John J. Burken

Bio: John J. Burken is an academic researcher from Neil A. Armstrong Flight Research Center. The author has contributed to research in topics: Adaptive control & Control system. The author has an hindex of 20, co-authored 60 publications receiving 1087 citations.

Two Reconfigurable Flight-Control Design Methods: Robust Servomechanism and Control Allocation

Abstract: Two methods for control system reconfiguration have been investigated. The first method is a robust servomechanism control approach (optimal tracking problem) that is a generalization of the classical proportional-plus-integral control to multiple input-multiple output systems. The second method is a control-allocation approach based on a quadratic programming formulation. A globally convergent fixed-point iteration algorithm has been developed to make onboard implementation of this method feasible. These methods have been applied to reconfigurable entry flight control design for the X-33 vehicle. Examples presented demonstrate simultaneous tracking of angle-of-attack and roll angle commands during failures of the fight body flap actuator. Although simulations demonstrate success of the first method in most cases, the control-allocation method appears to provide uniformly better performance in all cases.

Reconfigurable Flight Control Designs With Application to the X-33 Vehicle

Abstract: Two methods for control system reconfiguration have been investigated. The first method is a robust servomechanism control approach (optimal tracking problem) that is a generalization of the classical proportional-plus-integral control to multiple input-multiple output systems. The second method is a control-allocation approach based on a quadratic programming formulation. A globally convergent fixed-point iteration algorithm has been developed to make onboard implementation of this method feasible. These methods have been applied to reconfigurable entry flight control design for the X-33 vehicle. Examples presented demonstrate simultaneous tracking of angle-of-attack and roll angle commands during failures of the right body flap actuator. Although simulations demonstrate success of the first method in most cases, the control-allocation method appears to provide uniformly better performance in all cases.

Current and Future Research in Active Control of Lightweight, Flexible Structures Using the X-56 Aircraft

Abstract: The X-56 Multi-Utility Technology Testbed aircraft system is a versatile experimental research flight platform. The system was primarily designed to investigate active control of lightweight flexible structures, but is reconfigurable and capable of hosting a wide breadth of research. Current research includes flight experimentation of a Lockheed Martin designed active control flutter suppression system. Future research plans continue experimentation with alternative control systems, explore the use of novel sensor systems, and experiments with the use of novel control effectors. This paper describes the aircraft system, current research efforts designed around the system, and future planned research efforts that will be hosted on the aircraft system.

Using Engine Thrust for Emergency Flight Control: MD-11 and B-747 Results

Abstract: With modern digital control systems, using engine thrust for emergency flight control to supplement or replace failed aircraft normal flight controls has become a practical consideration. The NASA Dryden Flight Research Center has developed a propulsion-controlled aircraft (PCA) system in which computer-controlled engine thrust provides emergency flight control. An F-15 and an MD-11 airplane have been landed without using any flight control surfaces. Preliminary studies have also been conducted that show that engines on only one wing can provide some flight control capability if the lateral center of gravity can be shifted toward the side of the airplane that has the operating engine(s). Simulator tests of several airplanes with no flight control surfaces operating and all engines out on the left wing have all shown positive control capability within the available range of lateral center-of-gravity offset. Propulsion-controlled aircraft systems that can operate without modifications to engine control systems, thus allowing PCA technology to be installed on less capable airplanes or at low cost, are also desirable. Further studies have examined simplified 'PCA Lite' and 'PCA Ultralite' concepts in which thrust control is provided by existing systems such as auto-throttles or a combination of existing systems and manual pilot control.

An adaptive threshold approach for the design of an actuator failure detection and identification scheme

Abstract: Typical logic schemes associated with failure detection and identification algorithms rely on a set of constant thresholds. The selection of the values for these thresholds is generally a tradeoff between the goals of maximizing failure detectability while minimizing false alarm rates. The main purpose of this brief is to propose an alternative to this conventional approach for defining the thresholds of a specific aircraft actuator failure detection and identification scheme. A specific set of detection and identification criteria for failures of the decoupled stabilators, canards, ailerons, and rudders of the NASA Advanced Control Technology for Integrated Vehicle F-15 aircraft have been formulated in terms of neural network estimates and correlation functions of the angular rates. The proposed scheme is based on the use of adaptive thresholds through the floating limiter concept. This new approach eliminates the need for parameter scheduling and has shown to be able to reduce the delays associated with the constant threshold method. The functionality of the approach has been illustrated through numerical simulations on the West Virginia University NASA Intelligent Flight Control System F-15 simulator.

Planning Algorithms: Introductory Material

Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Evaluation of optimization methods for control allocation

Abstract: The performanceand computational requirements ofoptimization methodsfor control allocation areevaluated Two control allocation problems are formulated: a direct allocation method that preserves the directionality of the moment and a mixed optimization method that minimizes the error between the desired and the achieved momentsaswellasthecontroleffortTheconstrainedoptimizationproblemsaretransformedinto linearprograms so that they can be solved using well-tried linear programming techniques such as the simplex algorithm A variety of techniques that can be applied for the solution of the control allocation problem in order to accelerate computations are discussed Performance and computational requirements are evaluated using aircraft models with different numbers of actuators and with different properties In addition to the two optimization methods, three algorithms with low computational requirements are also implemented for comparison: a redistributed pseudoinverse technique, a quadratic programming algorithm, and a e xed-point method The major conclusion is that constrained optimization can be performed with computational requirements that fall within an order of magnitude of those of simpler methods The performance gains of optimization methods, measured in terms of the error between the desired and achieved moments, are found to be small on the average but sometimes signie cantAvariety ofissuesthataffecttheimplementation ofthevariousalgorithmsin ae ight-controlsystem are discussed