Bionic fuzzy sliding mode control and robustness analysis

Hua, Jing; An, Li-Xia; Li, Yimin

doi:10.1016/j.apm.2014.12.017

Cited by 34 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, the above research can also help to better understand the causes of spatial pattern formation and enrich the research paradigm of reaction-diffusion systems. In the future, we will continue to discuss the dynamics and control methods of spatiotemporal network rumor propagation [42][43][44][45][46] .…”

Section: Discussionmentioning

confidence: 99%

Pattern formation in a reaction–diffusion rumor propagation system with Allee effect and time delay

Zhu

2022

Nonlinear Dyn

View full text Add to dashboard Cite

This paper analyzes the diffusion behavior of the suspicious and the infected cabins in cyberspace by establishing a rumor propagation reaction diffusion model with Allee effect and time delay. The Turing instability conditions of the system under various conditions are emphatically studied. After considering the delay effect of rumor propagation systems, we have studied the correlation between the stability of the system under the influence of small time delay and the homogeneous system near the equilibrium point, and the critical condition of the delay-induced spatial instability is given. Further considering the possibility of diffusion coefficient changing with time, the critical parameter curves of stability and instability of approximate systems are given by means of Floquet theory, and the necessary conditions of Turing-instability of periodic coefficient are studied. In the numerical simulations, we find that the variation of diffusion coefficient will change the pattern type, and the periodical diffusion behavior will affect the arrangement of the crowd gathering area in the pattern.

show abstract

Section: Discussionmentioning

confidence: 99%

Pattern formation in a reaction–diffusion rumor propagation system with Allee effect and time delay

Zhu

2022

Nonlinear Dyn

View full text Add to dashboard Cite

show abstract

“…is improvement reduces the computational complexity of type-2 fuzzy sets, enhances the real-time application ability of type-2 fuzzy systems, and promotes the application of type-2 fuzzy set theory. By selecting different defuzzifiers to discuss the design of interval type-2 fuzzy systems, some researchers improved the theory of interval type-2 fuzzy systems, and the parameters of interval type-2 fuzzy systems are trained [18][19][20]. Some others pointed out the research status and development prospects of type-2 fuzzy systems in [11,21].…”

Section: Introductionmentioning

confidence: 99%

“…Because the T-S fuzzy model has great advantages in guaranteeing universal approximation and system stability, in recent years, studies on the type-2 T-S fuzzy model control achieved rich results [13][14][15][16][17][18][19][20][21][22][23][24][25]. In this paper, the type-2 direct adaptive fuzzy control of Mamdani and T-S type is used for addressing nonlinear systems, i.e., [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Type-2 Combined T-S Adaptive Fuzzy Control

Hao

Xia

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

For a class of nonlinear systems with a nonlinear relationship between input and output, a fuzzy control method combining interval type-2 and T-S fuzzy controller is proposed based on type-2 fuzzy system theory. In order to ensure its stability, anti-interference ability, and minimum approximation error, this design combines direct, indirect, supervised, and compensation control types to construct the controller. In this way, the structure of the controller not only has the characteristics of the type-2 fuzzy set, which can reduce the uncertainty of rules, but also has a T-S fuzzy model with linear combination of input variables, which can improve the modeling accuracy and reduce the number of rules of the system. By using the Lyapunov synthesis method, the global stability and the convergence of the closed-loop system under the condition that all variables are uniformly bounded are analyzed, and the adaptive laws of the system parameters are given as well. Finally, the effectiveness and superiority of the proposed method are verified by simulation.

show abstract

“…Nonlinear systems exist in real engineering widely. Since the pioneering work from Lurie in 1944, the research on nonlinear system control has become the challenging issue, and many techniques, such as differential geometry technique [1,2], sliding mode technique [3][4][5][6] and so on, have been proposed to deal with this problem. It can be noted that these approaches are based on multidimensional control.…”

Section: Introductionmentioning

confidence: 99%

New Results on the Control for a Kind of Uncertain Chaotic Systems Based on Fuzzy Logic

Wang

Chen

2019

Complexity

View full text Add to dashboard Cite

In this paper, the problem of the control for an uncertain nonlinear chaotic system has been studied; based on fuzzy logic, a kind of single-dimensional controller is constructed for the control of the chaotic systems in the situation that uncertainties and unknowns exist; at last some typical numerical simulations are carried out, and corresponding results illuminate the effectiveness of the controller.

show abstract

Bionic fuzzy sliding mode control and robustness analysis

Cited by 34 publications

References 17 publications

Pattern formation in a reaction–diffusion rumor propagation system with Allee effect and time delay

Pattern formation in a reaction–diffusion rumor propagation system with Allee effect and time delay

Type-2 Combined T-S Adaptive Fuzzy Control

New Results on the Control for a Kind of Uncertain Chaotic Systems Based on Fuzzy Logic

Contact Info

Product

Resources

About