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Limited Authority Adaptive Flight Control
, 2000
"... Contents Acknowledgements iii List of Figures vii Nomenclature xi Summary xiv 1 Introduction 1 1.1 Adaptive Flight Control for Reusable Launch Vehicles .......................................1 1.2 Design Integration Problems in Adaptive Control ................................................5 1.2.1 ..."
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Cited by 23 (11 self)
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Contents Acknowledgements iii List of Figures vii Nomenclature xi Summary xiv 1 Introduction 1 1.1 Adaptive Flight Control for Reusable Launch Vehicles .......................................1 1.2 Design Integration Problems in Adaptive Control ................................................5 1.2.1 Saturation ..................................................................... ..............................5 1.2.2 Linear Input Dynamics............................................................. ..................9 1.2.3 Quantized Control ..................................................................... ...............10 1.2.4 Adaptation While Not in Direct Control ..................................................10 1.2.5 Flight Certification of Adaptive Controllers.............................................11 1.3 Contributions of This Research ..................................................................... .....12 1.4 Brief Outline of Thesis ...............
A Hierarchical Approach to Adaptive Control for Improved Flight Safety
 JOURNAL OF GUIDANCE, CONTROL, AND DYNAMICS
, 2001
"... Following failures of primary aerodynamic actuators, safe flight can be maintained by introducing alternative actuation systems, such as secondary aerodynamic surfaces and propulsion, for critical stability and control augmentation. This paper presents an intelligent hierarchical flight control s ..."
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Cited by 16 (4 self)
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Following failures of primary aerodynamic actuators, safe flight can be maintained by introducing alternative actuation systems, such as secondary aerodynamic surfaces and propulsion, for critical stability and control augmentation. This paper presents an intelligent hierarchical flight control system architecture that is designed using nonlinear adaptive synthesis techniques and online learning neural networks. PseudoControl Hedging is used for proper adaptation in the presence of actuator saturation, rate limits and actuator failure. The hierarchical structure of the proposed approach incorporates nonactive secondary actuation channels that are engaged after a failure of a primary control surface has occurred. These secondary channels are designed to account for the usually lower authority and degraded performance that can be expected with a secondary actuation system. The proposed flight control system is evaluated in a nonlinear flight simulation environment.
Historical overview of research in reconfigurable flight control
 Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering
, 2005
"... This paper presents a historical overview of research in reconfigurable flight control with a focus on work done in the United States. For purposes of this paper, the term reconfigurable flight control is used to refer to software algorithms designed specifically to compensate for failures or damage ..."
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Cited by 13 (0 self)
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This paper presents a historical overview of research in reconfigurable flight control with a focus on work done in the United States. For purposes of this paper, the term reconfigurable flight control is used to refer to software algorithms designed specifically to compensate for failures or damage of flight control effectors or lifting surfaces by using the remaining effectors to generate compensating forces and moments. This paper will discuss influences on the development of the concept of control reconfiguration and initial research and flighttesting of approaches based on explicit fault detection, isolation, and estimation as well as later approaches based on continuously adaptive and intelligent control algorithms. Also, approaches for trajectory reshaping of an impaired aircraft with reconfigurable inner loop control laws will be briefly discussed. Finally, there will be some discussion of current implementations of reconfigurable control to improve safety on production and flight test aircraft and remaining challenges to enable broader use of the technology such as the difficulties of flight certification of these types of approaches. I.
Adaptive Nonlinear Controller Synthesis and Flight Test Evaluation On an Unmanned Helicopter
, 1999
"... Numerous simulation studies have recently revealed the potential benefits of a neural networkbased approach to direct adaptive control in the design of flight control systems. Foremost among the potential benefits is greatly reduced dependence on highfidelity modeling of system dynamics. However, ..."
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Cited by 12 (1 self)
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Numerous simulation studies have recently revealed the potential benefits of a neural networkbased approach to direct adaptive control in the design of flight control systems. Foremost among the potential benefits is greatly reduced dependence on highfidelity modeling of system dynamics. However, the methodology has only recently been proven practical by demonstration in an actual flight system. This paper begins with an overview of the design of a nonlinear adaptive control system for flight test on an unmanned helicopter test bed. Next, the design of an outer loop trajectory tracking controller as well as simulation results are presented. The paper concludes with the presentation of preliminary flight test results of the rate command system that document the actual performance of the control system in flight. 1. Introduction Traditional methods of flight control design consist of gain scheduling many linear point designs across the flight envelope using a high fidelity dynamic si...
Dynamic Neural Networks For Output Feedback Control
 Proceedings of the Conference on Decision and Control
"... A dynamic neural network is designed to estimate velocities from displacement measurements for a nonlinear system. A linearinparameters NN is used for state reconstruction. Conditions are provided under which the estimation error is guaranteed to be ultimately bounded. Subsequently, this observer ..."
