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Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition

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A Special Issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (16 June 2026) | Viewed by 19658

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Editors

Dr. Heng Shi

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Guest Editor

Department of Precision Instruments, Tsinghua University, Beijing 100190, China
Interests: cooperative guidance; intelligent guidance; trajectory planning; aircraft control; sensor fusion; aircraft simulations; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jihong Zhu

E-Mail Website
Guest Editor

Department of computer science and technology, Tsinghua University, Beijing 100190, China
Interests: automatic control; flight control; unmanned system
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Zheng Chen

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Guest Editor

School of Aeronautics and Astronautics, Zhejiang University, Zhejiang 310058, China
Interests: guidance, navigation, and control; flight dy-namics and simulations; optimal control and optimization
Special Issues, Collections and Topics in MDPI journals

Dr. Minchi Kuang

E-Mail Website
Guest Editor

Department of Precision Instrument, Tsinghua University, Beijing 100190, China
Interests: artificial intelligence; automatic control; flight control; unmanned system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Path planning, trajectory tracking, and guidance are essential for the autonomous operations of Unmanned Aerial Vehicles (UAVs). These processes involve determining the optimal path, implementing the planned path, and making real-time adjustments to ensure accurate tracking and obstacle avoidance. The ability to plan efficient and safe paths for UAVs is crucial for the successful completion of missions, especially in complex environments. Moreover, implementing planned paths while considering external factors such as wind and turbulence, along with real-time guidance adjustment, ensures UAV safety and stability. Research in this area focuses on developing advanced algorithms and control systems that enable UAVs to operate autonomously and effectively in complex environments.

This Special Issue will collect the latest research results for path planning, trajectory tracking, and guidance of UAVs, which are fundamentally important for the autonomous operations of UAVs.

Papers are solicited in areas directly related to topics including, but not limited to, the following:

Path planning and task assignment for UAV swarms;
Autonomous navigation and localization (both outdoor and indoor);
Autonomous decision making;
Trajectory planning and optimization;
Guidance for individual UAV or for multiple cooperative UAVs;
Control algorithms;
Data-driven guidance and control;
AI-based planning.

We look forward to receiving your original research articles and reviews.

Dr. Heng Shi
Prof. Dr. Jihong Zhu
Prof. Dr. Zheng Chen
Dr. Minchi Kuang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

path planning
trajectory tracking
guidance, navigation, and control
autonomous control
trajectory optimization
formation and reconfiguration

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Related Special Issues

Path Planning, Trajectory Tracking and Guidance for UAVs in Drones (15 articles)
Path Planning, Trajectory Tracking and Guidance for UAVs: 2nd Edition in Drones (11 articles)
Path Planning, Trajectory Tracking and Guidance for UAVs: 4th Edition in Drones (3 articles)

Published Papers (12 papers)

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Research

26 pages, 4369 KB

Open AccessArticle

Robust Trajectory Inference for Quadrotor UAVs Under External Disturbances via a Sliding-Mode-Enhanced CLOE Approach

by Fabrizio Ricardo Cahuas-Talledo, Juan Eduardo Velázquez-Velázquez and Alberto Luviano-Juárez

Drones 2026, 10(8), 593; https://doi.org/10.3390/drones10080593 - 2 Aug 2026

Viewed by 370

Abstract

This article examines the challenge of trajectory inference for an unknown system affected by external disturbances, with the objective of reconstructing the trajectory of a quadrotor using a reference model. The proposed methodology extends the Closed-Loop Output Error (CLOE) scheme through two complementary contributions: an identified gain, incorporated into the reference model to guarantee the Hurwitz condition of the closed-loop error dynamics, and a set of sliding-mode correction terms that further accelerate error convergence and enhance robustness against bounded disturbances. The stability of both contributions is formally established via Lyapunov-based analysis. The proposed approach is validated through realistic simulations carried out in the CoppeliaSim robotics environment, considering both constant and time-varying trajectory scenarios. Results show that the hybrid approach improves trajectory inference accuracy and convergence speed, maintaining resilience under adverse conditions, making it a promising alternative for autonomous quadrotor monitoring. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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30 pages, 1986 KB

