Jae Man Kim

Non-fragile Guaranteed Cost Control of Uncertain Nonlinear Systems with Time-varying Delays in State and Control Input

In this paper, we present a non-fragile guaranteed cost control design method for uncertain nonlinear systems with time varying delays in state and control input, even though the controller gain is perturbed. The uncertain nonlinear term in the systems is norm bounded and the linear matrix inequality(LMI) optimization method is employed as a stability analysis of the systems. We design a robust controller and show the asymptotical stability of uncertain time-varying systems based on Lyapunov method. Also, we guarantee a specific level of performance of the systems. The simulations are carried out to demonstrate the effectiveness of the proposed method.

Stochastic observer based H ∞ control of networked systems with packet dropouts

In this paper, the stochastic observer based H∞ control problem is investigated for the networked control systems(NCS) with packet dropouts. Packet dropout is modeled by Bernoulli probability sequence, which presents transmitted data through the network channel. Based on the existence of the packet dropout, the appropriate Lyapunov-Krasovskii functional and the linear matrix inequality(LMI) method derive the the stability condition and the H∞ norm constraint for NCS. An example demonstrates the effective of the proposed method.

Non-fragile guaranteed cost control of time-delayed uncertain systems

In this paper, we present a non-fragile guaranteed cost control design method for uncertain systems with time- varying delays in state and control input, even though the controller gain is perturbed. The uncertain nonlinear term in the systems is norm bounded and the linear matrix inequality(LMI) optimization method is employed as a stability analysis of the systems. We design a robust controller and show the asymptotical stability of uncertain time-varying systems based on Lyapunov method. Also, we guarantee a specific level of performance of the systems. The simulations are carried out to demonstrate the effectiveness of the proposed method.

Adaptive Wavelet Neural Network Controller for AQM Router in TCP Network

This paper presents a wavelet neural network (WNN) controller based on adaptive learning rates (ALRs) method, for active queue management(AQM) in end-to-end TCP network. The AQM is important to regulate the queue length and short round trip time in TCP network. The WNN controller using ALRs adaptively controls the dropping probability of the TCP network. Also the proposed controller is intelligently trained by GD algorithm. The parameters of WNN are tuned by ALRs method. We apply Lyapunov theorem to verify the stability of WNN controller using ALRs. The simulation results show that the performance of WNN controller using ALRs is superior to that of WNN controller using fixed learning rates.

Cluster consensus for heterogeneous multi-agent systems

This paper investigates the cluster consensus problem of heterogeneous multi-agent systems, where the considered systems are composed of first-order dynamics and second-order dynamics. The consensus protocol is designed to converge the position and velocity states of the heterogeneous multi-agent systems in two separate location. Stability analysis for heterogeneous multi-agent systems is presented based on Lyapunov function. Finally, some simulation results are provided to verify the effectiveness of the proposed methods.

Group Average-consensus and Group Formation-consensus for First-order Multi-agent Systems

This paper investigates the group average-consensus and group formation-consensus problems for first-order multi-agent systems. The control protocol for group consensus is designed by considering the positive adjacency elements. Since each intra-group Laplacian matrix cannot be satisfied with the in-degree balance because of the positive adjacency elements between groups, we decompose the Laplacian matrix into an intra-group Laplacian matrix and an inter-group Laplacian matrix. Moreover, average matrices are used in the control protocol to analyze the stability of multi-agent systems with a fixed and undirected communication topology. Using the graph theory and the Lyapunov functional, stability analysis is performed for group average-consensus and group formation-consensus, respectively. Finally, some simulation results are presented to validate the effectiveness of the proposed control protocol for group consensus.

Stable AQM Algorithm Based on AdaptiveWavelet Neural Network Controller

In this paper, we present the adaptive wavelet neural network (WNN) controller, which is trained by adaptive learning rates (ALRs), as an active queue management(AQM) in end-to-end TCP network. In TCP network, AQM is important to regulate the queue length and short round trip time by passing or dropping the packets at the intermediate routers. RED and PI algorithms have been used for AQM formerly. But these algorithms show weakness in the detection and control of the congestion under dynamically changing network situations. In our method, the WNN controller using ALRs is designed to overcome the problems, which adaptively controls the dropping probability of the TCP network and is trained by gradient-descent algorithm. We apply Lyapunov theorem to verify the stability of our proposed WNN controller. The simulation results show that the performance of WNN controller using ALRs is superior to that of PI controller