Adaptive Control Utilising Neural Swarming
نویسندگان
چکیده
Process changes, such as flow disturbances and sensor noise, are common in the chemical and metallurgical industries. To maintain optimal performance, the controlling system has to adapt continuously to these changes. This is a difficult problem because the controller also has to perform well while it is adapting. The Adaptive Neural Swarming (ANS) method introduced in this paper satisfies these goals. Using an existing neural network controller as a starting point, ANS modifies the network weights through Particle Swarm Optimisation. The ANS method was tested in a real-world task of controlling a simulated non-linear bioreactor. ANS was able to adapt to process changes while simultaneously avoiding hard operating constraints. This way, ANS balances the need to adapt with the need to preserve generalisation, and constitutes a general tool for adapting neural network controllers on-line.
منابع مشابه
Modeling and Control of Gas Turbine Combustor with Dynamic and Adaptive Neural Networks (TECHNICAL NOTE)
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