Vivek Venugopal Badami

Industrial applications of fuzzy logic at General Electric

Fuzzy logic control (FLC) technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. We illustrate some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, steam turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variables in a rolling mill stand. We compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit tradeoff criteria used to manage multiple control strategies. Finally, we describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering. >

Home appliances get smart

The authors describe how novel sensors and control algorithms embedded in todays domestic appliances make them smart enough to estimate the size and dirtiness of a load of clothes tossed in a washer or to determine whether water in a pot on the stove is boiling. They argue that, as semiconductor costs come down, such appliances will get even smarter.

Real-time temporal and causal reasoning for intelligent control

Automation of the control of complex systems has been achieved through the application of computers using such tools as closed-loop control, finite-state machines, etc. However, a number of tasks in the control of such processes must still be performed by human operators, resisting conventional automation efforts. These tasks usually involve unforeseen circumstances, such as defective controller hardware or unusual behavior of the application being controlled. Operators often use experience or heuristic reasoning to cope with these types of process deviations. In this paper, we will describe the theory and design of a generic temporal/causal system called TEMPROS (TEMporal PROgramming System) used to perform intelligent real-time control, capable of assuming many of the responsibilities that are currently exclusively performed by human operators. The system uses temporal propositions as its reasoning mechanism, deviating from current temporal reasoning systems by adding the new state “potentially true” to the truth value of a proposition. In addition, the system employs a plan hierarchy and a “lazy instantiation” mechanism to achieve the performance needed to make intelligent decisions in real time. We shall also outline the application of TEMPROS to the domain of growing gallium arsenide crystals using the LEC process.

An intelligent controller for process automation

A novel supervisory controller that incorporates both a procedural and a rule-based language and is capable of responding to asynchronous real-time sensor interrupts is presented. This supervisory controller has been used for automating the manual operations in GaAs crystal growth in a liquid-encapsulated-Czochralski- (LEC-) process-based puller from Cambridge Instruments (CI-358). The controller, referred to as the metacontroller (MC), executes plans to sequence the operations of crystal growth. Plans are structured English-like representations of scripts that operators follow to grow crystals. The metacontroller executes the plans by responding to asynchronous sensor data interrupts in realtime and issuing actuator commands to a real-time controller module. The notion of time is explicitly incorporated into the syntax of the language. The metacontroller has been successful in automating sections of the full crystal growth protocol for GaAs using the LEC apparatus. Two 4-in.-diameter boules have been grown from 8-kg charges using the system where plans have been implemented. Portions of the synthesis and meltback steps have been automated, and the seed-on step that requires the most manual operation is almost completely automated.<<ETX>>