Iterative learning control for UAVs formation based on point-to-point trajectory update tracking

Fu, Xingjian; Peng, Jianshuai

doi:10.1016/j.matcom.2023.01.038

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Thus, model-free control is gradually receiving much attention recently due to its modelindependent characteristics [18,19]. In the recent years, several model-free control methods that only rely on input and output system data have been developed, such as active disturbance rejection control [20,21], dynamiclinearization-based model free adaptive control [22,23], iterative learning control [24,25], etc. Moreover, on the basis of ultra-local model (ULM) principle, some enhanced and effective model-free controllers have been proposed [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Tian

et al. 2023

Preprint

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In this paper, a model-free fractional-order adaptive prescribed performance controller (MFF-APPC) is designed for mechatronic systems in presence of uncertainties, external disturbances and actuator failure. The MFF-APPC is composed of ultra-local model (ULM)-based time-delay estimation (TDE), Proportional-Differential (PD) controller and fractional-order adaptive prescribed performance compensator (FAPPC). The ULM is utilized to approximate the complex mechatronic system in an extra-short sliding time window and TDE is employed to observe and eliminate the lumped disturbance. Then, PD controller is designed to stabilize the closed-loop system. Correspondingly, the TDE-based PD (TDE-PD) controller is constructed. However, there always exists estimation error under TDE technique and the tracking performance cannot be guaranteed. Hence, the FAPPC is designed and introduced into TDE-PD controller to eliminate the effect of estimation error on control performance and constrain the tracking error with prescribed convergence time and precision. Moreover, an adaptive parameter is designed to approximate the upper bound of estimation error in TDE and thus reduces the unwanted chattering on the switching manifold. Correspondingly, the MFF-APPC is constructed. Furthermore, the stability of closed-loop system under the proposed MFF-APPC is analyzed by using Lyapunov theorem. To validate the proposed method, the numerical simulation on 2-DOF robotic manipulator and co-simulation on virtual prototype of 7-DOF iReHave upper limb exoskeleton are completed. Through the compared simulation results with supervising switching technique based adaptive model-free controller (SST-AMFC) and robust fault tolerant controller (RFTC), the proposed strategy shows better trajectory tracking with prescribed performance.

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Section: Introductionmentioning

confidence: 99%

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Tian

et al. 2023

Preprint

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“…Notably, ILC's lack of reliance on the knowledge of system dynamics, coupled with its superior adaptability, renders it an ideal fit for complex control systems. ILC is widely used in robot control systems [1]- [3], medical rehabilitation [4], [5], multi-agent formation [6], [7], batch processes [8], [9], train automatic control [10], [11]and so on.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for High Relative Degree Discrete-Time Systems with Random Initial Shifts

Chen,

Lu,

Yin

2023

IJACSA

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In this paper, an iterative learning control (ILC) strategy under compression mapping framework is presented for high relative degree discrete-time systems with random initial shifts. Firstly, utilizing the high relative degree of the system and difference term, a control law is designed and a p-order non-homogeneous linear difference equation is established. The appropriate control gain is selected according to the characteristics of solution of the difference equation and the initial shifts, so as to ensure that the high relative degree discrete-time system can reach a steady-state deviation output at a fixed time. Subsequently, a PD-type control law is employed to correct the fixed deviation of the system. Theoretical analysis indicates that this ILC strategy can ensure that the high relative degree systems achieve accurate tracking after the predefined time. Finally, the simulation experiments are conducted on a linear discretetime Multiple-Input Multiple-Output(MIMO) system with relative degree 1 and a Multiple-Input Single-Output(MISO) system with relative degree 2, respectively, and the results verify the effectiveness of the algorithm.

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Trajectory tracking of QUAV based on cascade DRL with feedforward control

He,

Han,

Cheng

2025

Neurocomputing

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Iterative learning control for UAVs formation based on point-to-point trajectory update tracking

Cited by 6 publications

References 19 publications

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Model-free fractional-order adaptive prescribed performance control for mechatronic systems with actuator failure

Iterative Learning Control for High Relative Degree Discrete-Time Systems with Random Initial Shifts

Trajectory tracking of QUAV based on cascade DRL with feedforward control

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