TABLE OF CONTENTS As a result of studying this chapter, the student should be able to:
Describe the theory and operation of a self-tuning controller
Describe the concept of statistical process control (SPC) and its use in analyzing and indicating the standards of performance in control systems.
This chapter introduces the basic concepts of self-tuning or adaptive controllers, intelligent controllers, and provides an overview of statistical process control (SPC).
Self or auto-tuning controllers are capable of automatically re-adjusting the process controllers tuning parameters. They first appeared on the market in the early 1970s and evolved from ones using optimum regulating and control types through to the current types that, with the advent of high speed processors, rely on adaptive control algorithms. The main elements of a self-tuning system are illustrated in Figure 14.1, these being:
A system identifier: This model estimates the parameters of the process.
A controller synthesizer: This model has to synthesize or calculate the controller parameters specified by the control object functions.
A controller implementation block: This is the controller whose parameters (gain K C, T INT, T DER, etc.) are changed and modified at periodic intervals by the controller synthesizer.
The system identifier, by comparing the PV action as a result of the MV change, and using algorithms based on recursive estimations, determines the response of the system. This is commonly achieved by the use of fuzzy logic that extracts key dynamic response features...
