System identification using a genetic algorithm and its application to internal adaptive model control

The requirement for the high-quality control of complex and/or structure-unknown plant is growing in the real-world industrial machine. Indirect Adaptive Control (IAC), which identifies model and updates the controllers automatically, is one promising way expected to meet this requirement. The conventional IAC, however, is required to know the structure of the controlled plant, that is, the order of its transfer function, in advance. This paper presents a new IAC scheme which makes use of Genetic Algorithm (GA) in its identification part. In the proposed framework, the information on the order of the plant is not required since the genetic algorithm searches both the structure of the plant dynamics and its parameters autonomously. A two-degree-of-freedom Internal Mode Control (IMC) is adopted as a basic control architecture since the indirect adaptation can be harmoniously embedded in it. The effectiveness of the proposed scheme is verified through numerical simulations and experiments applied to a velocity control of multimass systems. (C) 2003 Wiley Periodicals, Inc.