The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained accor...The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained according to the identification procedures in the open-loop, or in the closed-loop. In the open-loop, the identification methods are well known and offer good process approximation, which is not valid for the closed-loop identification, when the system provides the feedback output and doesn’t permit it to be identified in the open-loop. This paper offers an approach for experimental identification in the closed-loop, which supposes the approximation of the process with inertial models, with or without time delay and astatism. The coefficients are calculated based on the values of the critical transfer coefficient and period of the underdamped response of the closed-loop system with P controller, when system achieves the limit of stability. Finally, the closed-loop identification was verified by the computer simulation and the obtained results demonstrated, that the identification procedure in the closed-loop offers good results in process of estimation of the model of the process.展开更多
This paper is concerned with inertial bidirectional associative memory neural networks with mixed delays and impulsive effects.New and practical conditions are given to study the existence,uniqueness,and global expone...This paper is concerned with inertial bidirectional associative memory neural networks with mixed delays and impulsive effects.New and practical conditions are given to study the existence,uniqueness,and global exponential stability of anti-periodic solutions for the suggested system.We use differential inequality techniques to prove our main results.Finally,we give an illustrative example to demonstrate the effectiveness of our new results.展开更多
This paper,mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients.By combining Lyapunov function method with differential inequality approach,non-redu...This paper,mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients.By combining Lyapunov function method with differential inequality approach,non-reduced order method is used to establish some novel assertions on the existence and generalized exponential stability of periodic solutions for the addressed model.In addition,an example and its numerical simulations are given to support the proposed approach.展开更多
The robust stability problem of uncertain inertial neural networks with impulsive effects and distributed-delay is considered in the present paper.The average impulsive interval and differential inequality for delay d...The robust stability problem of uncertain inertial neural networks with impulsive effects and distributed-delay is considered in the present paper.The average impulsive interval and differential inequality for delay differential equations are used to obtain the global exponential stability of the inertial neural networks.The robust distributed-delaydependent stability criteria here are proposed in terms of both linear matrix inequalities and algebraic inequalities.Our results can not only be used to obtain the stability of the uncertain inertial neural network with impulsive disturbance,but also be utilized to design the impulsive control for the uncertain inertial neural networks.The novel criteria complement and extend the previous works on uncertain inertial neural network with/without impulsive effects.Typical numerical examples are used to test the validity of the developed stability criteria finally.展开更多
文摘The mathematical model that approximates the dynamics of the industrial process is essential for the efficient synthesis of control algorithms in industrial applications. The model of the process can be obtained according to the identification procedures in the open-loop, or in the closed-loop. In the open-loop, the identification methods are well known and offer good process approximation, which is not valid for the closed-loop identification, when the system provides the feedback output and doesn’t permit it to be identified in the open-loop. This paper offers an approach for experimental identification in the closed-loop, which supposes the approximation of the process with inertial models, with or without time delay and astatism. The coefficients are calculated based on the values of the critical transfer coefficient and period of the underdamped response of the closed-loop system with P controller, when system achieves the limit of stability. Finally, the closed-loop identification was verified by the computer simulation and the obtained results demonstrated, that the identification procedure in the closed-loop offers good results in process of estimation of the model of the process.
文摘This paper is concerned with inertial bidirectional associative memory neural networks with mixed delays and impulsive effects.New and practical conditions are given to study the existence,uniqueness,and global exponential stability of anti-periodic solutions for the suggested system.We use differential inequality techniques to prove our main results.Finally,we give an illustrative example to demonstrate the effectiveness of our new results.
基金the National Natural Science Foundation of China(Nos.71471020 and 51839002)Hunan Provincial Natural Science Foundation of China(No.2016.J.J1001)Scientific Research Fund of Hunan Provincial Education Department(No.15A003).
文摘This paper,mainly explores a class of non-autonomous inertial neural networks with proportional delays and time-varying coefficients.By combining Lyapunov function method with differential inequality approach,non-reduced order method is used to establish some novel assertions on the existence and generalized exponential stability of periodic solutions for the addressed model.In addition,an example and its numerical simulations are given to support the proposed approach.
基金National Natural Science Foundation of China(Nos.11771374,11471089,U1804158 and 61503175)Tackle key problem project in science and technology of Henan Province(Nos.172102210407 and 182102310955)+1 种基金the Program for Science & Technology Innovation Research Team in Universities of Henan Provience(No.18IRTSTHN014)Key scientific research projects in Henan colleges and universities(No.18A110026).
文摘The robust stability problem of uncertain inertial neural networks with impulsive effects and distributed-delay is considered in the present paper.The average impulsive interval and differential inequality for delay differential equations are used to obtain the global exponential stability of the inertial neural networks.The robust distributed-delaydependent stability criteria here are proposed in terms of both linear matrix inequalities and algebraic inequalities.Our results can not only be used to obtain the stability of the uncertain inertial neural network with impulsive disturbance,but also be utilized to design the impulsive control for the uncertain inertial neural networks.The novel criteria complement and extend the previous works on uncertain inertial neural network with/without impulsive effects.Typical numerical examples are used to test the validity of the developed stability criteria finally.
