Baotong Cui

Exponential stability of genetic regulatory networks with random delays

This paper on global exponential stability in the mean square sense of genetic regulatory networks (GRNs) is motivated by a practical consideration that different genes have different time delays for transcription and translation, and in some cases, each multimer is assigned to a randomly chosen gene promoter site as an activator or inhibitor. One important feature of the obtained results reported here is that the time-varying delays are assumed to be random and their probability distributions are known a priori. By employing the information of the probability distributions of the time delays, we present some stability criteria for the uncertain delayed genetic networks with SUM regulatory logic where each transcription factor acts additively to regulate a gene. The effects of both variation range and distribution probability of the time delays are taken into account in the proposed approach. Another feature of the results is that a novel Lyapunov functional dependence on auxiliary delay parameters is exploited, which renders the results to be potentially less conservative and allows the time-varying delays to be not differentiable. The theoretical findings are illustrated and verified with two examples.

Parameter-dependent robust stability of uncertain neural networks with time-varying delay

Abstract This paper is concerned with the problem of global robust asymptotic stability for delayed neural networks with polytopic parameter uncertainties and time-varying delay. A delay-dependent and parameter-dependent robust stability criterion for the equilibrium of delayed neural networks in the face of polytopic type uncertainties is presented by using a parameter-dependent Lyapunov functional and taking the relationship between the terms in the Leibniz–Newton formula into account. This criterion, expressed as a set of linear matrix inequalities, requires no matrix variable to be fixed for the entire uncertainty polytope, which produces a less conservative stability result.

A novel neural network for solving convex quadratic programming problems subject to equality and inequality constraints

This paper proposes a neural network model for solving convex quadratic programming (CQP) problems, whose equilibrium points coincide with Karush-Kuhn-Tucker (KKT) points of the CQP problem. Using the equality transformation and Fischer-Burmeister (FB) function, we construct the neural network model and present the KKT condition for the CQP problem. In contrast to two existing neural networks for solving such problems, the proposed neural network has fewer variables and neurons, which makes circuit realization easier. Moreover, the proposed neural network is asymptotically stable in the sense of Lyapunov such that it converges to an exact optimal solution of the CQP problem. Simulation results are provided to show the feasibility and efficiency of the proposed network.

Containment control of second-order multi-agent systems with directed topology and time-delays

Purpose – The purpose of this paper is to discuss the impacts of the communication time-delays to the distributed containment control of the second-order multi-agent systems with directed topology. Design/methodology/approach – A basic theoretical analysis is first carried out for the containment control of the second-order multi-agent systems under directed topology without communication time-delay and a sufficient condition is proposed for the achievement of containment control. Based on the above result and frequency-domain analysis method, a sufficient condition is also derived for the achievement of containment control of the second-order multi-agent systems under directed topology with communication time-delays. Finally, simulation results are presented to support the effectiveness of the theoretical results. Findings – For the achievement of containment control of the second-order multi-agent systems under directed topology with communication time-delay, the control gain in the control protocols is...

Observer-based synchronization of networked distributed parameter systems

Synchronization in networks of dynamical systems is of importance in biological, chemical, physical and social systems. This paper investigates an observer-based synchronization scheme for a class of networked distributed parameter systems under an abstract framework. As in reality the system states may not be available and different subsystems (agents) share information through a communication network, we estimate the states based on a distributed observer in the case of partial network connectivity. Then a synchronizing controller combining a state feedback is constructed and the well-posedness of the closed-loop system is examined. Numerical simulations are provided to illustrate the effectiveness of the proposed results.

Improved Control of Distributed Parameter Systems with Time-Varying Delay Based on Mobile Actuator-Sensor Networks

Abstract This paper investigates the control problem of distributed parameter systems (DPS) with time-varying delay by employing mobile actuator-sensor networks. It is assumed that each agent in the networks has a sensor device which can measure spatial state, and an actuator device that can dispense control signals to spatially distributed process and can communicate with its neighbors. To better control the DPS with time-varying delay, the strategy how to navigate the agents is considered. By constructing Lyapunov functionals and using inequality analysis, criterias for stability of the DPS with time-varying delays are derived. Meanwhile, the guidance scheme of every agent with augmented vehicle dynamics is derived. Simulation results show that such mobile actuator-sensor networks can improve the control performance of the DPS with time delay.

Global dissipativity of Cohen-Grossberg neural networks with mixed delays

This paper is concerned with global dissipativity of a general class of Cohen-Grossberg neural networks with both discrete time-varying delays and distributed time-varying delays. Based on the Lyapunov method, linear matrix inequality approach and some inequality techniques, some sufficient conditions are presented for checking the global dissipativity for Cohen- Grossberg neural networks with mixed time-varying delays, and characterizing the sets of global dissipativity and global exponentially dissipativity. Finally, some numerical simulations are given to show the effectiveness and feasibility of the results.

Event-triggered H ∞ filtering control for a class of distributed parameter systems with Markovian switching topology

Abstract The H∞ filtering problem for distributed parameter systems with stochastic switching topology is investigated in this paper based on event-triggered control scheme. The switching topology which subjects to a Markovian chain is considered in filter design because of the communication uncertainty of practical networks. An event-triggered mechanism as a sampling scheme is developed aiming at the benefit of reducing the computation load or saving the limited network resources. Based on some novel integral inequalities, the improved delayed method is proposed for the H∞ filtering control problem with event-triggered scheme. Moreover, by employing stochastic stability theory, filters with Markovian jump parameters are designed to guarantee that the stochastically mean square stability and H∞ performance of the underlying error system. Finally, in order to illustrate the applicability of the obtained results, numerical examples are presented.

Robust sampled-data control of a class of distributed parameter systems using mobile actuator-sensor networks

This paper study the sampled-data control problem of a class of distributed parameter systems. A novel sampled-data control scheme is presented using mobile actuator-sensor networks. By utilizing a Lyapunov functional which depend on spatial parameter, a controller combine to decentralized static output feedback control scheme and the point measurement of the mobile sensor is designed to derive several sufficient criteria ensuring the distributed parameter systems to be globally asymptotically stable. The criteria are given in the form of linear operator inequalities and the velocity law of each mobile actuator/sensor. It is also shown that static sampled-data control of distributed parameter systems is just a special case of our main results. A numerical simulations illustrate the effectiveness of the proposed control scheme in enhancing system performance.

A delay-decomposition approach to consensus of multi-agent network with time-varying delay

This paper mainly focuses on studying the consensus problems of second-order multi-agent networks. By employing a delay-decomposition approach and selecting available Lyapunov functions, some sufficient conditions about the consensus of multi-agent networks are proposed. Then the consensus problems of multi-agent networks with time-varying or constant delay can be solved by some linear matrix inequalities (LMIs). Finally, simulation results are given to support the theoretical results.