Artificial intelligence for process regulation and servo control

Abstract

Artificial intelligence (AI) techniques are increasingly being used throughout the field of process control where the applications benefiting from these techniques range from measurement (sensor) validation and control system design, to control loop supervision and tuning. This chapter concentrates on the on-line application of knowledge engineering techniques in real-time to achieve an improvement in process performance. Current trends in the application of expert system in industry have been outlined in a recent survey carried out by Sangiovanni and Romans (1). One of their more significant findings was that more than 30% of Expert Systems are used for the diagnosis of process or instrumentation faults whereas less than 5% of applications are concerned with process control. This is not surprising as real-time knowledge-base software has only recently become available and even so is still in the early stages of its evolution. This chapter attempts to overview the advantages of knowledge based systems (KBS) in order to satisfy the conflicting goals of maintaining process stability whilst meeting the demands of process operation. The difficulty in achieving 'good' control system performance results from having to accommodate inaccuracies (uncertainty) and changes (variations) between our theoretical understanding of the process and the actual process itself. The goal of current research is to improve the performance and operability of closed-loop systems where the structure of the solution is to be as general as possible and which naturally takes the form of a real-time KBS combining the knowledge of both control engineers and process engineers.