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Lei J.,Yunnan Nationalities University | Lei J.,Key Laboratory in Software Engineering of Yunnan Province
Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

The problem of optimal sampled-data vibration control for nonlinear systems with time delays and uncertainties is considered. With the purpose of simplifying the nonlinear optimal vibration control (NOVC) design, the original time-delay sampled-data system is converted into a discrete-time nondelayed system first, as well as the nonlinear and uncertain terms are treated as external excitations. Therefore, the design procedure for NOVC law is reduced and the successive approximation approach is sequentially developed in it. The obtained NOVC law is derived from a Riccati equation, a Stein equation, and sequences of adjoint vector difference equations. It is combined with a feedforward term, the nonlinearity and uncertainty compensator terms, and some control memory terms, which compensate for the effects produced by the disturbance, the nonlinearity and uncertainties, and the time delays. Moreover, the existence and uniqueness of NOVC law are proved and the stability of the closed-loop system is analyzed. In order to make the controller physically realizable, an observer is constructed and the corresponding dynamical control law is given. Furthermore, by this means, the NOVC law for a sampled-data quarter-car suspension model with actuator and sensor delays is designed. The results of numerical simulations illustrate that the NOVC gives satisfactory conclusions in effectiveness of suspension performance responses and feasibility of the proposed design approach. © VC 2012 by ASME. Source

Jiang Z.,Yunnan University | Jiang Z.,Key Laboratory in Software Engineering of Yunnan Province | Jiang Z.-J.,China Telecom | Fu Z.-T.,Yunnan University
Future Information Engineering and Manufacturing Science - Proceedings of the 2014 International Conference on Future Information Engineering and Manufacturing Science, FIEMS 2014

An Object-Oriented Software Evolution Process Meta-Model (OO-EPMM), formal OCL constraint of meta-model and a software evolution process model are presented in this paper. OO-EPMM can not only represent software development process, but also represent software evolution. © 2015 Taylor & Francis Group, London. Source

Li Y.-L.,Xiamen University | Lei J.,Yunnan Nationalities University | Lei J.,Key Laboratory in Software Engineering of Yunnan Province | Wang J.,Yunnan Nationalities University
International Journal of Distributed Sensor Networks

The optimal distributed tracking control algorithms over nonlinear cooperative wireless sensor networks (WSNs) are presented in this paper. In order to solve transfer delay and packet loss problem, the architecture of wireless active sensor (WAS) is employed, where a state estimator is embedded, which can provide the needed state information. Furthermore, the optimal distributed tracking control algorithm is proposed. By solving the matrix equations and the adjoint difference equations, the optimal control law can be obtained easily, in which an increment integral regulator is designed to implement tracking target without steady-state error and a nonlinearity compensation term is designed to compensate for the effect produced by system nonlinearity. Moreover, the observer-based dynamical algorithm is given considering the physically unrealizable disturbance states and the unavailable sensor states. Finally, computer simulations are carried out with application to two nonlinear pendulums, which prove that the algorithm is effective and easy to implement, and the system achieves the desired performance based on tradeoff between tracking error and control energy consumption. © 2013 Ya-Li Li et al. Source

Li J.,Yunnan University | Li J.,Key Laboratory in Software Engineering of Yunnan Province | Yue K.,Yunnan University | Liu W.-Y.,Yunnan University
Tien Tzu Hsueh Pao/Acta Electronica Sinica

A new coverage optimization problem named disjoint set k-cover for fusion-based coverage of WSN is investigated in this paper where sensor nodes are assumed using a fusion-based collective probabilistic sensor model. First, the problem is formulated as a fusion-based coverage game and then the game is proved as a potential game. So that the optimal solution is a pure Nash equilibrium. Second, we present the conditions that determine the independence of coverage utility among sensor nodes. Furthermore, two distributed algorithms only based on local information are proposed and proven to be convergent to pure Nash equlibria. Finally, experimental results show that Nash equilibria can provide a near-optimal and well-balanced solution to the problem. Source

Zhang C.,Yunnan University | Li J.,Yunnan University | Li J.,Key Laboratory in Software Engineering of Yunnan Province | Zhu M.,Yunnan University | Yang Z.,Yunnan University
Proceedings - 2011 7th International Conference on Natural Computation, ICNC 2011

A mobile sensor network (MSN) is composed of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. The locomotion capability of nodes makes mobile sensor networks able to complete various tasks, such as surveillancing over some mission space. In this paper we consider a class of MSN applications which have a limited number of sensors randomly deployed, in a task area, with different coverage values at different positions. The sensors' task is to relocate themselves from initial positions to optimal positions so as to maximize the total values. Towards this task, a distributed relocation protocol, based on a potential game approach, is proposed. Each sensor moves to a better sensor position constrained by the locomotion energy consumption in response to other sensors locomotion. The dynamics will finally converge to an equilibrium point, where no better position can be obtained by deviating from this point, within finite steps. Simulation results show that a good coverage performance can be obtained using our proposed distributed protocol. © 2011 IEEE. Source

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