Open AccessArticle

Wind-Aware Synchronized-Arrival Optimization for Small-Turbofan UAVs via Segmented RPM Scheduling and Phase-Circle Retention

by Xin Zhang, Bing Li, Changwei Mi, Yangyang Zhao, Junqi Dong, Zhenyang Cui and Zeyuan Zhao

Drones 2026, 10(8), 565; https://doi.org/10.3390/drones10080565 - 24 Jul 2026

Viewed by 328

Abstract

Cooperative UAV missions often require multiple vehicles to reach a target area at nearly the same time. For small-turbofan UAVs flying along recorded routes, this timing task is affected by path-length differences, spatially varying wind, and phase-circle segments kept for waiting or coordination. Under these conditions, using only a fixed cruise setting often leaves limited room for arrival-time adjustment. This paper develops a wind-aware synchronized-arrival optimization method for small-turbofan UAVs based on segmented RPM scheduling and phase-circle retention. The method combines local wind projection along the path, an empirical RPM-to-airspeed model, and discrete flight-time integration, and it evaluates three timing-adjustment modes: fixed-alpha segmented RPM optimization, fixed-RPM phase-circle retention optimization, and their joint optimization. The method is tested on the recorded trajectories of UAV 13 and UAV 14, using a reconstructed wind field obtained from multi-UAV flight data. In the wind-aware constant-RPM baseline, both UAVs keep the full phase-circle segments and fly at 6500 rpm, which gives a predicted synchronization error of

1.192

s. Segmented RPM optimization reduces the error to

0.013

s. Phase-circle retention optimization reduces it to

0.007

s and shortens the total path length from 37,752.3 m to 26,103.4 m. The joint optimization gives the best result, with a synchronization error of

6.58 \times 10^{- 4}

s and a total path length of 26,094.4 m. A no-wind ablation study also shows that ignoring wind leads to a total flight-time bias of about 29–31 s. These results indicate that phase-circle retention is effective for coarse flight-time adjustment through path shortening, whereas segmented RPM scheduling is better suited to fine timing correction. Using the two together improves synchronized-arrival prediction and adjustment for recorded UAV paths in wind. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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30 pages, 2493 KB

Open AccessArticle

Dual-Layer Adaptive T-Perturbation and Opposition-Based MOPSO for 3D UAV Path Planning in Complex Threat Environments

by Chenyang Sun, Xingyu He, Duo Qi and Xiaoyue Ren

Drones 2026, 10(7), 480; https://doi.org/10.3390/drones10070480 - 23 Jun 2026

Cited by 1 | Viewed by 355

Abstract

Three-dimensional UAV operations require path planning methods that can jointly maintain route efficiency, threat avoidance, and trajectory smoothness under spatially distributed and time-varying constraints. To address this problem, this paper develops an integrated Dual-Layer Adaptive T-perturbation and Opposition-based Multi-Objective Particle Swarm Optimization framework, termed DATO-MOPSO, for 3D UAV path planning in complex threat environments. The method integrates a dual-layer adaptive inertia-weight and velocity-regulation mechanism with symmetric T-perturbation, an elite quasi-opposition-based learning strategy for diversity recovery and feasible local exploitation, and an archive-driven simulated annealing rule for stagnation-aware personal-best updating. A three-objective model minimizing path length, threat exposure, and path smoothness is established, and comparative experiments against MOPSO, ZAMOPSO, NSGA-II, and SPEA2 are conducted in both static and dynamic environments, together with statistical and ablation analyses. In the static scenario, DATO-MOPSO achieved the highest mean HV and stable repeated-run performance, but its IGD was comparable to ZAMOPSO with higher computational cost. In the dynamic scenario, DATO-MOPSO showed its main advantage, achieving the highest mean HV and the lowest mean IGD with statistically significant HV and IGD improvements over all baselines. Overall, DATO-MOPSO is most advantageous in time-varying complex threat environments, whereas its static-scenario advantages are accompanied by higher computational cost. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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24 pages, 2595 KB

