Adaptive nonlinear controller synthesis and flight test evaluation on an unmanned helicopter

Prasad, J. V. R.; Calise, Anthony J.; Yao, Pei; Corban, J. E.

doi:10.1109/cca.1999.806163

Cited by 56 publications

(23 citation statements)

References 4 publications

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“…A bounded tracking control of RUAV using backstepping is proposed in [29], which also considers the flapping angles as control inputs [30]. For a selective literature review, relating to the nonlinear control design techniques, see: sliding mode [31], backstepping [32][33][34], neural-network-based controller [35], fuzzy control [36] and nonlinear H ∞ control [37].…”

Section: Introductionmentioning

confidence: 99%

Flight control of a rotary wing UAV using backstepping

Ahmed

Pota

Garratt

2009

Int. J. Robust Nonlinear Control

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SUMMARYThis paper presents a novel application of backstepping controller for autonomous landing of a rotary wing UAV (RUAV). This application, which holds good for the full flight envelope control, is an extension of a backstepping algorithm for general rigid body velocity control. The nonlinear RUAV model used in this paper includes the flapping and servo dynamics. The backstepping-based controller takes advantage of the 'decoupling' of the translation and rotation dynamics of the rigid body, resulting in a two-step procedure to obtain the RUAV control inputs. The first step is to compute desired thrusts and flapping angles to achieve the commanded position and the second step is to compute control inputs, which achieve the desired thrusts and flapping angles. This paper presents a detailed analysis of the inclusion of a flapping angle correction term in control. The performance of the proposed algorithm is tested using a high-fidelity RUAV simulation model. The RUAV simulation model is based on miniature rotorcraft parameters. The closed-loop response of the rotorcraft indicates that the desired position is achieved after a short transient. The Eagle RUAV control inputs, obtained using high-fidelity simulation results, clearly demonstrate that this algorithm can be implemented on practical RUAVs.

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Section: Introductionmentioning

confidence: 99%

Flight control of a rotary wing UAV using backstepping

Ahmed

Pota

Garratt

2009

Int. J. Robust Nonlinear Control

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“…Also, Koo et al (1998) used dynamical sliding mode control to stabilize the altitude of a nonlinear helicopter model in vertical flights. Later, neural network based inverse control of an aircraft system was presented in (Prasad et al, 1999). More MIMO control approaches for helicopter maneuver are presented in (SiraRamirez et al, 1994;Mahony et al, 2004;Marconi et al, 2007, Tao et al, 2010.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control of a 3-DOF helicopter system using successive linearization

Zhai¹,

Nounou²,

Nounou³

et al. 2011

Int. J. Eng. Sci. Tech

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Helicopter dynamics are in general nonlinear, time-varying, and may be highly uncertain. Traditional control schemes such as proportional-integral-derivative (PID) control, linear quadratic regulator (LQR), and eigen-structure assignment are usually not effective when a linearized model is used and the helicopter moves away from the design trim point. This paper presents a nonlinear model predictive control (NMPC) method to control the elevation and travel of a three degree of freedom (DOF) laboratory helicopter using successive linearization to approximate the internal model of the system. The developed algorithm is evaluated by simulation, and its performance is compared with that achieved by linear model predictive control (LMPC).

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“…The literature has several examples for this, which mainly solve only the stabilization of the helicopter, or pilot augmentation [3-5, 7, 8]. Spatial trajectory tracking controllers are rarely designed, and they are sophisticated nonlinear, adaptive ones: [6]. However, motion simulations showed that ( [14,15]) with slowly varying reference signal the quadrotor does not leave the linear region considered around hovering mode.…”

Section: Introductionmentioning

confidence: 99%

LQ Servo control design with Kalman filter for a quadrotor UAV

Bauer¹,

Ritzinger

Soumelidis

et al. 2008

Per. Pol. Transp. Eng.

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This paper deals with reference signal tracking control of a quadrotor UAV (Unmanned Aerial Vehicle).Before controller design, a nonlinear simulation model is needed, to be the base of design and the first testbed for the resulted controllers. The next step should be the linearization of the nonlinear model in hovering mode, and the reduction of the resulted linear model. The reduced linear model is controllable and observable.The control goal was to track a spatial trajectory with the helicopter center of gravity. For this purpose, an LQ Servo controller (with double integrator) was designed, augmented with a Kalman filter state observer.The resultant controller provided good tracking performance for a slowly varying reference signal, also on the nonlinear model! After the transient response, the tracking error was below 1 cm which provides safe handling even in indoor applications. The time of transients was approximately 4 seconds which is acceptable.

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Adaptive nonlinear controller synthesis and flight test evaluation on an unmanned helicopter

Cited by 56 publications

References 4 publications

Flight control of a rotary wing UAV using backstepping

Flight control of a rotary wing UAV using backstepping

Model predictive control of a 3-DOF helicopter system using successive linearization

LQ Servo control design with Kalman filter for a quadrotor UAV

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