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.
Application of single neuron adaptive PID controller during the process of timber drying
The paper presents a method of using single neuron adaptive PID control for adjusting system or servo system to implement timber drying process control, which combines the thought of parameter adaptive PID control and the character of neural network on exactly describing nonlinear and uncertainty dynamic process organically. The method implements functions of adaptive and self-learning by adjusting weighting parameters. Adaptive neural network can make some output trail given hoping value to decouple in static state. The simulation result indicates the validity, veracity and robustness of the method used in the timber drying process
Single Neuron Adaptive PID Controller and its Applications
The single neuron adaptive PID controller is discussed,including its main features, such as the characteristics,the control law, the application condition, and the parament adjustment etc.The single neuron adaptive PID controller on the SmartPro DCS platform of Hollysys is developed. The simulations of the single neuron adaptive PID in complex object close loop is given. The proposed single neuron adaptive PID is applied into the temperature control loop of ferment kettle at a pharmacy plant successfully. It is verified that this controller has few adjustable paraments and excellent robustness performance,and it can restrain the process time-delay effectively.