Hybrid Nonsingleton Fuzzy Strong Tracking Kalman Filtering for High Precision Photoelectric Tracking System

Zhao, Tao; Tong, Wei; Mao, Yao

doi:10.1109/tii.2022.3160632

Cited by 34 publications

(8 citation statements)

References 28 publications

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“…The uncertainty of the system can be approximated by a fuzzy logic system [ 58 , 59 ] and then an approximation-based controller can be designed as follows.…”

Section: Resultsmentioning

confidence: 99%

Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection

Yan

Qin

et al. 2023

Sensors

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This study proposed a novel adaptive interval Type-II fuzzy fault-tolerant control for constrained uncertain 2-DOF robotic multi-agent systems with an active fault-detection algorithm. This control method can realize the predefined-accuracy stability of multi-agent systems under input saturation constraint, complex actuator failure and high-order uncertainties. Firstly, a novel active fault-detection algorithm based on pulse-wave function was proposed to detect the failure time of multi-agent systems. To the best of our knowledge, this was the first time that an active fault-detection strategy had been used in multi-agent systems. Then, a switching strategy based on active fault detection was presented to design the active fault-tolerant control algorithm of the multi-agent system. In the end, based on the interval type-II fuzzy approximated system, a novel adaptive fuzzy fault-tolerant controller was proposed for multi-agent systems to deal with system uncertainties and redundant control inputs. Compared with other relevant fault-detection and fault-tolerant control methods, the proposed method can achieve predefinition of stable accuracy with smoother control input. The theoretical result was verified by simulation.

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“…The uncertainty of the system can be approximated by a fuzzy logic system [ 58 , 59 ] and then an approximation-based controller can be designed as follows.…”

Section: Resultsmentioning

confidence: 99%

Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection

Yan

Qin

et al. 2023

Sensors

Self Cite

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“…In the fields of optoelectronic tracking [1,2], free-space optical communication [3,4], and trajectory prediction [5,6], a significant challenge is the inability to obtain precise motion equations of the tracked targets. This issue is particularly pronounced when tracking non-cooperative, maneuvering targets, where we are limited to use generic models for estimation.…”

Section: Introductionmentioning

confidence: 99%

Multiple adaptive factors based interacting multiple model estimator

Sun,

Duan,

Xia

et al. 2024

IET Control Theory & Appl

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In the field of optoelectronic tracking, precisely modeling the motion equations of the tracked target is often challenging, and in some cases, they may even be entirely unknown. This necessitates the use of a robust state estimator for accurate state estimation. Additionally, atmospheric turbulence, variations in illumination, and intricate observation backgrounds may introduce a significant increase in observation noise for the tracked target. To address these challenges, one approach is to introduce adaptive factors, such as the Mahalanobis method, into the robust state estimator to enhance estimation accuracy. However, further exploration has revealed that adaptive factors designed using different methods offer unique advantages in scenarios with varying levels of noise amplification. In this paper, different adaptive factors are further combined using an interacting multiple model approach, allowing the designed state estimator to exhibit stronger adaptability to noise amplification. The stability and effectiveness of this algorithm are validated through program simulations, double reflection mirror experiment, and drone trace prediction, demonstrating its applicability and reliability in diverse scenarios.

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“…In the aspect of manipulator error compensation, the fuzzy control method is a suitable method [18,19]. The main advantage of a fuzzy system is that it can express the experience and knowledge of human understanding by mathematical formulas [20][21][22]. However, with the increasing requirements of control precision, the traditional simple fuzzy control system based on expert experience has been unable to meet the needs [23].…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator

Sun

Zhao

Liu

2023

Entropy

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In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.

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Hybrid Nonsingleton Fuzzy Strong Tracking Kalman Filtering for High Precision Photoelectric Tracking System

Cited by 34 publications

References 28 publications

Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection

Interval Type-II Fuzzy Fault-Tolerant Control for Constrained Uncertain 2-DOF Robotic Multi-Agent Systems with Active Fault Detection

Multiple adaptive factors based interacting multiple model estimator

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator

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