title: Optimal Communication Network-Based H∞ Quantized Control With Packet Dropouts for a Class of Discrete-Time Neural Networks With Distributed Time Delay.
abstract: This paper is concerned with optimal communication network-based H∞ quantized control for a discrete-time neural network with distributed time delay. Control of the neural network (plant) is implemented via a communication network. Both quantization and communication network-induced data packet dropouts are considered simultaneously. It is assumed that the plant state signal is quantized by a logarithmic quantizer before transmission, and communication network-induced packet dropouts can be described by a Bernoulli distributed white sequence. A new approach is developed such that controller design can be reduced to the feasibility of linear matrix inequalities, and a desired optimal control gain can be derived in an explicit expression. It is worth pointing out that some new techniques based on a new sector-like expression of quantization errors, and the singular value decomposition of a matrix are developed and employed in the derivation of main results. An illustrative example is presented to show the effectiveness of the obtained results.
authors.forename: Qing-Long
authors.initials: QL
authors.lastname: Han
authors.forename: Yurong
authors.initials: Y
authors.lastname: Liu
authors.forename: Fuwen
authors.initials: F
authors.lastname: Yang