Decentralized neural network variable structure controller design for wood drying process
This paper investigates the development and evaluation of a robust control system for a wood drying kiln process incorporating decentralized variable structure control (DVSC) such that the moisture content of lumber will reach and be stabilized at the desired set point. A description of the dynamics of the wood drying process by means of the time-delay neural network is also presented, in which the back-propagation algorithm was implemented for testing, training and validation. Then this identified model is used for simulation purpose and controller design. For comparison purpose, a conventional proportional-integral-derivative (PID) controller is also employed and system performance is evaluated through simulations. The results are evaluated to tune the controller parameters to achieve good performance in the wood-drying kiln and the DVSC strategy promises improved performance. The control system developed in this study may be applied in industrial wood-drying kilns, with a clear potential for improved quality and increased speed of drying.
Decentralized direct adaptive neural network control of interconnected systems
The problem of direct adaptive neural network control for a class of large-scale systems with unknown function control gains and the high-order interconnections is studied in This work. Based on the principle of sliding mode control and the approximation capability of multilayer neural networks, a design scheme of decentralized direct adaptive sliding mode controller is proposed. The plant dynamic uncertainty and modeling errors are adaptively compensated by the adjusted weights and sliding mode gains on-line for each subsystem only using local information. According to the Lyapunov method, the closed-loop adaptive control system is proven to be globally stable, with tracking errors converging to a neighborhood of zero.
Evaluation of Lapping Grains based on Shape Characteristics.
The shape and size of abrasive grains during lapping varies complicately and irregularly. In this study, the aspect ratio of grain size and the fractal dimension of the grain external form were used as shape characteristics of grain in order to evaluate grains with lapping time. The results obtained are summarized as follows : (1)The fractal dimension increases due to microscopic fracture and decreases due to macroscopic fracture or attritious wear. (2)The aspect ratio and the fractal dimension, which are grain shape characteristicsis of the macroscopic shape and the microscopic surface complication of grain respectively, can be used for evaluating grain shape variation during lapping.