Abstract
Artificial intelligence methods appear to be particularly well suited for control design when only inexact prior knowledge about the system to be controlled is available. Design tasks that can be solved include learning control from scratch, improving partial control knowledge, and controller tuning. The paper enlightens these approaches in two case studies, both dealing with nonlinear unstable systems: inverted pendulum control, and position control of a floating object. Comparison to the classical model-based control design approaches is also provided.
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