Eugene Lavretsky
Massachusetts Institute of Technology
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Featured researches published by Eugene Lavretsky.
IEEE Transactions on Control Systems and Technology | 2013
Zachary T. Dydek; Anuradha M. Annaswamy; Eugene Lavretsky
This brief describes the application of direct and indirect model reference adaptive control to a lightweight low-cost quadrotor unmanned aerial vehicle platform. A baseline trajectory tracking controller is augmented by an adaptive controller. The approach is validated using simulations and flight tested in an indoor test facility. The adaptive controller is found to offer increased robustness to parametric uncertainties. In particular, it is found to be effective in mitigating the effects of a loss-of-thrust anomaly, which may occur due to component failure or physical damage. The design of the adaptive controller is presented, followed by a comparison of flight test results using the existing linear and augmented adaptive controllers.
IEEE Transactions on Automatic Control | 2009
Eugene Lavretsky
This technical note introduces a provably stable state-feedback design modification for combined/composite adaptive control of multi-input multi-output dynamical systems with matched uncertainties. The proposed design methodology is applied to control longitudinal dynamics of an aerial vehicle.
Archive | 2013
Eugene Lavretsky; Kevin A. Wise
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IEEE Transactions on Automatic Control | 2003
Eugene Lavretsky; Naira Hovakimyan; Anthony J. Calise
price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. E. Lavretsky, K. Wise Robust and Adaptive Control
american control conference | 2006
Kevin A. Wise; Eugene Lavretsky; Naira Hovakimyan
The problem of approximation of unknown dynamics of a continuous-time observable nonlinear system is considered using a feedforward neural network, operating over delayed sampled outputs of the system. Error bounds are derived that explicitly depend upon the sampling time interval and network architecture. The main result of this note broadens the class of nonlinear dynamical systems for which adaptive output feedback control and state estimation problems are solvable.
IEEE Transactions on Automatic Control | 2012
Eugene Lavretsky
This paper discusses the application of direct adaptive model reference control as it relates to the control of aircraft and weapon systems. Recent extensions in the approach, primarily driven by practical considerations, have led to very successful flight testing. The flight control architectures considered incorporate direct adaptive increments to augment a baseline control signal designed using nonlinear and/or linear robust control methods. Recently applied to a modified guided weapon, this adaptive flight control system was designed and flight tested without wind tunnel measurement of any aerodynamic changes to the modified weapon. This paper discusses the design methodology, practical considerations, extensions incorporated, as well as open problems that remain in making this approach acceptable in the aerospace community
International Journal of Control | 2009
Vijay V. Patel; Chengyu Cao; Naira Hovakimyan; Kevin A. Wise; Eugene Lavretsky
This technical note introduces an observer-based adaptive output feedback tracking control design for multi-input-multi-output dynamical systems with matched uncertainties. The reported methodology exploits asymptotic behavior of LQG/LTR regulators. Sufficient conditions for closed-loop stability and uniform ultimate boundedness of the corresponding tracking error dynamics are formulated. This method is valid for systems whose nominal linearized dynamics are controllable and observable. We assume that the number of the system measured outputs (sensors) is greater than the number of the control inputs (actuators) and that the system output-to-input matrix product has full column rank. In this case, the system can be “squared-up” (i.e., augmented) using pseudo-control signals to yield relative degree one minimum-phase dynamics. Since it is known that the “squaring-up” problem is solvable for any controllable observable triplet (A, B, C), the proposed design is applicable to systems whose regulated output dynamics may be non-minimum phase or have a high relative degree. A simulation example is presented to demonstrate key design features.
Journal of Guidance Control and Dynamics | 2008
Jiang Wang; Vijay V. Patel; Chengyu Cao; Naira Hovakimyan; Eugene Lavretsky
This article considers application of ℒ1 adaptive controller to multi-input multi-output open loop unstable unmanned military aircraft. The control is designed to accommodate and to be robust to actuator failures as well as to pitch break uncertainty, which is used to model uncertain aerodynamics. Results of using the ℒ1 adaptive controller are compared with the conventional model reference adaptive control scheme to show improved transient command tracking as well as guaranteed time-delay margin.
Journal of Guidance Control and Dynamics | 2009
Eugene Lavretsky; Ross Gadient; Irene M. Gregory
Autonomous aerial refueling autopilot design is addressed in this paper using a novel C 1 neural-network-based adaptive control approach, which is capable of accommodating trailing-vortex-induced uncertainties and uncertainties in control effectiveness. The main advantage of the new approach is its ability of fast adaptation that leads to uniform transient performance for the systems signals, both inputs and outputs, simultaneously, with guaranteed performance specifications. Simulation results verify the benefit of this new approach.
IEEE Access | 2013
Travis E. Gibson; Anuradha M. Annaswamy; Eugene Lavretsky
This paper is devoted to robust, Predictor-based Model Reference Adaptive Control (PMRAC) design. The proposed adaptive system is compared with the now-classical Model Reference Adaptive Control (MRAC) architecture. Simulation examples are presented. Numerical evidence indicates that the proposed PMRAC tracking architecture has better than MRAC transient characteristics.