Computationally Efficient Stability-Based Nonlinear Model Predictive Control Design for Quadrotor Aerial Vehicles

Gomaa, Mahmoud A. K.; Silva, Oscar De; Mann, George K. I.; Gosine, Raymond G.

doi:10.1109/tcst.2022.3188399

Cited by 3 publications

(4 citation statements)

References 54 publications

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“…Furthermore, a group from the Czech Technical University deployed quadrotors with PD control [157] and MPC [158]. However, MPC faces the challenge of a heavy computational load, but there have been published ideas to tackle this issue [80,81]. The current focus is on transitioning these ideas from theoretical proposals to practical implementation in real flight scenarios while also addressing any potential challenges that may arise during this process.…”

Section: Comparative Discussionmentioning

confidence: 99%

“…Consequently, these control methodologies are deemed more favorable within the realm of high-impact studies. [15] (RP167), [74] (X5), [157] (X6), [80] (X7), [81] (X8), [110] (X9), [158] (X10), [83] (X11), [108] (X12), [149] (X13), [109] (X14), [144] (X15), [150] (X16), [148] (X17).…”

Section: Comparative Discussionmentioning

confidence: 99%

“…In [81], a computationally efficient NMPC was proposed to derive a growth-bound sequence, enabling the shortest possible prediction horizon for stability. A comparison with [58] demonstrates that when [58] used N = 4, they failed, whereas their approach did not.…”

mentioning

confidence: 99%

“…Illustration of the four development stages for quadrotor control leading to actual deployment. Reference numbers are as follows:[145] (X1),[75] (X2),[79] (X3),[15] (RP167),[74] (X5),[157] (X6),[80] (X7),[81] (X8),[110] (X9),[158] (X10),[83] (X11),[108] (X12),[149] (X13),[109] (X14),[144] (X15),[150] (X16),[148] (X17).…”

mentioning

confidence: 99%

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Historical and Current Landscapes of Autonomous Quadrotor Control: An Early-Career Researchers’ Guide

Asignacion,

Satoshi

2024

Drones

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The rising demand for autonomous quadrotor flights across diverse applications has led to the introduction of novel control strategies, resulting in several comparative analyses and comprehensive reviews. However, existing reviews lack a comparative analysis of experimental results from published papers, resulting in verbosity. Additionally, publications featuring comparative studies often demonstrate biased comparisons by either selecting suboptimal methodologies or fine-tuning their own methods to gain an advantageous position. This review analyzes the experimental results of leading publications to identify current trends and gaps in quadrotor tracking control research. Furthermore, the analysis, accomplished through historical insights, data-driven analyses, and performance-based comparisons of published studies, distinguishes itself by objectively identifying leading controllers that have achieved outstanding performance and actual deployment across diverse applications. Crafted with the aim of assisting early-career researchers and students in gaining a comprehensive understanding, the review’s ultimate goal is to empower them to make meaningful contributions toward advancing quadrotor control technology. Lastly, this study identifies three gaps in result presentation, impeding effective comparison and decelerating progress. Currently, advanced control methodologies empower quadrotors to achieve a remarkable flight precision of 1 cm and attain flight speeds of up to 30 m/s.

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Section: Comparative Discussionmentioning

confidence: 99%

Section: Comparative Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Historical and Current Landscapes of Autonomous Quadrotor Control: An Early-Career Researchers’ Guide

Asignacion,

Satoshi

2024

Drones

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Neuroadaptive Sliding Mode Tracking Control for an Uncertain TQUAV With Unknown Controllers

Xiong,

2024

Intl J Robust & Nonlinear

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In this article, a neuroadaptive sliding mode control (NSMC) strategy based on recurrent neural network (RNN) for robustly and adaptively tracking the desired position and attitude of an uncertain tilting quadrotor unmanned aerial vehicle (TQUAV) with unknown controllers is presented. The main contribution of this article is the real‐time adjustment of unknown flight controllers using the approximation characteristics of RNN, in which the derived approximation errors of RNN are sufficiently estimated by adaptive control method that can reduce or eliminate the impact of error terms on the evolution of closed‐loop systems. Especially, Lyapunov stability analysis is greatly simplified compared to existing methods and does not require amplification or reduction. Finally, the superior performance of the NSMC strategy was verified by comparing simulation results.

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Explicit flatness-based MPC design for indoor nano-quadcopter position tracking

Do,

Prodan

2024

International Journal of Control

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Computationally Efficient Stability-Based Nonlinear Model Predictive Control Design for Quadrotor Aerial Vehicles

Cited by 3 publications

References 54 publications

Historical and Current Landscapes of Autonomous Quadrotor Control: An Early-Career Researchers’ Guide

Historical and Current Landscapes of Autonomous Quadrotor Control: An Early-Career Researchers’ Guide

Neuroadaptive Sliding Mode Tracking Control for an Uncertain TQUAV With Unknown Controllers

Explicit flatness-based MPC design for indoor nano-quadcopter position tracking

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