Open AccessArticle

Spatio-Temporal Cooperative Optimization of UAVs and WSNs for Urban Fire Monitoring

by Mingzhan Chen and Yaqin Xie

Drones 2026, 10(5), 320; https://doi.org/10.3390/drones10050320 - 23 Apr 2026

Viewed by 628

Abstract

To address challenges such as the sudden onset of urban fires, data synchronization delays in early warning systems, response lags, and insufficient routine monitoring, this paper proposes a Spatio-Temporal Collaborative Optimization for Joint Control and Scheduling (STCO-JCS) algorithm tailored for unmanned aerial vehicles (UAVs) and wireless sensor networks (WSNs). First, spatial autocorrelation analysis based on fire data classifies areas into ultra-high, high, medium, and low risk zones to assist in determining UAV access priorities. Second, we construct optimal inspection trajectories for the UAV by taking into account the inspection sequence and the city’s topography. By modeling the path deviations caused by wind interference and designing precision control algorithms, we improve the accuracy of the UAV’s flight path, ultimately achieving the goal of reducing UAV inspection time. Finally, by coordinating the spatiotemporal operations of drones and wireless sensor networks, we can achieve early detection and rapid response in high-risk fire zones, thereby reducing drone energy consumption while enhancing the efficiency of the UAV-WSN fire monitoring system. Simulation results demonstrate that under a 20-square-kilometer simulation area, STCO-JCS controls inspection paths within 14–17 km. In the multi-UAV scenario, the proposed method achieves approximately 3.17–9.66% improvement in energy efficiency, while in the single-UAV scenario, improvements of 10.83%, 50.54%, and 9.26% are observed in metrics. This provides effective decision support for the dynamic deployment of firefighting and rescue resources. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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23 pages, 8315 KB

Open AccessArticle

Dubins-Aware NCO: Learning SE(2)-Equivariant Representations for Heading-Constrained UAV Routing

by Jiazhan Gao, Yutian Wu, Liruizhi Jia, Heng Shi and Jihong Zhu

Drones 2026, 10(1), 59; https://doi.org/10.3390/drones10010059 - 14 Jan 2026

Cited by 1 | Viewed by 1122

Abstract

The nonholonomic constraints of fixed-wing UAVs, characterized by coupled heading-curvature feasibility and asymmetric costs, fundamentally deviate from classical Euclidean routing assumptions. While standard neural combinatorial optimization (NCO) architectures could theoretically incorporate Dubins costs via reward signals, such naive adaptations lack the capacity to explicitly model the intrinsic SE(2) geometric invariance and directional asymmetry of fixed-wing motion, leading to suboptimal generalization. To bridge this gap, we propose a Dubins-Aware NCO framework. We design a dual-channel embedding to decouple asymmetric physical distances from rotation-stable geometric features. Furthermore, we introduce a Rotary Phase Encoding (RoPhE) mechanism that theoretically guarantees strict SO(2) equivariance within the attention layer. Extensive sensitivity, ablation, and cross-distribution generalization experiments are conducted on tasks spanning varying turning radii and problem variants with instance scales of 10, 20, 36, and 52 nodes. The results consistently validate the superior optimality and stability of our approach compared with state-of-the-art DRL and NCO baselines, while maintaining significant computational efficiency advantages over classical heuristics. Our results highlight the importance of explicitly embedding geometry-physics consistency, rather than relying on scalar reward signals, for real-world fixed-wing autonomous scheduling. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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29 pages, 2810 KB

Open AccessArticle

PAIR: A Hybrid A^* with PPO Path Planner for Multi-UAV Navigation in 2-D Dynamic Urban MEC Environments

by Bahaa Hussein Taher, Juan Luo, Ying Qiao and Hussein Ridha Sayegh

Drones 2026, 10(1), 58; https://doi.org/10.3390/drones10010058 - 13 Jan 2026

Cited by 3 | Viewed by 1993

Abstract

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible

100 \times 100

grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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25 pages, 25190 KB

Open AccessArticle

Collaborative Vehicle-Mounted Multi-UAV Routing and Scheduling Optimization for Remote Sensing Observations

by Bing Du, Anqi Tang, Huping Ye, Huanyin Yue, Chenchen Xu, Lina Hao, Hongbo He and Xiaohan Liao

