Control system synthesis through inductive learning of Boolean concepts

In control, learning is often used to identify a single controller satisfying a particular performance measure. In certain cases, however, it is desirable to identify the set of all controllers which ensure that the controlled plant satisfies a control property such as Lyapunov stability, robust stability, or robust performance. A set of procedures identifying such sets of admissible solutions can be devised using Boolean concept learning algorithms. This type of learning procedure is applicable to computational learning. The objective of this article is to provide some examples illustrating how Boolean concept learning can be used in control systems. The first example examined in this article uses concept learning to identify the set of stabilizing controllers for certain classes of linear time-invariant plants. Another example illustrates the use of concept learning in the identification of discrete event system (DES) controllers.<
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