Unified stabilizing controller synthesis approach for discrete-time intelligent systems with time delays by dynamic output feedback

作者：Liu MeQin^*

来源：Science in China - Series F: Information Sciences , 2007, 50(4): 636-656.

DOI：10.1007/s11432-007-0043-4

• standard neural network model (SNNM)
• linear matrix inequality (LMI)
• intelligent system
• asymptotic stability
• output feedback control
• time delay
• discrete-time
• chaotic neural network
• Takagi and Sugeno (T-S) fuzzy model

A novel model, termed the standard neural network model (SNNM), is advanced to describe some delayed (or non-delayed) discrete-time intelligent systems composed of neural networks and Takagi and Sugeno (T-S) fuzzy models. The SNNM is composed of a discrete-time linear dynamic system and a bounded static nonlinear operator. Based on the global asymptotic stability analysis of the SNNMs, linear and nonlinear dynamic output feedback controllers are designed for the SNNMs to stabilize the closed-loop systems, respectively. The control design equations are shown to be a set of linear matrix inequalities (LMIs) which can be easily solved by various convex optimization algorithms to determine the control signals. Most neural-network-based (or fuzzy) discrete-time intelligent systems with time delays or without time delays can be transformed into the SNNMs for controller synthesis in a unified way. Three application examples show that the SNNMs not only make controller synthesis of neural-network-based (or fuzzy) discrete-time intelligent systems much easier, but also provide a new approach to the synthesis of the controllers for the other type of nonlinear systems.

• 出版日期2007-8
• 单位浙江大学