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81
Adaptive Output Feedback Control of Uncertain Systems using Single Hidden Layer Neural Networks
 IEEE Transactions on Neural Networks
, 2002
"... We consider adaptive output feedback control of uncertain nonlinear sy[q6M3 in which both the dy6Mq1g and the dimension of the regulated plant may be unknown.Only knowledge of relative degree is assumed. Given a smooth reference trajectory , the problem is to design a controller that forces the syM9 ..."
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Cited by 43 (14 self)
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We consider adaptive output feedback control of uncertain nonlinear sy[q6M3 in which both the dy6Mq1g and the dimension of the regulated plant may be unknown.Only knowledge of relative degree is assumed. Given a smooth reference trajectory , the problem is to design a controller that forces the syM9q measurement to track it with bounded errors. The classical approach necessitates buildinga state observer. However, findinga good observer for an uncertain nonlinear syM[9 is not an obvious task. We argue that it should be su#cient to build an observer for the output trackingerror. Ultimate boundedness of the error signals is shown through Ly apunov like stability analyqqg The method is illustrated in the design of a controller for a fourth order nonlinear syM99 of relative degree 2 and a highbandwidth attitude command symma for a model R50 helicopter. 1 Introdu88/ Research in adaptive output feedback control of uncertain nonlinear sy[q#6 is motivated by the many emerging applications that employ novel actuation devices for active control of flexible structures, fluid flows and combustion processes. These include such devices as piezo electric films, andsy thetic jets, which are ty pically nonlinearly coupled to the dy[M11q of the processes they are intended to control. Modelingfor these applications vary from havingaccurate low frequency models in the case of structural control problems, to havingno reasonable set of model equations in the case of active control of flows and combustion processes. Regardless of the extent of the model accuracy that may be present, an important aspect in any control design is the e#ect of parametric uncertainty and unmodeleddydg#q69 While it can be said the issue of parametric uncertainty is addressed within the context of adaptive cont...
Adaptive neural network control of nonlinear systems by stable output feedback
 IEEE Trans. Syst., Man, Cybern. B
, 1999
"... Abstract—This paper presents a novel control method for a general class of nonlinear systems using neural networks (NN’s). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the o ..."
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Cited by 28 (6 self)
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Abstract—This paper presents a novel control method for a general class of nonlinear systems using neural networks (NN’s). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a highgain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closedloop system is proven to be semiglobally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions. Index Terms — Adaptive control, highgain observer, neural networks, nonlinear system, output feedback control.
Highgain observers in nonlinear feedback control
 INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
, 2013
"... In this document, we present the main ideas and results concerning highgain observers and some of their applications in control. The introduction gives a brief history of the topic. Then, a motivating secondorder example is used to illustrate the key features of highgain observers and their use i ..."
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Cited by 22 (1 self)
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In this document, we present the main ideas and results concerning highgain observers and some of their applications in control. The introduction gives a brief history of the topic. Then, a motivating secondorder example is used to illustrate the key features of highgain observers and their use in feedback control. This is followed by a general presentation of highgainobserver theory in a unified framework that accounts for modeling uncertainty, as well as measurement noise. The paper concludes by discussing the use of highgain observers in the robust control of minimumphase nonlinear systems.
An adaptive output feedback control methodology for nonminimum phase systems
 in IEEE Proc. of Conference on Decision and Control
, 2002
"... A method of output feedback design of an adaptive controller is presented that can be used to augment a fixedgain linear controller. The key feature is that it is applicable to nonminimum phase nonlinear systems, having both parametric uncertainty and unmodeled dynamics. Ultimate boundedness of er ..."
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Cited by 20 (6 self)
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A method of output feedback design of an adaptive controller is presented that can be used to augment a fixedgain linear controller. The key feature is that it is applicable to nonminimum phase nonlinear systems, having both parametric uncertainty and unmodeled dynamics. Ultimate boundedness of error signals can be shown using Lyapunov’s direct method. An example is provided to illustrate the effectiveness of the approach. 1
OutputFeedback Stochastic Nonlinear Stabilization
 also in Proceedings of IEEE CDC
, 1997
"... . We present the first result on global outputfeedback stabilization (in probability) for stochastic nonlinear continuoustime systems. The class of systems that we consider is a stochastic counterpart of the broadest class of deterministic systems for which globally stabilizing controllers are cur ..."
