Abstract
In network control, signal transmission between each system component is carried out through the communication network. Since the bandwidth of the network is limited, quantization is a vital and fundamental technology used to convert the continuous signal to an approximate signal with a finite number of discrete value levels. Furthermore, event-triggering is an effective method to reduce signal transmission frequency in network control. Input signal quantization and event-triggering are considered simultaneously in this study for a class of underactuated systems. First, the logarithmic quantizer is used to quantize the input signal, and then the quantized input signal is further processed by the event-triggered mechanism based on the fixed threshold strategy. Adopting the proposed adaptive control scheme with the aid of radial basis function (RBF) neural network-based sliding mode control, the states of the closed-loop system are guaranteed to be bounded, and the control goals can be achieved. Finally, the control effect is shown through the numerical simulations.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Transactions of the Institute of Measurement and Control
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.