State-Dependent Riccati Equation Control of a Small Unmanned Helicopter

Богданов, А. А.; Carlsson, Magnus; Harvey, G. F.; Hunt, John L.; Kieburtz, Dick; Merwe, Rudolph van der; Wan, Eric A.

doi:10.2514/6.2003-5672

Cited by 37 publications

(22 citation statements)

References 1 publication

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“…This leads to a growing interest in applying nonlinear control methodology into the control of helicopters. Feedback linearization and state-dependent Riccati equation method [1,5], for example, were used to handle the nonlinear dynamics of a helicopter by online linearization and optimization. Backstepping and predictive control approaches have also been proposed for the control of helicopters [3,6,7], but the implementation of these methods is constrained due to the computational complexity and the lack of robustness [8].…”

Section: Doi: 102514/145659mentioning

confidence: 99%

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Acceleration-Feedback-Enhanced Robust Control of an Unmanned Helicopter

2010

Journal of Guidance, Control, and Dynamics

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While a few proposed control strategies have shown their acceptable effectiveness, performance improvement on stability and robustness of unmanned helicopters are still imperative and a great challenge due to strong nonlinearities, extensive parameter uncertainties and external disturbances when the flight condition is terrible, such as flight on a windy day. Because acceleration feedback control is advantageous in terms of simple controller structure and easy implementation, we attempt to incorporate it into the tracking control of an unmanned helicopter that is highly nonlinear and underactuated. In this paper, we use a prefilter to formulate a new acceleration feedback control and then use it as a robust enhancement for the H 1 algorithm to attenuate uncertainties and external disturbances involved in the tracking control of an unmanned helicopter. We conduct simulations with an unmanned model helicopter and compare the tracking performance of the helicopter with and without acceleration feedback control. The results show that the use of acceleration feedback control does enhance tracking performance greatly compared to the standard H 1 control.

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Section: Doi: 102514/145659mentioning

confidence: 99%

“…(4)(5)(6)(7)(8)(9)(10)] is still too complicated to be directly used in the design of the controller. In the following, the model is further simplified such that it can be feedback-linearized and a tracking controller can be then designed.…”

Section: Simplified Helicopter Dynamics and Feedback Linearizationmentioning

confidence: 99%

Acceleration-Feedback-Enhanced Robust Control of an Unmanned Helicopter

2010

Journal of Guidance, Control, and Dynamics

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“…Some previous studies on control design for modelscaled autonomous helicopters often consider extensively the rotor flapping dynamics [5][6][7][8][9]. States of the rotor dynamics (namely the coning and flapping angles) are regarded as part of the system states, adding at least two (sometimes four) dimensions to the overall mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Approximate analysis for main rotor flapping dynamics of a model-scaled helicopter with Bell–Hiller stabilizing bar in hovering and vertical flights

Zhu

2016

Nonlinear Dyn

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In this paper, the effects of the rotor flapping dynamics in the control design for the modelscaled autonomous helicopter are analyzed in detail. The analysis is based on a specific model-scaled helicopter equipped with a Bell-Hiller stabilizing bar. A simplified analytic model for the flapping dynamics in hovering flight mode is derived and then expanded to the vertical flight mode. Eigenvalues of the approximated linear system indicate that flapping dynamics in both flight modes is comparatively fast and asymptotically stable. Effects of the rotor dynamics with Bell-Hiller stabilizing bar are assessed with both simulation and experimental results, indicating that static approximation of the rotor dynamics in control design is acceptable.

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“…Thus, system Equation (9) driven by control Equation (40) subject to constraints Equation (21) is stable. Note, the stability of such platforms with coupled fast and slow dynamics has been studied extensively by other researchers, and some notable techniques, such as singular perturbation control can be found in [21,22].…”

Section: Stability Of Constrained Linear Mpcmentioning

confidence: 99%

“…The essential idea is to utilize the state-dependent coefficient (SDC) factorization [39] of the nonlinear dynamics. A state space representation of the quadrotor is obtained, where each of its system matrices are now expressed as functions of the current state [40]. Consider a dynamic system as in Equation (8).…”

Section: Nonlinear Model Predictive Control Formulationmentioning

confidence: 99%

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

Subbarao

2017

Aerospace

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This paper discusses the derivation and implementation of a nonlinear model predictive control law for tracking reference trajectories and constrained control of a quadrotor platform. The approach uses the state-dependent coefficient form to capture the system nonlinearities into a pseudo-linear system matrix. The state-dependent coefficient form is derived following a rigorous analysis of aerial vehicle dynamics that systematically accounts for the peculiarities of such systems. The same state-dependent coefficient form is exploited for obtaining a nonlinear equivalent of the model predictive control. The nonlinear model predictive control law is derived by first transforming the continuous system into a sampled-data form and and then using a sequential quadratic programming solver while accounting for input, output and state constraints. The boundedness of the tracking errors using the sampled-data implementation is shown explicitly. The performance of the nonlinear controller is illustrated through representative simulations showing the tracking of several aggressive reference trajectories with and without disturbances.

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State-Dependent Riccati Equation Control of a Small Unmanned Helicopter

Cited by 37 publications

References 1 publication

Acceleration-Feedback-Enhanced Robust Control of an Unmanned Helicopter

Acceleration-Feedback-Enhanced Robust Control of an Unmanned Helicopter

Approximate analysis for main rotor flapping dynamics of a model-scaled helicopter with Bell–Hiller stabilizing bar in hovering and vertical flights

Nonlinear Model Predictive Control for Unmanned Aerial Vehicles

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