Drones 2025, 9(11), 783; https://doi.org/10.3390/drones9110783 - 11 Nov 2025

Cited by 2 | Viewed by 2102

Abstract

Vehicle-mounted multi-UAV (VM-UAV) systems offer enhanced flexibility and rapid deployment for large-scale remote sensing tasks such as disaster response and land surveys. However, maximizing their operational efficiency remains challenging, as it requires the simultaneous resolution of task scheduling and coverage path planning—an NP-hard problem. This study presents a novel multi-objective genetic algorithm (GA) framework that jointly optimizes routing and scheduling for cost-constrained, load-balanced multi-UAV remote sensing missions. To improve convergence speed and solution quality, we introduce two innovative operators: a Multi-Region Edge Recombination Crossover (MRECX) to preserve superior path segments from parents and an Adaptive Hybrid Mutation (AHM) mechanism that dynamically adjusts mutation strategies to balance exploration and exploitation. The algorithm minimizes total flight distance while equalizing workload distribution among UAVs. Extensive simulations and experiments demonstrate that the proposed GA significantly outperforms conventional GA, particle swarm optimization (PSO), ant colony optimization (ACO), and clustering-based planning methods in both solution quality and robustness. The practical applicability of our framework is further validated through two real-world case studies. The results confirm that the proposed approach delivers an effective and scalable solution for vehicle-mounted multi-UAV scheduling and path planning, enhancing operational efficiency in time-critical remote sensing applications. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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24 pages, 41039 KB

Open AccessArticle

A Novel Design of a Sliding Mode Controller Based on Modified ERL for Enhanced Quadcopter Trajectory Tracking

by Ahmed Abduljabbar Mahmood, Fernando García and Abdulla Al-Kaff

Drones 2025, 9(11), 737; https://doi.org/10.3390/drones9110737 - 23 Oct 2025

Cited by 3 | Viewed by 993

Abstract

This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified exponential reaching law (ERL) of the sliding mode control (SMC) based on a Gaussian kernel function with a continuous nonlinear Smoother Signum Function (SSF). The smooth continuous signum function is proposed as a substitute for the signum function to prevent the chattering effect caused by the switching sliding surface. The closed-loop system’s stability is ensured according to Lyapunov’s stability theory. Optimal trajectory tracking is attained based on particle swarm optimization (PSO) to select the controller parameters. A comparative analysis with a classical hierarchical SMC based on different ERLs (sign function, saturation function, and SSF) is presented to further substantiate the superior performance of the proposed controller. The outcomes of the simulation prove that the suggested controller has much better effectiveness, unknown disturbance resistance, input variation resistance, and parametric uncertainty than the other controllers, which produce chattering and make the control signal range fall within unrealistic values. Furthermore, the suggested controller outperforms the classical SMC by reducing the tracking integral mean squared errors by 96.154% for roll, 98.535% for pitch, 44.81% for yaw, and 22.8% for altitude under normal flight conditions. It also reduces the tracking mean squared errors by 99.05% for roll, 99.26% for pitch, 40.18% for yaw, and 99.998% for altitude under trajectory tracking flight conditions in the presence of external disturbances. Therefore, the proposed controller can efficiently follow paths in the presence of parameter uncertainties, input variation, and external disturbances. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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21 pages, 2522 KB

Open AccessArticle

A Reinforcement Learning-Based Adaptive Grey Wolf Optimizer for Simultaneous Arrival in Manned/Unmanned Aerial Vehicle Dynamic Cooperative Trajectory Planning

by Wei Jia, Lei Lv, Ruizhi Duan, Tianye Sun and Wei Sun

Drones 2025, 9(10), 723; https://doi.org/10.3390/drones9100723 - 17 Oct 2025

Cited by 9 | Viewed by 1876

Abstract

Addressing the challenge of high-precision time-coordinated path planning for manned and unmanned aerial vehicle (UAV) clusters operating in complex dynamic environments during missions like high-level autonomous coordination, this paper proposes a reinforcement learning-based Adaptive Grey Wolf Optimizer (RL-GWO) method. We formulate a comprehensive multi-objective cost function integrating total flight distance, mission time, time synchronization error, and collision penalties. To solve this model, we design multiple improved GWO strategies and employ a Q-Learning framework for adaptive strategy selection. The RL-GWO algorithm is embedded within a dual-layer “global planning + dynamic replanning” framework. Simulation results demonstrate excellent convergence and robustness, achieving second-level time synchronization accuracy while satisfying complex constraints. In dynamic scenarios, the method rapidly generates safe evasion paths while maintaining cluster coordination, validating its practical value for heterogeneous UAV operations. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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23 pages, 12369 KB

