Direct Data Driven Scheme for UAV Flight Control

Wang, Jianhong; Zhang, Ying; Ramirez‐Mendoza, Ricardo A.

doi:10.1109/access.2022.3213695

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Observing Figure 1 or cost functions ( 5), (15), plant P (z) exists in all above mathematical derivation. Moreover, those two final controllers ( 8) and ( 16) are dependent on unknown plant P (z), meaning we must identify that unknown plant P (z) firstly, and substitute its explicit or implicit forms in the final controller.…”

Section: Direct Data Driven Controlmentioning

confidence: 99%

“…Specifically, reference [14] gives a complete statistical analysis for one kind of direct data driven control, i.e. virtual reference feedback tuning control, and adaptive idea is combined with direct data driven control together to form adaptive iterative correlation tuning control [15]. Instead direct data driven control is benefit for engineering applications, for example, UAV flight system in [16].…”

Section: Introductionmentioning

confidence: 99%

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Complete Synthesis Analysis for Direct Data Driven Control

Bohua,

JIanhong

2023

IEEE Access

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Due to vast data in information era,direct data driven control is more widely studied all over the world, i.e. designing the unknown controller directly from the measured input-output data,while avoiding the modeling process for the unknown plant. For completeness, after classical model reference control is introduced from our own understanding, i.e. controller design, statistical analysis, algorithm and regularization,we propose a complete synthesis analysis for direct data driven control from some different points, for example, algorithm, statistical analysis and adaptation, such that making direct data driven control suit for different environments and paving new roads for further research. To prove our obtained theoretical results, one aircraft flight control system is applied to implement the main essence of direct data driven control strategy.

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Section: Direct Data Driven Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complete Synthesis Analysis for Direct Data Driven Control

Bohua,

JIanhong

2023

IEEE Access

Self Cite

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“…From a computational point of view, the problem of designing controller is transferred into one quadratic programming problem, being solved by a proximally stabilized search algorithm (Liao et al , 2020). During these recent years, we also achieve some new contributions about data-driven control strategy, for example, out contribution (Jianhong et al , 2022) that applies direct data-driven scheme into UAV flight control system, and contribution (Jianhong et al , 2021) that combines iterative identification and model predictive control to get another new control strategy, i.e. data-driven predictive control.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning data driven strategy for aircraft control system

Jianhong,

Xiaoyong

2023

AEAT

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Purpose This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy. Design/methodology/approach First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly. Findings A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control. Originality/value To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

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