Trajectory-Driven Adaptive Control of Autonomous Unmanned Aerial Vehicles with Disturbance Accommodation

Prabhakar, Nirmit; Painter, Andrew; Prazenica, Richard J.; Balas, Mark J.

doi:10.2514/1.g003341

Cited by 22 publications

(19 citation statements)

References 34 publications

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“…Recent work in the area of adaptive guidance algorithms include [2], which demonstrates an adaptive control law for a UAV tracking a reference trajectory, where the adaptive controller adapts to external disturbances. One limitation is the linear dynamics model, which may not be accurate, as well as the fact that the frequency of the disturbance must be known.…”

Section: Introductionmentioning

confidence: 99%

Adaptive guidance and integrated navigation with reinforcement meta-learning

2020

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This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt real time to environmental forces acting on the agent. We compare the performance of the DR/DV guidance law, an RL agent with a non-recurrent policy, and an RL agent with a recurrent policy in four challenging environments with unknown but highly variable dynamics. These tasks include a safe Mars landing with random engine failure and a landing on an asteroid with unknown environmental dynamics. We also demonstrate the ability of a RL meta-learning optimized policy to implement a guidance law using observations consisting of only Doppler radar altimeter readings in a Mars landing environment, and LI-DAR altimeter readings in an asteroid landing environment, thus integrating guidance and navigation.

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Section: Introductionmentioning

confidence: 99%

Adaptive guidance and integrated navigation with reinforcement meta-learning

2020

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“…(8) and so λC(s, p) = C(λs, λ −1 • p) in Eq. (9). Now, we will proof that the same properties hold for Eq.…”

Section: Appendix A: Proof Of Lemma Iii1mentioning

confidence: 61%

“…In this sense, heading and attitude control in UAVs is very important [4], particularly relevant in airplanes (fixed-wing flying vehicles), because they are strongly non-linear, coupled, and tend to be underactuated systems with non-holonomic constraints. Hence, designing a good attitude controller is a difficult task [5,6,7,8,9], where stability must be taken into account by the controller [10]. Indeed, if the reference is too demanding for the controller or non-achievable because its dynamics is too fast, the vehicle might become unstable.…”

Section: Introductionmentioning

confidence: 99%

Clothoid-Based Three-Dimensional Curve for Attitude Planning

Girbés

Vanegas

Armesto

2019

Journal of Guidance, Control, and Dynamics

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“…H ∞ as a lower bound in (11). The above improvement is straightforward but is first provided in the present paper.…”

Section: Preliminaries For Computing the L∞-induced Normmentioning

confidence: 90%

“…Furthermore, these norms do not correspond to dealing with bounded persistent disturbances, which are often encountered in real control systems because of unknown external elements such as unexpected environmental changes. In connection with this, practical issues for bounded persistent disturbances have been extensively discussed in various fields, such as robot manipulators [8], humanoids [9], aerospace systems [10], [11], model predictive control [12], Markovian jump systems [13], networked control systems [14], and so on. When we are in a position to consider the effect of bounded persistent disturbances, taking the L ∞ norm is quite appropriate because the L ∞ norm of a signal corresponds to its maximum amplitude and the L ∞ -induced norm of a system corresponds to the worst maximum magnitude of the output for bounded persistent disturbances with a unit magnitude.…”

Section: Introductionmentioning

confidence: 99%

Computing the L∞-Induced Norm of LTI Systems: Generalization of Piecewise Quadratic and Cubic Approximations

Choi

Hagiwara

2020

IEEE Access

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This paper is concerned with performance analysis for bounded persistent disturbances of continuous-time linear time-invariant (LTI) systems. Such an analysis can be done by computing the L ∞induced norm of continuous-time LTI systems since it corresponds to the worst maximum magnitude of the output for the worst persistent external input with a unit magnitude. In our preceding study, piecewise constant and linear approximation schemes for analyzing this norm have been developed through two alternative approximation approaches, one for the input and the other for the relevant kernel function, via the fast-lifting technique. The approximation errors in these approximation schemes have been shown to converge to 0 at the rates of 1/N and 1/N 2 , respectively, as the fast-lifting parameter N is increased. Along this line, this paper aims at developing generalized techniques that offer improved accuracy named the piecewise quadratic and cubic approximation schemes. The generalization and the associated accuracy improvement discussed in this paper are not limited to the increased orders of approximation but are extended further to taking advantage of the freedom in the point around which relevant functions are expanded to Taylor series. The approximation errors in the piecewise quadratic and cubic approximation schemes are shown to converge to 0 at the rates of 1/N 3 and 1/N 4 , respectively, regardless of the point at which the Taylor expansion is applied. Finally, effectiveness of the developed computation methods is confirmed through a numerical example. INDEX TERMS Approximate computing, approximation methods, linear systems, performance analysis, robustness.

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Trajectory-Driven Adaptive Control of Autonomous Unmanned Aerial Vehicles with Disturbance Accommodation

Cited by 22 publications

References 34 publications

Adaptive guidance and integrated navigation with reinforcement meta-learning

Adaptive guidance and integrated navigation with reinforcement meta-learning

Clothoid-Based Three-Dimensional Curve for Attitude Planning

Computing the L∞-Induced Norm of LTI Systems: Generalization of Piecewise Quadratic and Cubic Approximations

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