Adaptive Augmentation of a New Baseline Control Architecture for Tail-Controlled Missiles Using a Nonlinear Reference Model

Peter, Florian; Leitão, Miguel; Holzapfel, Florian

doi:10.2514/6.2012-5037

Cited by 19 publications

(9 citation statements)

References 7 publications

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“…The main incentive of using L 1 adaptive control is its ability to cope with unmatched and matched uncertainties as well as the possibility of estimating uncertainties and determining the control effort to minimize the influence of these uncertainties on the system closed-loop response. The low-pass filters, introduced in this method, even in the presence of fast adaptation and large amplitude reference inputs, guarantees that the closed-loop response of L 1 adaptive control output remains in the lowfrequency range 21,22 . The primary results on L 1 adaptive control system design are reported in the seminal work in 23 .…”

Section: Introductionmentioning

confidence: 99%

A fast L1 adaptive constrained back-stepping controller using barrier Lyapunov function for anuncertain submersible vehicle

Ahmadian

Arefi

Khayatian

et al. 2021

Preprint

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In this paper, a new L1 adaptive back-stepping controller based on the barrier Lyapunov function (BLF) is proposed to respect the position and velocity constraints usually imposed in designing Euler-Lagrange systems. The purpose of this investigation is to improve different aspects of a conventional L1 adaptive control. More specifically, the modified controller has a lower complexity by removing the low-pass filter from the design procedure. The performance of the controller is also enhanced by having a faster convergence speed and increased robustness against nonlinear uncertainties and disturbances arising in practical applications. The proposed scheme is evaluated on two different Euler-Lagrange systems, i.e. a 6-DOF remotely operated vehicle (ROV) and a single-link manipulator. The results for the new back-stepping design are assessed in both scenarios in terms of settling time, percentage of overshoot, and trajectory tracking error. The results confirm that both tracking and state estimation errors for position and velocity outputs outperform the standard L1 adaptive control technique. The results also demonstrate the high performance of the proposed approach in removing the matched nonlinear time-varying disturbances and dynamic uncertainties and a good trajectory tracking despite the uncertainty on the input gain of the system.

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Section: Introductionmentioning

confidence: 99%

A fast L1 adaptive constrained back-stepping controller using barrier Lyapunov function for anuncertain submersible vehicle

Ahmadian

Arefi

Khayatian

et al. 2021

Preprint

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“…launch vehicles. Notable contributions to the adaptive control literature using nonlinear reference systems are documented by the authors of [7][8][9][10][11][12][13]. In particular, [7] proposes an adaptive control algorithm for scalar nonlinear systems based on a nonlinear reference system, while [8] extends this result for a general class of uncertain nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Safety-Critical Adaptive Control with Nonlinear Reference Model Systems

Arabi

Garg

Panagou

2020

2020 American Control Conference (ACC)

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In this paper, a model reference adaptive control architecture is proposed for uncertain nonlinear systems to achieve prescribed performance guarantees. Specifically, a general nonlinear reference model system is considered that captures an ideal and safe system behavior. An adaptive control architecture is then proposed to suppress the effects of system uncertainties without any prior knowledge of their magnitude and rate upper bounds. More importantly, the proposed control architecture enforces the system state trajectories to evolve within a user-specified prescribed distance from the reference system trajectories, satisfying the safety constraints. This eliminates the ad-hoc tuning process for the adaptation rate that is conventionally required in model reference adaptive control to ensure safety. The efficacy of the proposed control architecture is also demonstrated through an illustrative numerical example.

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“…This model features a nonlinear dynamic inversion (NDI) feedforward-feedback controller approach [2] with nonlinear reference models to control the bodies [3]. Basically, the NDI approach will provide an interface between the reference models for the movement that are based on the muscle dynamics of each body and the overall dynamics of the MBS.…”

Section: Introductionmentioning

confidence: 99%

Multi-Body Ski Jumper Model with Nonlinear Dynamic Inversion Muscle Control for Trajectory Optimization

Piprek

Glas

Fang

et al. 2018

The 12th Conference of the International Sports Engineering Association

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This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.

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Adaptive Augmentation of a New Baseline Control Architecture for Tail-Controlled Missiles Using a Nonlinear Reference Model

Cited by 19 publications

References 7 publications

A fast L1 adaptive constrained back-stepping controller using barrier Lyapunov function for anuncertain submersible vehicle

A fast L1 adaptive constrained back-stepping controller using barrier Lyapunov function for anuncertain submersible vehicle

Safety-Critical Adaptive Control with Nonlinear Reference Model Systems

Multi-Body Ski Jumper Model with Nonlinear Dynamic Inversion Muscle Control for Trajectory Optimization

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