Optimal Tuning of Adaptive Augmenting Controller for Launch Vehicles in Atmospheric Flight

Trotta, Domenico; Zavoli, Alessandro; Matteis, G. De; Neri, Agostino

doi:10.2514/1.g005352

Cited by 9 publications

(5 citation statements)

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“…Gains for lateral, z-axis control are as small as one or two orders of magnitude lower than K P θ or K D θ in order to satisfy the constraints on maximum drift and drift-rate in Table 3 without hindering the attitude error performance [24].…”

Section: Baseline Controller a Control System Requirementsmentioning

confidence: 99%

“…Among many applications this approach has proved effective for tuning the longitudinal control loop of a fixed wing aircraft [20], for the attitude stabilization of a quad-rotor [21], and the synthesis of a robust servomechanism linear quadratic regulator on a fixedwing UAV [22]. A GA-based tuning, where a robust design optimization (RDO) problem [23] is solved, has been recently presented for improving the performance of an adaptive architecture for the attitude control of LVs [24].…”

Section: Introductionmentioning

confidence: 99%

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Genetic Algorithm Based Parameter Tuning for Robust Control of Launch Vehicle in Atmospheric Flight

et al. 2021

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An hybrid approach to the design of the attitude control system for a launch vehicle (LV) in the atmospheric flight phase is proposed in this paper, where a structured H ∞ controller is tuned using a genetic algorithm (GA). The H ∞ synthesis relies on a classical architecture for the thrust vector control (TVC) system that features proportional-derivative loops and bending filters. Once a set of requirements on stability and robustness typical of industrial practice is specified, control design is carried out by parameterizing the H ∞ weighting functions, and solving a two-layer max-min global optimization problem for the tuning parameters. The design methodology is applied to the model of a medium-size LV. The novel design is analyzed in off-nominal conditions taking into consideration model parameter scattering and wind disturbances. The results show that the automated design procedure provided by GA allows to devise timescheduled controllers providing adequate stability and performance, and appears as a viable and effective solution for reducing the burden of recurrent activities for controller tuning and validation conducted prior to each launch.

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Section: Baseline Controller a Control System Requirementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Based Parameter Tuning for Robust Control of Launch Vehicle in Atmospheric Flight

et al. 2021

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“…GA shows superior performance in forward flight among a number of EA algorithms, the performances of which are compared in the optimized tuning of a PID-based FCS for a medium-scale rotorcraft [39]. GA effectiveness for control tuning has been also exploited by the authors for the attitude control of a launch vehicle in atmospheric flight [40].…”

Section: Introductionmentioning

confidence: 99%

Optimization of UAV Robust Control Using Genetic Algorithm

D’antuono,

De Matteis,

Trotta

et al. 2023

IEEE Access

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A hybrid methodology combining the use of a robust LQR servomechanism and a genetic algorithm for the design of the flight control system (FCS) of a light unmanned aerial vehicle is the subject of this paper. The objective is to develop a systematic design approach based on a proven technique that provides improved time response and robust steady-state performance of the control system, so as to reduce the burden of trial-and-error procedures. The design of the FCS is formulated as an optimization problem aimed at maximizing a weighted sum of an appropriately defined multi-objective fitness function and evaluated through a series of nonlinear simulations, so as to fully engage the control system in complex maneuvers, such as combined changes in altitude and heading at different flight speeds. The performance of the proposed control design approach is evaluated using analytical tools for linear systems, softwarein-the-loop simulations, and Monte Carlo campaigns. The comparison between the new controller and a classical FCS with internal PID loops on attitude angles for stability and control augmentation is analyzed and discussed using an accurate vehicle model with an extended Kalman filter for output reconstruction.

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“…The determination of the parameters of the notch filter is the key to the design, and the location of the zero point of the notch filter; i.e., the frequency center, can be determined after the transfer function of the elastic launch vehicle is determined with sufficient accuracy in the model [4,5]. In order to solve the low-frequency, dense-frequency elastic vibration modes appearing in the launch vehicle, some scholars adopted the method of attitude control of the flexible launch vehicle by adaptive control of the adaptive notch, and the adaptive controller of the adaptive notch filter successfully stabilized the uncertain and time-varying equations of the launch vehicle model dynamics through thrust vector control [6]. Another scholar designed a bending mode filter for the whole system, which had a better filtering function for low-frequency, dense-frequency modes, and achieved good control results [7].…”

Section: Introductionmentioning

confidence: 99%

“…Cui Naigang et al applied an interference compensation control loop and active load reduction control loop based on the dilated state observer to the adaptive gain adjustment structure, and performed a simulation analysis of the pitch channel control [22]. To enhance the robustness to changes in elastic modal parameters, Domenico Trotta integrated the AAC control architecture with adaptive notch filters and proposed two novel and effective tuning methods for adaptively enhanced control systems, which were optimized by robust design and solved by genetic algorithms to achieve continuous improvement in the performance and robustness of standard launch vehicle single-axis attitude controllers in atmospheric flight [6,23]. Diego Navarro designed two adaptive augmentation control laws using a robust control design (structured H∞ control) as a baseline controller to improve the robust performance of AAC control, while analyzing the effect of the adaptive action on the classical stability margin, and validated this analysis using nonlinear time-domain stability margin evaluation techniques [24].…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Augmentation Control for a Flexible Launch Vehicle with Elastic Vibration

Pang

Zhou

Cai

et al. 2021

Entropy

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The continuous development of spacecraft with large flexible structures has resulted in an increase in the mass and aspect ratio of launch vehicles, while the wide application of lightweight materials in the aerospace field has increased the flexible modes of launch vehicles. In order to solve the problem of deviation from the nominal control or even destabilization of the system caused by uncertainties such as unknown or unmodelled dynamics, frequency perturbation of the flexible mode, changes in its own parameters, and external environmental disturbances during the flight of such large-scale flexible launch vehicles with simultaneous structural deformation, rigid-elastic coupling and multimodal vibrations, an improved adaptive augmentation control method based on model reference adaption, and spectral damping is proposed in this paper, including a basic PD controller, a reference model, and an adaptive gain adjustment based on spectral damping. The baseline PD controller was used for flight attitude control in the nominal state. In the non-nominal state, the spectral dampers in the adaptive gain adjustment law extracted and processed the high-frequency signal from the tracking error and control-command error between the reference model and the actual system to generate the adaptive gain. The adjustment gain was multiplied by the baseline controller gain to increase/decrease the overall gain of the system to improve the system’s performance and robust stability, so that the system had the ability to return to the nominal state when it was affected by various uncertainties and deviated from the nominal state, or even destabilized.

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Optimal Tuning of Adaptive Augmenting Controller for Launch Vehicles in Atmospheric Flight

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Cited by 9 publications

References 10 publications

Genetic Algorithm Based Parameter Tuning for Robust Control of Launch Vehicle in Atmospheric Flight

Genetic Algorithm Based Parameter Tuning for Robust Control of Launch Vehicle in Atmospheric Flight

Optimization of UAV Robust Control Using Genetic Algorithm

Improved Adaptive Augmentation Control for a Flexible Launch Vehicle with Elastic Vibration

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