Abstract
This work investigates a decentralized state feedback scheme of neural network control for an interconnected system. The completely unknown associated terms are estimated directly by the neural structure. A modified approach is proposed to deal with the state feedback format. By combining the Lyapunov function and backstepping technology together, an adaptive decentralized controller is established, and we can construct the boundedness of all signals in the closed-loop structure through the controller, which can drive the formation of a given reference signal. In the end, the effectiveness of the presented strategy is referred to a simulation example.
Highlights
Interconnected systems are a race of large-scale systems, which contain some related subsystems
Some important achievements have been made in this field, especially the stabilization and tracking control issues, see [1,2,3,4,5,6,7]
In most of research studies, adaptive controllers were constructed in traditional backstepping design
Summary
Interconnected systems are a race of large-scale systems, which contain some related subsystems. In most of research studies, adaptive controllers were constructed in traditional backstepping design Whether this method can be directly applied to obtain controllers for stochastic and interconnected characteristics, and what improvements are needed are the issues to be discussed in this work. (1) When the completely unknown associated terms exist and the state variable information is unknown, a modified adaptive neural scheme is proposed to deal with the distributed large-scale systems. It is different from the above works that the whole system needs only one virtual control signal.
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