Suboptimal LQR-based spacecraft full motion control: Theory and experimentation

Guarnaccia, Leone; Bevilacqua, Riccardo; Pastorelli, Stefano Paolo

doi:10.1016/j.actaastro.2016.01.016

Cited by 33 publications

(21 citation statements)

References 27 publications

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“…Saleh and Adeli () present parallel algorithms for solution of the Riccati equation using the multiprocessing capabilities of supercomputers (Adeli & Kamal, ). Using a State‐dependent algebraic Riccati equation and linearization of the equations of motion, Guarnaccia, Bevilacqua, and Pastorelli () propose a suboptimal control algorithm for manoeuvring of a six‐DOF spacecraft by recomputing the control gain of the LQR control at each sample time.…”

Section: Linear Control Algorithmsmentioning

confidence: 99%

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Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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Artificial intelligence and expert system remains a key technology in the 21st century. Using active controllers, a structure can adaptively adjust its behaviour during dynamic loads. Such structures with self‐modifying capabilities are referred to as intelligent or smart structures. Smart structure technology has the potential to be a game changer in the structural engineering field. It promises to have enormous consequences in terms of preventing loss of life and damage to structure and their content especially for large structures with hundreds or thousands of components. A key element in successful implementation of smart active control technology is an effective control algorithm to compute the magnitudes of actual forces to be applied to the structure. In this paper, an overview of main active control methodologies for vibration control of smart civil and mechanical structures subjected to external dynamic loads is presented. The advantages and the disadvantages of different control algorithms are discussed. Finally, new trends in control algorithm research are pointed out including multiparadigm strategies, decentralized control, application of deep neural network machine learning techniques, control design for sustainability, and unification of the two fields of structural health monitoring and vibration control.

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Section: Linear Control Algorithmsmentioning

confidence: 99%

“…Using a State-dependent algebraic Riccati equation and linearization of the equations of motion, Guarnaccia, Bevilacqua, and Pastorelli (2016) propose a suboptimal control algorithm for manoeuvring of a six-DOF spacecraft by recomputing the control gain of the LQR control at each sample time.…”

Section: Linear Quadratic Regulator Controlmentioning

confidence: 99%

Control methodologies for vibration control of smart civil and mechanical structures

Adeli

2018

Expert Systems

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“…To realize the theoretical results in practical applications, the air-bearing ground test facilities have been developed [18,19] and some controllers have been validated by hardware-in-the-loop experiments [20][21][22][23][24][25][26]. For spacecraft operations subject to parametric uncertainties, adaptive controllers are designed and the related experimental validations are conducted on the ground test facilities [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…For spacecraft operations subject to parametric uncertainties, adaptive controllers are designed and the related experimental validations are conducted on the ground test facilities [20,21]. For spacecraft operations, PID [22] and LQR [23] controllers are designed and experimentally tested, respectively. In addition, several controllers [24][25][26] are developed and validated on the testbed facility.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Saturated Neural Network Tracking Control of Spacecraft: Theory and Experimentation

Xia

Lee

Park

2019

International Journal of Aerospace Engineering

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An adaptive saturated neural network (NN) controller is developed for 6 degree-of-freedom (6DOF) spacecraft tracking, and its hardware-in-the-loop experimental validation is tested on the ground-based test facility. To overcome the dynamics uncertainties and prevent the large control saturation caused by the large tracking error at the beginning operation, a saturated radial basis function neural network (RBFNN) is introduced in the controller design, where the approximate error is counteracted by an adaptive continuous robust term. In addition, an auxiliary dynamical system is employed to compensate for the control saturation. It is proved that the ultimate boundedness of the closed-loop system is achieved. Besides, the proposed controller is implemented into a testbed facility to show the final operational reliability via hardware-in-the-loop experiments, where the experimental scenario describes that the simulator is tracking a planar trajectory while synchronizing its attitude with the desired angle. Experimental results illustrate that the proposed controller ensures that the simulator can track a preassigned trajectory with robustness to unknown inertial parameters and disturbances.

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Aerodynamic/control coupling optimization of reentry vehicle under wide speed range

Jiang,

Dong,

Pan

et al. 2024

Acta Mech. Sin.

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Suboptimal LQR-based spacecraft full motion control: Theory and experimentation

Abstract: 2016-12-23T18:37:48

Cited by 33 publications

References 27 publications

Control methodologies for vibration control of smart civil and mechanical structures

Control methodologies for vibration control of smart civil and mechanical structures

Adaptive Saturated Neural Network Tracking Control of Spacecraft: Theory and Experimentation

Aerodynamic/control coupling optimization of reentry vehicle under wide speed range

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