A knowledge-based approach to improve neural network control design

Control engineers have been investigating and developing different on-line adaptation schemes to fine-tune performance of controllers after off-line design. Artificial neural network technology has shown satisfactory results for many control applications. However, certain types of nonlinear problems are difficult for the neural controller to learn by conventional on-line adaptation schemes, while the nonlinear system can be effectively controlled by incorporating heuristics knowledge. This paper presents an effective approach to incorporate heuristics control knowledge into a neural controller by off-line pre-training, then fine-tune the neural controller performance further by on-line adaptation. Experimental results on a servomotor system with significant nonlinear friction characteristics are used to demonstrate the effectiveness of the design approach.