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When a product is successfully recognized, the system provides the essential information to a process controller or programmable logic controller for further action without requiring any particular interface.
In the first pass, it is forwarded to the controller for further processing, and in the second pass, it is forwarded along the data path once a forwarding rule has been established.
Metrics such as CPU load and memory usage of both the VM and of the individual deployed applications are collected and fed to the global controller for further processing.
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The master FuHSM controller was utilized to grantee the optimal power coordination of the multiple power sources and the slave DPPC controller was responsible for further compensating the load power demand to enhance the dynamic performance and bus voltage stability.
The improvement of the controller is suggested for further study.
Moreover, the proportional-integral (PI) training algorithms of the control system are derived from the Lyapunov stability theorem, which are utilized to update the adjustable parameters of WNN controller on-line for further assuring system stability and obtaining a fast convergence.
Advantages and possible extensions of the proposed methodology are discussed to provide scientific guide for further controller design and operation improvement.
Analysis of EDM pulse trains reveals insights for controller design and identifies requirements for further improvement.
The parameters of the adaptive TSK fuzzy controller (AFC) are adjusted online for further reducing error residues after applying the SMC.
The test results show that the fuzzy self-adaptive PID controller's performance is better than that of the other two controllers, which provides a basis for further study and application.
Conclusions are drawn from this analysis for further robust nonlinear model based controller design.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com