Direct Adaptive and Neural Control of Wing-Rock Motion of Slender Delta Wings

Singh, Sahjendra N.; Yirn, Woosoon; Wells, W. R.

doi:10.2514/3.56652

Cited by 138 publications

(88 citation statements)

References 9 publications

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“…In this section the GP-MRAC approach is compared with fixed-center RBFN-MRAC in numerical simulations. Note that the goal is not to present a highly tuned adaptive controller for wing rock, several other authors have already done that assuming known operating domain or known bases of ∆(x) [13], [23], [36], [56], [62]. Rather, the goal is to test the performance when the assumption of known operating domain (needed by fixedcenter RBFN-MRAC to pre-allocate centers) or known bases of uncertainty are violated, forcing the controller to adapt to unknown operating conditions.…”

Section: Trajectory Tracking In Presence Of Wing Rock Dynamics In mentioning

confidence: 99%

Bayesian Nonparametric Adaptive Control Using Gaussian Processes

Chowdhary

Kingravi

How

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

143

160

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Abstract-Most current Model Reference Adaptive Control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element are Radial Basis Function Networks (RBFNs), with RBF centers pre-allocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become non-effective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semi-global in nature. This paper investigates a Gaussian Process (GP) based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.

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Section: Trajectory Tracking In Presence Of Wing Rock Dynamics In mentioning

confidence: 99%

Bayesian Nonparametric Adaptive Control Using Gaussian Processes

Chowdhary

Kingravi

How

et al. 2015

IEEE Trans. Neural Netw. Learning Syst.

143

160

View full text Add to dashboard Cite

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“…; 5 are nonlinear functions of the angle of attack. The aerodynamic parameters for 25°angle of attack are used for simulations, the parameters are given by b 0 = 0, b 1 ¼ À0:01859521; b 2 ¼ 0:015162375; b 3 ¼ À0:06245153; b 4 ¼ 0:00954708 and b 5 ¼ 0:02145291 [11]. The open-loop system time response with u = 0 was simulated for two initial conditions: initial condition 1 (u ¼ 6 ; _ u ¼ 3 Á sec À1 ) and initial condition 2 (u ¼ 30 ; _ u ¼ 10 Á sec À1 ).…”

Section: Simulation Resultsmentioning

confidence: 99%

“…These aircrafts may become unstable due to oscillation in a rolling motion known as wing rock motion [2,11]. A dynamic system of the wing rock motion system can be written in a state variable form as…”

Section: Simulation Resultsmentioning

confidence: 99%

Neural-network-based robust adaptive control for a class of nonlinear systems

Lin

Ting

et al. 2011

Neural Comput & Applic

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In this study, a robust adaptive control (RAC) system is developed for a class of nonlinear systems. The RAC system is comprised of a computation controller and a robust compensator. The computation controller containing a radial basis function (RBF) neural network is the principal controller, and the robust compensator can provide the smooth and chattering-free stability compensation. The RBF neural network is used to approximate the system dynamics, and the adaptive laws are derived to on-line tune the parameters of the neural network so as to achieve favorable estimation performance. From the Lyapunov stability analysis, it is shown that all signals in the closedloop RBAC system are uniformly ultimately bounded. To investigate the effectiveness of the RAC system, the design methodology is applied to control two nonlinear systems: a wing rock motion system and a Chua's chaotic circuit system. Simulation results demonstrate that the proposed RAC system can achieve favorable tracking performance with unknown of the system dynamics.

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“…For the simulation we consider a wing rock motion of a slender delta wing given by the equation [14] …”

Section: Simulation Resultsmentioning

confidence: 99%

M-MRAC for nonlinear systems with bounded disturbances

Stepanyan

Krishnakumar

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-This paper presents design and performance analysis of a modified reference model MRAC (M-MRAC) architecture for a class of multi-input multi-output uncertain nonlinear systems in the presence of bounded disturbances. M-MRAC incorporates an error feedback in the reference model definition, which allows for fast adaptation without generating high frequency oscillations in the control signal, which closely follows the certainty equivalent control signal. The benefits of the method are demonstrated via a simulation example of an aircraft's wing rock motion.

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Direct Adaptive and Neural Control of Wing-Rock Motion of Slender Delta Wings

Cited by 138 publications

References 9 publications

Bayesian Nonparametric Adaptive Control Using Gaussian Processes

Bayesian Nonparametric Adaptive Control Using Gaussian Processes

Neural-network-based robust adaptive control for a class of nonlinear systems

M-MRAC for nonlinear systems with bounded disturbances

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