Understanding Smart Sensors - Nomads.usp

Control Techniques 169Domain-type specifications allow the sensor to function with wider tolerancein terms of repeatability, accuracy drift, and linearity. A natural extension of thefuzzy logic rules to the sensor has the sensor expressing the measurementin terms of grade of membership to the predefined domain. Those principleshave already been applied to color, proximity, fluid control, and positionsensors.An extensive analysis of classic control theory, fuzzy logic, neural networks,and rough set theory has been performed for smart sensors [30]. Theauthors have defined an operator’s inference model that allows evaluation ofthe situation, assignment of the situation to a characteristic state, and selectionand execution of the proper characteristic control. The model has been used toevaluate smart sensors and control system design. The rough set in the analysisis similar to fuzzy logic, except that a rough ADC replaces the fuzzifier in thecontroller, and a rough DAC replaces the defuzzifier. The inference engine inthe controller evaluates the decision table for the rough set. The decision tablesused to derive the rules in the rough controller are simple to understand andeasy to edit.As part of their findings, the authors determined that classic control theory,a mature technology with widely supported hardware, can accomplishmore complex computing by increasing the millions of instructions per second.Fuzzy logic has commercial developments available that are making its acceptancewidespread, but it does have safety and reliability disadvantages. Therough set can be used for pattern matching and is fast and low cost, but it doeshave stability and completeness concerns similar to those with fuzzy logic andneural networks. Neural networks can possibly provide an all-analog systemthat is easily integrated with sensors, but they have the added problem of anunknown decision basis. Attention to the areas of concern, however, is allowingall those systems to be evaluated for new applications. The combination ofthose control techniques with sensors will achieve new levels of smart sensing.7.9 SummaryThis chapter discussed control techniques and significantly improved computingcapabilities. Fuzzy logic is already finding broad acceptance in many controlapplications. More complex systems will benefit from fuzzy logic and otheradvanced approaches, such as model-based algorithms. Some of those systemswill use more feedback and, consequently, more sensors. However, the sensors,at least in some instances, may not have to be as accurate and therefore couldoffset at least some of the added cost of getting smarter.