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Cited by 8 (5 self)
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A dynamic neural network is designed to estimate velocities from displacement measurements for a nonlinear system. A linearinparameters NN is used for state reconstruction. Conditions are provided under which the estimation error is guaranteed to be ultimately bounded. Subsequently, this observer is integrated into an adaptive controller architecture. The controller is based on model inversion and is augmented with a second learningwhilecontrolling neural network. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined observercontroller feedback system. Open loop and closed loop simulations for a Van Der Pol oscillator are used to illustrate the results. Introduction In the case of linear systems with known parameters, there exists vast literature on estimation theory that allows asymptotic tracking of the actual state by its estimate, e.g. [1, 2]. At the opposite end of the spectrum one can find approaches for nonlinear plants with uncertai...
Backlash Compensation In Nonlinear Systems Using Dynamic Inversion By Neural Networks
, 1999
"... A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of ..."
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Cited by 7 (4 self)
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A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A tuning algorithm is presented for the NN backlash compensator which yields a stable closedloop system. 1 INTRODUCTION A general class of industrial motion control systems has the structure of a nonlinear dynamical system preceded by some nonlinearities in the actuator, either deadzone, backlash, saturation, etc. This includes xypositioning tables [19], robot manipulators [14], overhead crane mechanisms, and more. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the nonanalytic nature of the actuator nonlinearities and the fact that their exact nonlinear functions are unknown, such systems...
High Bandwidth Adaptive Flight Control
 Proceedings of the AIAA Guidance, Navigation, and Control Conference
, 2000
"... This paper presents a novel approach to adaptive output feedbackcontrol. The approach permits adaptation to both parametric uncertainty and unmodeled dynamics. Of particular interest here is its use in high bandwidth flight control, which is made possible through interaction with poorly modeled high ..."
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Cited by 5 (1 self)
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This paper presents a novel approach to adaptive output feedbackcontrol. The approach permits adaptation to both parametric uncertainty and unmodeled dynamics. Of particular interest here is its use in high bandwidth flight control, which is made possible through interaction with poorly modeled high frequency dynamics. Adaptation is achieved using only input/output sequences of the uncertain system. The approach is illustrated by the design of pitchrate flight control system for an R50 experimental helicopter. Introduction Modern fighter aircraft can be operated in highly nonlinear and uncertain flight conditions. In the future, uninhabited aerial vehicles (UAV's) will begin to displace manned aircraft in many traditional roles for both military and civilian missions. Achallenge to designers of flightcontrol systems for future vehicles is to permit nearly carefree operation of high performance vehicles, without limiting their full potential for maneuvering. Presently there exists a...
Backlash Compensation with Filtered Prediction in Discrete Time Nonlinear Systems by Dynamic Inversion Using Neural Networks
 Journal of Control
, 1999
"... A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. This paper extends the dynamic inversion technique to discretetime systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash no ..."
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Cited by 4 (1 self)
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A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. This paper extends the dynamic inversion technique to discretetime systems by using a filtered prediction, and shows how to use a neural network (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discretetime tuning algorithm is given for the NN weights so that the backlash compensation scheme guarantees bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discretetime adaptive control techniques, no certainty equivalence (CE) or linearinthe parameters (LIP) assumptions are needed. 1 Introduction Many physical components of control systems have nonsmooth nonlinea...
Neural Network Augmentation of Linear Controllers With Application to Underwater Vehicles
 American Control Conference 2000
, 2000
"... A novel approach to augment a linear compensator with an online neural network is presented. This scheme provides the benefits of adaptation with only minor modification to the existing control architecture, which is a substantial advantage over other approaches that require complete redesign. A neu ..."
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Cited by 3 (1 self)
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A novel approach to augment a linear compensator with an online neural network is presented. This scheme provides the benefits of adaptation with only minor modification to the existing control architecture, which is a substantial advantage over other approaches that require complete redesign. A neural network update law that guarantees bounded tracking for the augmented architecture is outlined. The advantages of the proposed technique are demonstrated through an application to an Autonomous Underwater Vehicle. The design requirement is for attitude control such that robust trajectory following is achieved. A detailed nonlinear model of the AUV is given, and an operating point for nominal design is selected, about which a linear approximation is obtained. Structured uncertainties due to errors in the computation of hydrodynamic coefficients, linearization and truncation of plant dynamics, as well as effects of unknown disturbances are included in the control synthesis and compensated for by the neural network.
Backlash Compensation in Discrete Time Nonlinear Systems Using Dynamic Inversion by Neural Networks
, 1999
"... A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. The compensator uses backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure f ..."
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Cited by 2 (1 self)
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A dynamics inversion compensation scheme is designed for control of nonlinear discretetime systems with input backlash. The compensator uses backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamics preinverse of an invertible discrete time dynamical system. A discretetime tuning algorithm is given for the NN weights so that the backlash compensation scheme becomes adaptive, guaranteeing bounded tracking and backlash errors, and also bounded parameter estimates. A rigorous proof of stability and performance is given and a simulation example verifies performance. Unlike standard discretetime adaptive control techniques, no certainty equivalence (CE) assumption is needed. 1 Introduction Robotic systems often have nonlinearities in the actuator such as deadzone, backlash, saturation, etc. This includes xypositioning tables, robot manipulators, ove...