Machine-learned rule-based control differs from more typical approaches to the engineering of controllers for physical systems in the following respect. In traditional control theory, a mathematical model of the system is constructed and then analysed in order to synthesise a control method. This approach is clearly deductive. A machine-learning approach to the synthesis of controllers aims to inductively acquire control knowledge, thereby avoiding the necessity of constructing a mathematical model of the system. In applications where systems are very complex, or insufficient knowledge is available, the construction of such a model may be impossible, and traditional methods therefore inappropriate. It is for these applications that an inductive approach promises solutions.

Inspec keywords: control engineering computing; control system synthesis; learning (artificial intelligence); knowledge based systems

Other keywords: physical system controller; machine-learning approach; control theory; mathematical model; controllers synthesis; machine-learned rule-based control method; model construction

Subjects: Control engineering computing