Improved Sliding Mode Control for Finite-Time Synchronization of Nonidentical Delayed Recurrent Neural Networks

Xiong, Jing-Jing; Zhang, Guobao; Wang, Junxiao; Yan, Tianhong

doi:10.1109/tnnls.2019.2927249

Cited by 47 publications

(35 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural network control has nonlinear approximation characteristics and self-learning and self-organization capabilities. Most importantly, it does not require an accurate model of the controlled plant to realize adaptive control of the servo control system [20,21,22,23]. At present, the control methods combining intelligent control and sliding mode control have made many research results [24,25,26,27].…”

Section: Introductionmentioning

confidence: 99%

High-precision motion control method for permanent magnet linear synchronous motor

Zhao

Yuan

2021

IEICE Electron. Express

View full text Add to dashboard Cite

In this paper, aiming at the known or unknown uncertainties in permanent magnet linear synchronous motor (PMLSM) servo system, an intelligent backstepping terminal sliding mode control (IBTSMC) method is proposed for accurate position tracking of the servo system. The designed controller makes use of the advantage of backstepping terminal sliding mode control theory to ensure fast convergence and robustness. Finally, the stability analysis is carried out by using Lyapunov stability theory, and the effectiveness of the designed control scheme is proved by experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

High-precision motion control method for permanent magnet linear synchronous motor

Zhao

Yuan

2021

IEICE Electron. Express

View full text Add to dashboard Cite

show abstract

“…Accordingly, with the in-depth study of RNNs, it can be seen that the stability is a forerunner condition for the multifarious practical applications. Hence, the research on the stability of the system is becoming more and more abundant [1][2][3][4][5][6][7][8][9][10][11][12][13][14], such as asymptotic stability [1], exponential stability [2][3][4], multistability [5], synchronization [6], dissipativity [3,7,8], region stability [9], memristorbased dynamic behavior stability [10], and exponential Lagrange stability [11,12]. Additionally, in terms of the widespread application fields such as the visual optimization, image processing, language recognition, associative memory, and other fields, the stability of RNNs has become an indispensable dynamical behavior characteristic which must be further considered.…”

Section: Introductionmentioning

confidence: 99%

“…Taking [13] as an example, a natural question is raised and deeply explored: how much the interference intensity can a disturbed system bear to realize the stability again on the basis of its original stability, which implies the exact connotation of the robustness studied in this paper. In addition, the emerging literatures have also indirectly confirmed that various generalized stability behaviors of the neural networks are inevitably and immensely subject to the category and quantity of disturbances, for instance, time delays [1][2][3][4][5][6][7][8], stochastic disturbances [8,13,15], parameter disturbances [9], piecewise constant arguments [14], neutral terms [22], and Markov switching [12,23]. Hence, the subsequent perturbations will be attached to RNNs to further examine and guarantee the robustness of global exponential stability (RoGES) of RNNs.…”

Section: Introductionmentioning

confidence: 99%

Further Results on Exponentially Robust Stability of Uncertain Connection Weights of Neutral-Type Recurrent Neural Networks

Xie

2021

Complexity

View full text Add to dashboard Cite

Further results on the robustness of the global exponential stability of recurrent neural network with piecewise constant arguments and neutral terms (NPRNN) subject to uncertain connection weights are presented in this paper. Estimating the upper bounds of the two categories of interference factors and establishing a measuring mechanism for uncertain dual connection weights are the core tasks and challenges. Hence, on the one hand, the new sufficient criteria for the upper bounds of neutral terms and piecewise arguments to guarantee the global exponential stability of NPRNN are provided. On the other hand, the allowed enclosed region of dual connection weights is characterized by a four-variable transcendental equation based on the preceding stable NPRNN. In this way, two interference factors and dual uncertain connection weights are mutually restricted in the model of parameter-uncertainty NPRNN, which leads to a dynamic evolution relationship. Finally, the numerical simulation comparisons with stable and unstable cases are provided to verify the effectiveness of the deduced results.

show abstract

“…Chen et al [28] proposed a nonsingular terminal sliding mode control algorithm to implement accurate and robust body position trajectory tracking of six-legged robots. Xiong et al [29] constructed a novel integral sliding mode surface to guarantee the synchronization error convergence to zero in finite time. However, the traditional SMC has the problem of chattering in the control signal which is undesirable [30].…”

Section: Introductionmentioning

confidence: 99%

Research of Robust Trajectory Tracking Control and Attenuated Chattering: Application on Quadrotor

Zhou

Yao

Liu

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In this paper, we presented a strategy for accurate trajectory tracking control of a quadrotor with unknown disturbances. To guarantee that the tracking errors of all system state variables converge to zero in finite time and eliminate the chattering phenomenon caused by the switching control action, a control strategy that combines linear prediction model of disturbances and fuzzy sliding mode control (SMC) based on logical framework with side conditions (LFSC) was designed. LFSC was applied for both position and attitude tracking of the quadrotor. Firstly, a linear prediction method was devised to minimize the effects of external disturbances. Secondly, a new fuzzy law was implemented to eliminate the chattering phenomenon. In addition, the stabilities of position and attitude were demonstrated by using Lyapunov theory, respectively. Simulation results and comprehensive comparisons demonstrated the superior performance and robustness of the proposed LFSC scheme in the case of external disturbances.

show abstract

Improved Sliding Mode Control for Finite-Time Synchronization of Nonidentical Delayed Recurrent Neural Networks

Cited by 47 publications

References 60 publications

High-precision motion control method for permanent magnet linear synchronous motor

High-precision motion control method for permanent magnet linear synchronous motor

Further Results on Exponentially Robust Stability of Uncertain Connection Weights of Neutral-Type Recurrent Neural Networks

Research of Robust Trajectory Tracking Control and Attenuated Chattering: Application on Quadrotor

Contact Info

Product

Resources

About