Aircraft Loss-of-Control Accident Prevention: Switching Control of the GTM Aircraft with Elevator Jam Failures

Switching control, servomechanism, and H2 control theory are used to provide a practical and easy-to-implement solution for the actuator jam problem. A jammed actuator not only causes a reduction of control authority, but also creates a persistent disturbance with uncertain amplitude. The longitudinal dynamics model of the NASA GTM UAV is employed to demonstrate that a single fixed reconfigured controller design based on the proposed approach is capable of accommodating an elevator jam failure with arbitrary jam position as long as the thrust control has enough control authority. This paper is a first step towards solving a more comprehensive in-flight loss-of-control accident prevention problem that involves multiple actuator failures, structure damages, unanticipated faults, and nonlinear upset regime recovery, etc

Optimal shipboard power system management via mixed integer dynamic programming

Proceedings Electric Ship Technology Symposium, Philadelphia, Pa. Retrieved 4/1/2006 from http://www.pages.drexel.edu/~hgk22/OnlinePubs/KwatnyMensahNieburTeolisESTS05.pdfPower systems involve continuous and discrete components and controls. The modeling of ‘hybrid’ power systems using a logical specification to define the transition dynamics of the discrete subsystem is described. A computational tool for reduction of the logical specification to a set of inequalities is discussed. The use of the transformed model in a dynamic programming approach to the design of optimal feedback controls is described. Examples are given

Nonlinear reconfiguration for asymmetric failures in a six degree-of-freedom F-16

Proceedings, American Control Conference, pp 1823-1828. Retrieved April 2006 from http://www.pages.drexel.edu/~hgk22/OnlinePubs/Thomas%20Kwatny%20Chang%20ACC04%200326_WeP15.6.pdf.In this paper we consider an F-16 fighter aircraft subject to asymmetric actuator failures. To address nonsymmetric faults it is not possible to decouple the longitudinal and lateral dynamics. It is necessary to deal with a full six degree of freedom airframe. First, we outline an automated procedure to assemble symbolic and simulation models of complex aircraft. The symbolic model can be manipulated in various ways and used for both linear and nonlinear control system design. In the event of actuator failures, the failed surfaces not only cease to function as viable inputs but also impose persistent disturbances on the system. As previously shown, the problem of designing a reconfigured controller can be formulated as a nonlinear disturbance rejection problem. We apply this method to design a controller for the F-16

Bifurcation analysis of flight control systems

Proceedings International Federation of Automatic Control Triennial World Congress, Prague, July 2005. Retrieved April 2006 from http://www.pages.drexel.edu/~hgk22/OnlinePubs/Thomas%20Kwatny%20Chang%20IFAC%202005.pdfHigh performance systems, like the F-16, when pushed to their limits encounter qualitative changes in control system properties like loss of controllability or observability. This work identifies and characterizes bifurcations occurring in a nonlinear six degree of freedom F-16 in two scenarios - straight and level flight and in a coordinated turn. Phenomena such as stall, tumbling and spin-roll departure were observed around bifurcation points. This work provides a basis for a formal understanding of how aircraft depart from controlled flight, it is a prerequisite for the systematic design of recovery strategies, and it will contribute to the design of reconfigurable control of impaired aircraft

Aircraft Accident Prevention: Loss-of-Control Analysis

The majority of fatal aircraft accidents are associated with loss-of-control . Yet the notion of loss-of-control is not well-defined in terms suitable for rigorous control systems analysis. Loss-of-control is generally associated with flight outside of the normal flight envelope, with nonlinear influences, and with an inability of the pilot to control the aircraft. The two primary sources of nonlinearity are the intrinsic nonlinear dynamics of the aircraft and the state and control constraints within which the aircraft must operate. In this paper we examine how these nonlinearities affect the ability to control the aircraft and how they may contribute to loss-of-control. Examples are provided using NASA s Generic Transport Model

Power system dynamics and control

This monograph explores a consistent modeling and analytic framework that provides the tools for an improved understanding of the behavior and the building of efficient models of power systems. It covers the essential concepts for the study of static and dynamic network stability, reviews the structure and design of basic voltage and load-frequency regulators, and offers an introduction to power system optimal control with reliability constraints. A set of Mathematica tutorial notebooks providing detailed solutions of the examples worked-out in the text, as well as a package that will enable readers to work out their own examples and problems, supplements the text. A key premise of the book is that the design of successful control systems requires a deep understanding of the processes to be controlled; as such, the technical discussion begins with a concise review of the physical foundations of electricity and magnetism. This is followed by an overview of nonlinear circuits that include resistors, inductors, capacitors, and memristors, along with an examination of basic circuit mathematical models and formulations. AC power systems are considered next, in which models for their basic components are derived. The following chapters address power system dynamics using both the ordinary differential equation and differential-algebraic equation models of a power network, as well as bifurcation analysis and the behavior of a network as it approaches voltage instability. Two classic control problems – voltage regulation and load-frequency control – are then described, including the coordination of economic dispatch with load-frequency control. Finally, power system control problems involving operation in highly nonlinear regimes and subjected to discrete failure modes are discussed. Power System Dynamics and Control will appeal to practicing power system engineers, control systems engineers interested in power systems, and graduate students in these areas. Because it provides sufficient information about their modelling and behavior, control engineers without a background in power systems will also find it to be a valuable resource

Computation of singular and singularity induced bifurcation points of differential-algebraic power system model

Abstract—In this paper, we present an efficient algorithm to compute singular points and singularity-induced bifurcation points of differential-algebraic equations for a multimachine power-system model. Power systems are often modeled as a set of differential-algebraic equations (DAE) whose algebraic part brings singularity issues into dynamic stability assessment of power systems. Roughly speaking, the singular points are points that satisfy the algebraic equations, but at which the vector field is not defined. In terms of power-system dynamics, around singular points, the generator angles (the natural states variables) are not defined as a graph of the load bus variables (the algebraic variables). Thus, the causal requirement of the DAE model breaks down and it cannot predict system behavior. Singular points constitute important organizing elements of power-system DAE models. This paper proposes an iterative method to compute singular points at any given parameter value. With a lemma presented in this paper, we are also able to locate singularity induced bifurcation points upon identifying the singular points. The proposed method is implemented into voltage stability toolbox and simulations results are presented for a 5-bus and IEEE 118-bus systems. Index Terms—Differential-algebraic equations (DAEs), power systems, singular points, singularity-induced bifurcations. I