Simulated neurocontrol of an autogenous mill with evolutionary reinforcement learning
In this investigation the development of nonlinear control system for an autogenous mill was considered. A symbiotic adaptive neuroevolution algorithm was used in conjunction with a dynamic multilayer perceptron model fitted to actual plant data to evolve neurocontrol systems. Simulation studies established the potential of the approach, which yielded satisfactory results, despite having had to learn from a model that covered part of the state space only. Copyright © 2007 IFAC.