Open AccessArticle

Dual-Objective Model Predictive Control for Longitudinal Tracking and Connectivity-Aware Trajectory Optimization of Fixed-Wing UAVs

by Abdurrahman Talha Yildiz and Kemal Keskin

Drones 2025, 9(10), 719; https://doi.org/10.3390/drones9100719 - 16 Oct 2025

Cited by 2 | Viewed by 2421

Abstract

This paper presents a dual-objective Model Predictive Control (MPC) framework for fixed-wing unmanned aerial vehicles (UAVs). The framework was designed with two goals in mind: improving longitudinal motion control and optimizing the flight trajectory when connectivity and no-fly zone constraints are present. A multi-input–multi-output model derived from NASA’s Generic Transport Model (T-2) was used and linearized for controller design. We compared the MPC controller with a Linear Quadratic Regulator (LQR) in MATLAB simulations. The results showed that MPC reached the reference values faster, with less overshoot and phase error, particularly under sinusoidal reference inputs. These differences became even more evident when the UAV had to fly in windy conditions. Trajectory optimization was carried out using the CasADi framework, which allowed us to evaluate paths that balance two competing requirements: reaching the target quickly and maintaining cellular connectivity. We observed that changing the weights of the cost function had a strong influence on the trade-off between direct flight and reliable communication, especially when multiple base stations and no-fly zones were included. Although the study was limited to simulations at constant altitude, the results suggest that MPC can serve as a practical tool for UAV missions that demand both accurate flight control and robust connectivity. Future work will extend the framework to more complete models and experimental validation. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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21 pages, 5727 KB

Open AccessArticle

Model-in-the-Loop Design and Flight Test Validation of Flight Control Laws for a Small Fixed-Wing UAV

by Ting-Ju Shen and Chieh-Li Chen

Drones 2025, 9(9), 624; https://doi.org/10.3390/drones9090624 - 4 Sep 2025

Cited by 6 | Viewed by 3947

Abstract

This study provides an experimentally validated workflow for the development and model-in-the-loop (MIL) validation of flight control laws for a small, low-cost fixed-wing UAV within a model-based design (MBD) framework, addressing the limitation that previous workflow demonstrations largely remain conceptual or simulation-only and that systematic processes for low-cost UAVs are lacking. A key advantage is that control law methods or parameters can be determined prior to flight testing, avoiding on-site tuning, a major challenge in UAV deployment. The Skysurfer X8 UAV served as the experimental platform. Linearized dynamic models were derived to design rate and attitude controllers using frequency-domain techniques, where loop shaping was applied to meet U.S. military flight quality standards. The control algorithms were validated in an MIL environment, enabling early evaluation of control logic, dynamic response, and robustness under idealized and perturbed conditions. Following MIL verification, the control logic was generated via Simulink Coder and deployed on a Pixhawk 6C flight controller with the PX4 autopilot. Flight test results on the Skysurfer X8 showed good agreement with MIL simulations, confirming the reliability and consistency of the proposed methodology in both simulated and real domains, while also demonstrating a systematic workflow that fills a practical gap in low-cost UAV development. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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19 pages, 9202 KB

Open AccessArticle

Fuzzy Adaptive Fixed-Time Bipartite Consensus Self-Triggered Control for Multi-QUAVs with Deferred Full-State Constraints

by Chenglin Wu, Shuai Song, Xiaona Song and Heng Shi

Drones 2025, 9(8), 591; https://doi.org/10.3390/drones9080591 - 20 Aug 2025

Viewed by 1158

Abstract

This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article

(This article belongs to the Special Issue Path Planning, Trajectory Tracking and Guidance for UAVs: 3rd Edition)

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