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Cited by 18 (3 self)
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. We present the first result on global outputfeedback stabilization (in probability) for stochastic nonlinear continuoustime systems. The class of systems that we consider is a stochastic counterpart of the broadest class of deterministic systems for which globally stabilizing controllers are currently available. Our controllers are "inverse optimal" and possess an infinite gain margin. A reader of the paper needs no prior familiarity with techniques of stochastic control. 1 Introduction Despite huge popularity of the LQG control problem, the stabilization problem for nonlinear stochastic systems has hardly received any attention until recently. Efforts toward (global) stabilization of stochastic nonlinear systems have been initiated in the work of Florchinger [5, 6, 7] who, among other things, extended the concept of control Lyapunov functions and Sontag's stabilization formula [22] to the stochastic setting. A breakthrough towards arriving at constructive methods for stabilizati...
Output feedback adaptive robust control of uncertain linear systems with disturbances
 ASME Journal of Dynamic Systems, Measurement, and Control
, 2006
"... In this paper, a discontinuous projection based output feedback adaptive robust control (ARC) scheme is constructed for a class of linear systems subjected to both parametric uncertainties and disturbances that might be output dependent. An observer is first designed to provide exponentially converg ..."
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Cited by 13 (5 self)
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In this paper, a discontinuous projection based output feedback adaptive robust control (ARC) scheme is constructed for a class of linear systems subjected to both parametric uncertainties and disturbances that might be output dependent. An observer is first designed to provide exponentially convergent estimates of the unmeasured states. This observer has an extended filter structure so that online parameter adaptation can be utilized to reduce the effect of possible large disturbances that have known shapes but unknown amplitudes. Estimation errors that come from initial state estimates and uncompensated disturbances are dealt with via certain robust feedback at each step of the backstepping design. Compared to other existing output feedback robust adaptive control schemes, the proposed method explicitly takes into account the effect of disturbances and uses both parameter adaptation and robust feedback to attenuate their effects for an improved tracking performance. Experimental results on the control of an iron core linear motor are presented to illustrate the effectiveness and achievable performance of the proposed scheme. �DOI: 10.1115/1.2363413�
Adaptive Output Feedback for HighBandwidth Control of an Unmanned Helicopter
 PROCEEDINGS OF THE AIAA GUIDANCE, NAVIGATION, AND CONTROL CONFERENCE
, 2001
"... We consider asider e output feedback neurocontrol of uncertain nonlinear systems, and in particular its asfi::C2fiJ to highbaJ=:=(F flight control of unma fi: rotorcrafiJ Givena smooth referencetra jectory, the problem is to designa controller tha would force the systemmeamfiFC t to tra k ita:=F: ..."
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Cited by 12 (1 self)
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We consider asider e output feedback neurocontrol of uncertain nonlinear systems, and in particular its asfi::C2fiJ to highbaJ=:=(F flight control of unma fi: rotorcrafiJ Givena smooth referencetra jectory, the problem is to designa controller tha would force the systemmeamfiFC t to tra k ita:=F::fiJ cafia or with bounded errors. TheclaCCfi a pproa h necessita+) building sta( observers. The sta( estimati a re used both in the controller designas in the afi::C+fiJ la ws. However, findinga good observer for a uncertaF nonlinea pla t is not a obvious taus Wea+F2 tha it should be su#cient to builda observer for the outputtra king error only. The method is employed in the design ofa highbaJ:CC ahba commab system for an unmanned helicopter.
Learning composite adaptive control for a class of nonlinear systems
 In IEEE International Conference on Robotics and Automation
, 2004
"... Abstract — In our recent work, an adaptive composite control technique was suggested for nonlinear adaptive control with statistical learning methods. While this original work was restricted to a simple class of nonlinear SISO systems that were linear in the inputs, in this paper we present a more g ..."
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Cited by 9 (4 self)
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Abstract — In our recent work, an adaptive composite control technique was suggested for nonlinear adaptive control with statistical learning methods. While this original work was restricted to a simple class of nonlinear SISO systems that were linear in the inputs, in this paper we present a more general treatment of learning a composite controller for the class of nonlinear systems characterized by the form ˙x = f(x)+g(x)u. We will first examine such systems in the first order SISO framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Second, we generalize our adaptive controller to higher order SISO systems, and discuss the application to MIMO problems. We evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control. I.
Adaptive Spacecraft Attitude Tracking Control with Actuator Uncertainties1
"... An adaptive control algorithm for the spacecraft attitude tracking problem when the spin axis directions and/or the gains of the flywheel actuators are uncertain is developed. A smooth projection algorithm is applied to keep the parameter estimates inside a singularityfree region and avoid paramete ..."
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Cited by 9 (4 self)
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An adaptive control algorithm for the spacecraft attitude tracking problem when the spin axis directions and/or the gains of the flywheel actuators are uncertain is developed. A smooth projection algorithm is applied to keep the parameter estimates inside a singularityfree region and avoid parameter bursting. Numerical examples show that the controller successfully deals with unknown misalignments of the axis directions as well as the unknown gains of the flywheel actuators.