Guan Zhi-Hong

An Adaptive Particle Swarm Optimization Algorithm and Simulation

To overcome premature searching by standard particle swarm optimization (PSO) algorithm for the large lost in population diversity, the measure of population diversity and its calculation are given, and an adaptive PSO with dynamically changing inertia weight is proposed. Simulation results show that the adaptive PSO not only effectively alleviates the problem of premature convergence, but also has fast convergence speed for balancing the trade-off between exploration and exploitation.

Controlling bifurcations and chaos in discrete small-world networks

We propose an impulsive hybrid control method to control the period-doubling bifurcations and stabilize unstable periodic orbits embedded in a chaotic attractor of a small-world network. Simulation results show that the bifurcations can be delayed or completely eliminated. A periodic orbit of the system can be controlled to any desired periodic orbit by using this method.

Tracking and disturbance rejection for networked feedback systems under control energy constraint

The optimal tracking and disturbance rejection problem for networked feedback systems is studied in this paper. If there is a disturbance from the sensor to the controller, the performance is to be minimized jointly with the controller design. Two degree of freedom controller is designed to control the system. The reference signal is a step signal. The best performance index for considering tracking limitation is given, which showed the fundamental design limitation and how to avoid the disturbance. The performance limitation is tightly dependent on the nonminimum phase zeros, and the plant gain in the frequency range etc.

Group Motion Control of Multi-agent Systems Based on Complex Network

Multi-agent systems (MAS) are, nowadays, an important research area within Robotics and Artificial Intelligence. Motivated by recent advances in synchronization theory of complex dynamical network, this paper develops a general methods of controller design for formation reaching and group motion controlling of the MAS. A novel motion model of MAS is presented at first.Based on this model, the design method of decentralized controller for each agent is investigated. The stability properties of the system is also analyzed in detail.In this control scheme the topology of the control interconnections is fixed and invariant.The control policy ensures that all agents eventually move in group formation with desired group motion dynamics while at same time avoid collisions.In the end,the validity of this scheme is supported by computer simulation.

The Chaotification of Discrete Hopfield Neural Networks via Impulsive Control

The chaotification of discrete Hopfield neural networks is studied with impulsive control techniques. No matter whether the original systems are stable or not, chaotification theorems for discrete Hopfield neural networks are derived, respectively. Finally, the effectiveness of the theoretical results is illustrated by some numerical examples.

Stability and Neimark?Sacker bifurcation analysis of a food-limited population model with a time delay

In this paper, a kind of discrete delay food-limited model obtained by the Euler method is investigated, where the discrete delay τ is regarded as a parameter. By analyzing the associated characteristic equation, the linear stability of this model is studied. It is shown that Neimark—Sacker bifurcation occurs when τ crosses certain critical values. The explicit formulae which determine the stability, direction, and other properties of bifurcating periodic solution are derived by means of the theory of center manifold and normal form. Finally, numerical simulations are performed to verify the analytical results.

Optimal Capacity Allocation on Heterogeneous Complex Transport Networks

We study how the node delivering capacity is allocated so that the traffic transport efficiency can be enhanced maximally. Network heterogeneity of degree distribution and processing delay of the traffic are considered. An explicit analytical solution is provided, which is based on the M/M/1 queueing theory and optimization principle, provided that the network structure and routing strategy are given. In particular, we extend the relevant conclusions in the literature [Europhys. Lett. 83 (2008) 28001]. Finally, an order parameter simulation example by comparing results with those obtained via simple capacity allocation in large Barabasi—Albert (BA) scale-free network is provided to illustrate the effectiveness of the theoretical results.

Chaotification for a class of nonlinear systems

More and more attention has been focused on effectively generating chaos via simple physical devices. The problem of creating chaotic attractors is considered for a class of nonlinear systems with backlash function in this paper. By utilizing the Silnikov heteroclinic and homoclinic theorems, some sufficient conditions are established to guarantee that the nonlinear system has horseshoe-type chaos. Examples and simulations are given to verify the effectiveness of the theoretical results.

Decentralized impulsive control for a class of uncertain interconnected systems

A great deal of stabilization criteria has been obtained from study of stabilizing interconnected systems. The results obtained are usually based on continuous systems by state feedback. In this paper, decentralized impulsive control is presented to stabilize a class of uncertain interconnected systems based on Lyapunov theory. The system under consideration involves parameter uncertainties and unknown nonlinear interactions among subsystems. Some new criteria of stabilization under impulsive control are established. Two numerical examples are offered to prove the effectiveness and practicality of the proposed method.

On Multi-population Parallel Particle Swarm Optimization Algorithm

To improve performance of particle swarm optimization (PSO) algorithm and avoid trapping to local minima, a multi-population parallel particle swarm optimization (DPPSO) algorithm is proposed. In the algorithm, sub populations are divided into exploration and exploitation types. The global version PSO is used in the exploration population to enhance ability of exploring the best individual, and the local version PSO is used in the exploitation population to enhance ability of local search and find the best global result in the local range. Simultaneously, keep communication with sub populations in running. The experimental results show that the restraining premature convergence is enhanced for maintaining the individual diversity.