Applied intelligent control - control of automotive paint process

We present an intelligent control algorithm that is targeted to process control of the steady state of a class of industrial MIMO nonlinear systems. The algorithm, called the RBIC intelligent control algorithm, combines the conventional indirect adaptive control approach with a rule base of initial conditions (RBIC) - an intelligent tool improving the conventional indirect adaptive algorithm in the presence of large disturbances and multiple operating modes. The RBIC operates as an associative memory that periodically reinitializes the indirect adaptive control algorithm by using a fuzzy reasoning inference mechanism. We discuss the main features and application aspects of the RBIC intelligent control algorithm. We also demonstrate one large scale process control application of the RBIC intelligent control algorithm as the main component of Ford Motor Company´s integrated paint quality control system