Substitute Target Learning Based Control System for Control Knowledge Acquisition Within Constrained Environment

Real-time operations are usually conducted within a constrained environment. A human requires constantly updated knowledge to respond flexibly under different constraints to configure a control method around the constraints. In this research, a control system based on substitute target learning is proposed to enable control operations to configure their own control method around the constraints. This control system is applied to an inverted pendulum control system and its effectiveness is confirmed through a series of simulations and an experiment using a real machine.