve the path-tracking problem of an airship. The control law is obtained from a coupled linear model of the airship that allows to control the longitudinal and lateral motions simultaneously. Due to the importance of taking into account wind effects, which are rather important due to the airship large volume, the wind is included in the kinematics, and the dynamics is expressed as function of the air velocity. Two examples are presented with the inclusion of wind, one considering a constant wind input and the other considering in addition a 3D turbulent gust, demonstrating the effectiveness of this single controller tracking a reference path over the entire flight envelope.

We propose a method of interpolating linear time-invariant controllers with observer state feedback structure in order to generate a continuously-varying family of controllers that stabilizes a family of linear plants. Gain scheduling is a motivation for this work, and the interpolation method yields guidelines for the design of gain scheduled controllers. The scheduling method is illustrated with the design of