Optimization of hysteretic chaotic neural network based on fuzzy sliding mode control

Xu, Guowei; Liu, Fengnan; Xiu, Chunbo; Sun, Liankun; Liu, Chang

doi:10.1016/j.neucom.2015.12.055

Cited by 26 publications

(8 citation statements)

References 47 publications

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“…It is also widely used in many fields such as optimization search, neural network, image compression, nonlinear time series data prediction, pattern recognition, fault diagnosis, secure communication, etc. [31][32][33][34].…”

Section: A Chaos Introductionmentioning

confidence: 99%

Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

Luo¹,

Jin²,

Li³

et al. 2020

IEEE Access

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Aimed at the bottleneck of Firework Algorithm's performance and the problem that it is easy to fall into the local extremum, this paper introduces a chaotic map approach and analyzes its statistical characteristics. New hybrid chaotic systems and a chaotic perturbation operator are designed. Based on different chaotic map approaches, a new Chaotic Firework Algorithm (CFWA) is proposed and simulated by typical test functions. The results show that performance of the Chaotic Firework Algorithm is generally better than the original algorithms, and the chaotic hybrid approaches are superior to single chaotic approaches. In order to further verify the application of the proposed algorithm in practical engineering problems, CFWA is applied to Blind Source Separation (BSS) of radar signals. As a result, it does well in sorting observed signals, and has faster convergence and better sorting performance than the traditional algorithms. In this way, the CFWA extends its engineering application.

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Section: A Chaos Introductionmentioning

confidence: 99%

Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

Luo¹,

Jin²,

Li³

et al. 2020

IEEE Access

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“…The sliding mode control includes good robustness, which is widely applied in robot dynamics control [20]- [22]. A lot of sliding mode control approaches have been proposed in recent years, such as terminal sliding model [23], global sliding model [24], neural sliding model [25]. A steady state error may occurs if a certain external disturbance in trajectory, ordinary sliding mode variable structure happens, leading to a condition that the required performance or trajectory tracking can not be reached in the system.…”

Section: Introductionmentioning

confidence: 99%

Disturbance Observer Based on Biologically Inspired Integral Sliding Mode Control for Trajectory Tracking of Mobile Robots

Yang¹,

Peng²,

Zhang³

et al. 2019

IEEE Access

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This paper proposes an integral sliding control system based on the nonlinear disturbance observer, aiming to the trajectory tracking of the mobile robot under the external disturbance. First, a kinematic model of mobile robot was built, besides, the position error signal was gained by the biological membrane potential model, and the problem of velocity oscillation was solved by the design of the backstepping controller. Then, an integral sliding control system was designed in accordance with the kinematic model of the mobile robot, meanwhile, a disturbance observer was designed in consideration of external disturbance to do the real-time observation on the disturbance occurring in the system with an addition of feedforward compensation and the observation error was converged by selecting the design parameters. The Lyapunov function was used to prove the stability of the system. Finally, the simulation of tracking circularity trajectory was utilized, with the comparison of trajectory without the use of jammer, to prove that this method can well overcome the nonlinear and uncertainty originated from external, thereby improving the control performance and increasing the robustness.INDEX TERMS Mobile robots, trajectory tracking, disturbance observer, integral sliding mode variable structure, back-stepping control.

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“…In 2016, Xu et al 3 implemented the hysteretic chaotic neural network with optimization. The model was designed based on the fuzzy SMC.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

Roy

Ghoshal²

2019

Robotica

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SUMMARY The flexible motion of the inchworm makes the locomotion mechanism as the prominent one than other limbless animals. Recently, the application of engineering greatly assists the inchworm locomotion to be applicable in the robotic mechanism. Due to the outstanding robustness, sliding mode control (SMC) has been validated as a robust control strategy for diverse types of systems. Even though the SMC techniques have made numerous achievements in several fields, some systems cannot be comfortably accepted as the general SMC approaches. Accordingly, this paper develops the Grey Wolf-Second order sliding mode control (GW-SoSMC) to control the manipulator of the inchworm robot. The GW-SoSMC reduces the chattering phenomenon of SMC and improves the controlling ability of SoSMC by weightage function. Subsequently, it compares the performance of the proposed method with several conventional techniques like Grey Wolf-SMC (GW-SMC), FireFly-SoSMC (FF-SoSMC), Artificial Bee Colony-SoSMC (ABC-SoSMC), Group Searching-SoSMC (GS-SoSMC), and Genetic Algorithm-SoSMC (GA-SoSMC). It portrays the valuable comparative analysis by measuring the accomplished joint angles, error, and response of the controller. Thus the proposed method discovers the supervisory controller for the inchworm robot that is immensely better than conventional controllers mentioned earlier.

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Optimization of hysteretic chaotic neural network based on fuzzy sliding mode control

Cited by 26 publications

References 47 publications

Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

Optimal Performance and Application for Firework Algorithm Using a Novel Chaotic Approach

Disturbance Observer Based on Biologically Inspired Integral Sliding Mode Control for Trajectory Tracking of Mobile Robots

Grey Wolf Optimization-Based Second Order Sliding Mode Control for Inchworm Robot

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