General decay projective synchronization of memristive competitive neural networks via nonlinear controller

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Malika Sader ◽  
Fuyong Wang ◽  
Zhongxin Liu ◽  
Zengqiang Chen
Keyword(s):  
Neural Networks ◽  
General Decay ◽  
Projective Synchronization ◽  
Communication Security ◽  
Activation Functions ◽  
Nonlinear Feedback ◽  
Nonlinear Controller ◽  
Control Scheme ◽  
Special Cases ◽  
Inequality Techniques

Abstract In this paper, the general decay projective synchronization of a class of memristive competitive neural networks with time delay is studied. Firstly, a nonlinear feedback controller is designed, which does not require any knowledge about the activation functions. Then, some new and applicable conditions dependent on the Lyapunov function and the inequality techniques are obtained to guarantee the general decay projective synchronization of the considered systems under the developed controller. Unlike other forms of synchronization, projective synchronization can improve communication security due to the scaling constant’s unpredictability. In addition, the polynomial synchronization, asymptotical synchronization, and exponential synchronization can be seen as the special cases of the general decay projective synchronization. Finally, a numerical example is given to demonstrate the effectiveness of the proposed control scheme.

General Decay Synchronization for Fuzzy Cellular Neural Networks with Time-Varying Delays

2019 ◽  
Vol 20 (5) ◽  
pp. 551-560 ◽  
Author(s):  
Ahmadjan Muhammadhaji ◽  
Abdujelil Abdurahman
Keyword(s):  
Neural Networks ◽  
Numerical Simulations ◽  
Cellular Neural Networks ◽  
General Decay ◽  
Time Varying ◽  
Fuzzy Cellular Neural Networks ◽  
Activation Functions ◽  
General Activation ◽  
Inequality Techniques ◽  
Theoretical Results

AbstractThis paper studies the general decay synchronization (GDS) of a class of fuzzy cellular neural networks (FCNNs) with general activation functions and time-varying delays. By introducing suitable Lyapunov-Krasovskii functionals and employing useful inequality techniques, some novel criteria ensuring the GDS of considered FCNNs are established via a type of nonlinear control. In addition, two examples with numerical simulations are presented to illustrate the obtained theoretical results.

scholarly journals Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Xuefei Wu ◽  
Chen Xu ◽  
Jianwen Feng ◽  
Yi Zhao ◽  
Xuan Zhou
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Lyapunov Stability Theory ◽  
Projective Synchronization ◽  
Nonlinear Coupling ◽  
Nonlinear Feedback ◽  
Nonlinear Controller ◽  
Feedback Controllers ◽  
Mixed Time Delays ◽  
Generalized Projective Synchronization

The generalized projective synchronization (GPS) between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such neural networks. Some results for GPS of these neural networks are proved theoretically by using the Lyapunov stability theory and the LaSalle invariance principle. Moreover, by comparison, we determine an optimal nonlinear controller from several ones and provide an adaptive update law for it. Computer simulations are provided to show the effectiveness and feasibility of the proposed methods.

Real-Time Planning and Nonlinear Control for Quadrupedal Locomotion With Articulated Tails

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Randall T. Fawcett ◽  
Abhishek Pandala ◽  
Jeeseop Kim ◽  
Kaveh Akbari Hamed
Keyword(s):  
Real Time ◽  
Degrees Of Freedom ◽  
Center Of Mass ◽  
Hierarchical Control ◽  
Nonlinear Feedback ◽  
Nonlinear Controller ◽  
External Disturbances ◽  
Quadrupedal Locomotion ◽  
Control Scheme ◽  
High Level

Abstract The primary goal of this paper is to develop a formal foundation to design nonlinear feedback control algorithms that intrinsically couple legged robots with bio-inspired tails for robust locomotion in the presence of external disturbances. We present a hierarchical control scheme in which a high-level and real-time path planner, based on an event-based model predictive control (MPC), computes the optimal motion of the center of mass (COM) and tail trajectories. The MPC framework is developed for an innovative reduced-order linear inverted pendulum (LIP) model that is augmented with the tail dynamics. At the lower level of the control scheme, a nonlinear controller is implemented through the use of quadratic programming (QP) and virtual constraints to force the full-order dynamical model to track the prescribed optimal trajectories of the COM and tail while maintaining feasible ground reaction forces at the leg ends. The potential of the analytical results is numerically verified on a full-order simulation model of a quadrupedal robot augmented with a tail with a total of 20 degrees-of-freedom. The numerical studies demonstrate that the proposed control scheme coupled with the tail dynamics can significantly reduce the effect of external disturbances during quadrupedal locomotion.

