Adaptive planning and tracking of trajectories for the simulation of maneuvers with multibody models

Bottasso, Carlo L.; Chang, Chong-Seok; Croce, Alessandro; Leonello, D.; Riviello, L.

doi:10.1016/j.cma.2005.03.011

Cited by 26 publications

(5 citation statements)

References 15 publications

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“…69 This issue however does not exist with SCP methods, 71 which is one of the reasons why SCP methods have actively being investigated. 69 In addition, SCP methods are known to be very effective and robust, 70 and SCP techniques have been applied to solve nonlinear optimal control problems, such as in space and launch/reentry applications, 61,62,[72][73][74][75][76] in aircraft applications, 61,[77][78][79][80] in helicopter applications, [81][82][83] in UAV applications, [84][85][86] and glider applications. 87 Since SCP methods have been intensively researched in the last decade, we present next a general overview of this concept.…”

Section: Iiib Direct Optimal Control and The Pseudospectral Discretmentioning

confidence: 99%

Identification Of A Nonlinear Grey-Box Helicopter UAV Model

Taamallah

2013

AIAA Atmospheric Flight Mechanics (AFM) Conference

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We present a flight dynamics nonlinear model for a flybarless helicopter UAV, valid for a range of flight conditions, including the Vortex-Ring-State (VRS) and autorotation. To allow for computational efficiency, while maintaining a high-level of model fidelity, a greybox modeling framework has been adopted, in which model uncertainty such as parameter uncertainties, unmodeled higher-order dynamics, and unmodeled static nonlinearities have been replaced by empirical coefficients. The derivation of these coefficients has been based upon a novel identification approach, anchored in the combined paradigms of nonlinear optimal control and neural networks. Preliminary simulation results show that our model is in good agreement with an equivalent FLIGHTLAB model.

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Section: Iiib Direct Optimal Control and The Pseudospectral Discretmentioning

confidence: 99%

Identification Of A Nonlinear Grey-Box Helicopter UAV Model

Taamallah

2013

AIAA Atmospheric Flight Mechanics (AFM) Conference

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“…An interesting alternative is to formulate the steering problem directly as a numerical optimisation problem (Botasso, Chang, Croce, Leonello and Riviello 2006). In this context, Keviczky, Falcone, Borrelli, Asgari, and Hrovat (2006) proposed a Nonlinear Model Predictive Control approach (NMPC), which is a powerful control technique that has found widespread acceptance in industry and systems science (Li and Xi 2011;Wang 2011).…”

Section: Introductionmentioning

confidence: 99%

A new min-max methodology for computing optimised obstacle avoidance steering manoeuvres of ground vehicles

Kanarachos

2012

International Journal of Systems Science

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In this paper, a new methodology for computing optimised obstacle avoidance steering manoeuvres for ground vehicles is presented and discussed. Most of the existing methods formulate the obstacle avoidance problem as an optimal control problem which is hard to solve or as a numerical optimisation problem with a large number of unknowns. This method is based on a reformulation of Pontryagin's Maximum Principle and leads to the solution of an adjustable time optimal controller. The control input is significantly simplified and permits its application in a sample and hold sense. Furthermore, with the proposed approach the maximum tyre forces exerted during the manoeuvre are minimised. In this study, it is shown how to 'warm start' the proposed algorithm and which constraints to 'relax'. Numerical examples and benchmark tests illustrate the performance of the proposed controller and compare it with other standard controllers. A sensitivity analysis for different vehicle parameters is performed and finally conclusions are drawn. A significant advantage of the method is the small computational complexity. The overall simplicity of the controller makes it attractive for application on autonomous vehicles.

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“…Model predictive control (MPC) is popular because it is one of the few control methodologies that can stabilize both linear and nonlinear systems [1]. In the past 20 years, applications of MPC have grown steadily, and MPC is now in widespread use, particularly in the aerospace field [2][3][4][5], the process industry [6][7][8], and mechanical systems [9][10][11]. The popularity of the method is the result of the strong theoretical background that has been developed over the past few years, the development of efficient optimization algorithms and codes, and the substantial increase in computational power [1].…”

Section: Introductionmentioning

confidence: 99%

Fast Model Predictive Control Method for Large-Scale Structural Dynamic Systems: Computational Aspects

Peng

Gao

et al. 2014

Shock and Vibration

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A fast and accurate model predictive control method is presented for dynamic systems representing large-scale structures. The fast model predictive control formulation is based on highly efficient computations of the state transition matrix, that is, the matrix exponential, using an improved precise integration method. The enhanced efficiency for model predictive control is achieved by exploiting the sparse structure of the matrix exponential at each discrete time step. Accuracy is maintained using the precise integration method. Compared with the general model predictive control method, the reduced central processing unit (CPU) time required by the fast model predictive control scheme can result in a shorter control update interval and a lower online computational burden. Therefore, the proposed method is more efficient for large-scale structural dynamic systems.

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Adaptive planning and tracking of trajectories for the simulation of maneuvers with multibody models

Cited by 26 publications

References 15 publications

Identification Of A Nonlinear Grey-Box Helicopter UAV Model

Identification Of A Nonlinear Grey-Box Helicopter UAV Model

A new min-max methodology for computing optimised obstacle avoidance steering manoeuvres of ground vehicles

Fast Model Predictive Control Method for Large-Scale Structural Dynamic Systems: Computational Aspects

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