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ABSTRACT: This paper presents a derivative-free decentralized adaptive control architecture for large-scale interconnected systems with matched and unmatched time-varying uncertainties, interconnections and matched disturbances. The assumption of un-known constant ideal weights is generalized to the existence of time-varying weights without assuming the existence of their derivatives in a time interval. As a result, the proposed approach is particularly well suited for disturbance rejection, and for adaptation in the presence of sudden change in each subsystem's uncertain dynam-ics, such as might be due to, for example, deployment of, configuration changes in, docking and/or damage to, a large space structure. Boundedness of the er-ror signals is shown using a Lyapunov-Krasovskii functional without the need for modification terms in the adaptive law. Several small-scale examples are used to illustrate the main properties of the adaptive controller.
Journal of the Astronautical Sciences 01/2012; · 0.29 Impact Factor
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ABSTRACT: In this paper, a new modification term for use in adaptive control is developed to improve robustness of an existing design. The objective is to recover the loop transfer properties of a reference model associated with a nonadaptive control design. Consequently, this term can increase the level of confidence of an adaptive control system for purposes of increased flight safety. An illustrative example on an unmanned combat aerial vehicle model is provided to illustrate the efficacy of the proposed approach.
Journal of Guidance, Control, and Dynamics 01/2012;
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ABSTRACT: In this paper, we develop an output feedback adaptive control framework for continuous-time nonminimum phase multivariable systems for output stabilization, command following, and disturbance rejection. The approach is based on a nonminimal state space realization that generates an expanded set of states using the filtered inputs and filtered outputs and their derivatives of the original system. Specifically, a direct adaptive controller for the nonminimal state space model is constructed using the expanded states of the nonminimal realization and is shown to be effective for multi-input, multi-output nonminimum phase systems with unstable dynamics. The adaptive controller does not require any model information nor does it require information of the system poles and system zeros or estimation of the system Markov parameters. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach. Copyright © 2010 John Wiley & Sons, Ltd.
International Journal of Adaptive Control and Signal Processing 03/2011; 25(4):352 - 373. · 0.91 Impact Factor
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ABSTRACT: This paper illustrates an application of derivative-free, output feedback adaptive control on an aeroelastic model of longitudinal dynamics for a generic transport model. The controller uses a state observer as a reference model, and has a derivative-free delayed weight update law. Since it does not assume the existence of constant ideal weights, it is particulary well suited for adaptation to sudden changes in system dynamics, such as might be due to reconfiguration, deployment of a payload, docking, structural damage, or to difficult to model external disturbances. In addition, it is applicable to output feedback adaptive control design for non-minimum phase plants.
AIAA Guidance, Navigation, and Control Conference; 01/2011
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ABSTRACT: In this paper we develop a Kalman filter based adaptive controller for multivariable uncertain systems with loop transfer recovery of an associated reference system. This approach increases the level of confidence of adaptive control systems by providing a means for preserving stability margins even under uncertainty and failures. In addition, it results in an optimization based time-varying adaptation gain. An example is provided to illustrate the efficacy of the proposed approach.
American Control Conference; 01/2011
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ABSTRACT: This paper presents an output feedback adaptive control architecture for continuous-time uncertain dynamical systems based on state observer and derivative-free delayed weight update law. The assumption of constant unknown ideal weights is generalized to the existence of time-varying weights without assuming the existence of their derivatives in a time interval. As a result, this approach is particularly well suited for adaptation in the presence of sudden change in uncertain system dynamics, such as might be due to reconfiguration, deployment of a payload, docking, structural damage, or there is a difficult to model disturbance. Using a Lyapunov-Krasovskii functional, it is proven that the error dynamics are uniformly ultimately bounded, without the need for modification terms in the adaptive law. The complexity of the proposed approach is less than many other output feedback adaptive control architectures available in the literature and it can be used to augment an existing state observer based linear controller.
AIAA Guidance, Navigation, and Control Conference; 01/2011
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ABSTRACT: In this paper, we present both simulated and flight test results of a derivative-free model reference adaptive controller applied to a generic transport model, a high-fidelity transport aircraft model developed by NASA Langley Research Center. The derivative free form of adaptive control is expected to provide faster adaptation and smoother error transients, particularly for situations where aircraft dynamics undergo a sudden change. Moreover, it does not assume the existence of constant ideal weights, and does not require modification terms like σ-or e-modification, or a projection operator to guarantee that the error signals are uniformly ultimately bounded.
AIAA Guidance, Navigation, and Control Conference; 01/2010
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ABSTRACT: This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving time window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress and cancel system uncertainty without the need for persistency of excitation. A nonlinear parametrization of the system uncertainty is considered and state and output feedback neuroadaptive controllers are developed. To illustrate the efficacy of the proposed approach we apply our results to a spacecraft model with unknown moment of inertia and compare our results with standard neuroadaptive control methods.
IEEE Transactions on Neural Networks 09/2009; 20(11):1707-23. · 2.95 Impact Factor
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ABSTRACT: This paper presents a new modification term for use in adaptive control to improve an already existing design. By employing this term in a conventional adaptive law, the loop transfer properties of a reference model associated with a non-adaptive control design can be preserved. Consequently, this term increases the level of confidence of adaptive flight control systems for purposes of increased flight safety. The results are illustrated on an unmanned combat aerial vehicle dynamic model.
AIAA Guidance, Navigation, and Control Conference; 01/2009
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ABSTRACT: This paper presents a neuroadaptive full-state feedback controller design for the Boeing unmanned combat aerial vehicle (UCAV), and a neuroadaptive output feedback controller design for a damped van der Pol oscillator model. The proposed neuroadaptive controllers employ a novel controller architecture involving additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty and actuator system failures.
AIAA Guidance, Navigation, and Control Conference; 01/2009