Improved Control Schemes for Projective Synchronization of Delayed Neural Networks with Unmatched Coefficients

2019 ◽  
Vol 34 (03) ◽  
pp. 2051005 ◽  
Author(s):  
Abdujelil Abdurahman ◽  
Haijun Jiang
Keyword(s):  
Neural Networks ◽  
Time Delays ◽  
Chaos Synchronization ◽  
Projective Synchronization ◽  
Chaotic Neural Networks ◽  
Delayed Neural Networks ◽  
Mixed Time Delays ◽  
Control Schemes ◽  
Master System ◽  
Inequality Techniques

Projective synchronization (PS) is a type of chaos synchronization where the states of slave system are scaled replicas of the states of master system. This paper studies the asymptotic projective synchronization (APS) between master–slave chaotic neural networks (NNs) with mixed time-delays and unmatched coefficients. Based on useful inequality techniques and constructing a suitable Lyapunov functional, some simple criteria are derived to ensure the APS of considered networks via designing a novel adaptive feedback controller. In addition, a numerical example and its MATLAB simulations are provided to check the feasibility of the obtained results. The main innovation of our work is that we dealt with the APS problem between two different chaotic NNs, while most of the existing works only concerned with the PS of chaotic systems with the same topologies. In addition, compared with the controllers introduced in the existing papers, the designed controller in this paper does not require any knowledge about the activation functions, which can be seen as another novelty of the paper.

SYNCHRONIZATION IN AN ARRAY OF NONLINEARLY COUPLED CHAOTIC NEURAL NETWORKS WITH DELAY COUPLING

2008 ◽  
Vol 18 (10) ◽  
pp. 3101-3111 ◽  
Author(s):  
JIANQUAN LU ◽  
DANIEL W. C. HO ◽  
JINDE CAO
Keyword(s):  
Neural Networks ◽  
Nearest Neighbor ◽  
Chaotic Neural Networks ◽  
Scale Free ◽  
Dynamical Network ◽  
Feedback Strength ◽  
Control Scheme ◽  
Special Cases ◽  
General Complex ◽  
Free Network

A general complex dynamical network consisting of N nonlinearly coupled identical chaotic neural networks with coupling delays is firstly formulated. Many studied models with coupling systems are special cases of this model. Synchronization in such dynamical network is considered. Based on the Lyapunov–Krasovskii stability theorem, some simple controllers with updated feedback strength are introduced to make the network synchronized. The update gain γi can be properly chosen to make some important nodes synchronized quicker or slower than the rest. Two examples including nearest-neighbor coupled networks and scale-free network are given to verify the validity and effectiveness of the proposed control scheme.

scholarly journals Sliding Intermittent Control for BAM Neural Networks with Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Jianqiang Hu ◽  
Jinling Liang ◽  
Hamid Reza Karimi ◽  
Jinde Cao
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Impulsive Control ◽  
Intermittent Control ◽  
Bam Neural Networks ◽  
Periodically Intermittent Control ◽  
Control Scheme ◽  
Special Cases ◽  
Exponentially Stable ◽  
Continuous Feedback

This paper addresses the exponential stability problem for a class of delayed bidirectional associative memory (BAM) neural networks with delays. A sliding intermittent controller which takes the advantages of the periodically intermittent control idea and the impulsive control scheme is proposed and employed to the delayed BAM system. With the adjustable parameter taking different particular values, such a sliding intermittent control method can comprise several kinds of control schemes as special cases, such as the continuous feedback control, the impulsive control, the periodically intermittent control, and the semi-impulsive control. By using analysis techniques and the Lyapunov function methods, some sufficient criteria are derived for the closed-loop delayed BAM neural networks to be globally exponentially stable. Finally, two illustrative examples are given to show the effectiveness of the proposed control scheme and the obtained theoretical results.

scholarly journals Periodic Solution for Impulsive Cellar Neural Networks with Time-Varying Delays in the Leakage Terms

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Bingwen Liu ◽  
Shuhua Gong
Keyword(s):  
Neural Networks ◽  
Periodic Solution ◽  
Differential Inequality ◽  
Lyapunov Functional ◽  
Sufficient Conditions ◽  
Functional Method ◽  
Time Varying ◽  
Activation Functions ◽  
Lyapunov Functional Method ◽  
Inequality Techniques

This paper is concerned with impulsive cellular neural networks with time-varying delays in leakage terms. Without assuming bounded and monotone conditions on activation functions, we establish sufficient conditions on existence and exponential stability of periodic solutions by using Lyapunov functional method and differential inequality techniques. Our results are complement to some recent ones.

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