The voltage and current output pins can be tied together to configure the end system as a single-channel output if desired. The whole system will benefit from the ultralow noise and low temperature coefficient of ADR The ADR44x family of references are ideal for driving either the reference or the power input of converters because they can source and sink current. The AD also incorporates a power-on reset circuit to ensure that the DAC output powers up to either midscale or zero and remains there until a valid write takes place.
The circuit must be constructed on a multilayer PC board with a large area ground plane. The AD has highly flexible and configurable output ranges that can be tailored to meet the needs of the application. The test results for all the other ranges are listed in Table 1. Note that the output ranges 0 mA to Using My Computer, locate the drive that contains the evaluation software disc and open the Readme file. Follow the instructions contained in the Readme file for installing and using the evaluation software. Figure 5 shows a functional block diagram of the test setup. Figure 5 shows a functional block diagram of the test setup.
Test Setup Functional Block Diagram. An automatic test program is necessary to step through the codes and analyze the data.
This is not provided on the CD but must be implemented by the customer to correspond to the requirements of the particular multimeter used in the test setup. This allows the multimeter readings corresponding to each code to be loaded into an Excel spreadsheet in the PC.
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Please Select a Language. Use nylon hardware to firmly secure the two boards, using the holes provided at the ends of the pin connectors. Ensure that the cable connection on the front panel of Agilent A is correct, depending on the different input signal type current or voltage.
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Community Discussion In Engineering Zone. Buy functionality exists in desktop site. Evaluation Boards Pricing displayed is based on 1-piece. Pricing displayed is based on 1-piece. These operations may have precise definitions, though the definitions can vary considerably between different implementations. In practice, the fuzzy rule sets usually have several antecedents that are combined using fuzzy operators, such as AND, OR, and NOT, though again the definitions tend to vary: AND, in one popular definition, simply uses the minimum weight of all the antecedents, while OR uses the maximum value.
There is also a NOT operator that subtracts a membership function from 1 to give the "complementary" function. There are several ways to define the result of a rule, but one of the most common and simplest is the "max-min" inference method, in which the output membership function is given the truth value generated by the premise. Rules can be solved in parallel in hardware, or sequentially in software. The results of all the rules that have fired are "defuzzified" to a crisp value by one of several methods. There are dozens, in theory, each with various advantages or drawbacks.
The "centroid" method is very popular, in which the "center of mass" of the result provides the crisp value. Another approach is the "height" method, which takes the value of the biggest contributor. The centroid method favors the rule with the output of greatest area, while the height method obviously favors the rule with the greatest output value.
The diagram below demonstrates max-min inferencing and centroid defuzzification for a system with input variables "x", "y", and "z" and an output variable "n". Note that "mu" is standard fuzzy-logic nomenclature for "truth value":. Notice how each rule provides a result as a truth value of a particular membership function for the output variable. In centroid defuzzification the values are OR'd, that is, the maximum value is used and values are not added, and the results are then combined using a centroid calculation.
Fuzzy control system design is based on empirical methods, basically a methodical approach to trial-and-error.
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The general process is as follows:. As a general example, consider the design of a fuzzy controller for a steam turbine. The block diagram of this control system appears as follows:. In practice, the controller accepts the inputs and maps them into their membership functions and truth values. These mappings are then fed into the rules.
Fuzzy control system - Wikipedia
If the rule specifies an AND relationship between the mappings of the two input variables, as the examples above do, the minimum of the two is used as the combined truth value; if an OR is specified, the maximum is used. The appropriate output state is selected and assigned a membership value at the truth level of the premise. The truth values are then defuzzified. For an example, assume the temperature is in the "cool" state, and the pressure is in the "low" and "ok" states.
Fuzzy control system
The pressure values ensure that only rules 2 and 3 fire:. The output value will adjust the throttle and then the control cycle will begin again to generate the next value. A fuzzy set is defined for the input error variable "e", and the derived change in error, "delta", as well as the "output", as follows:. These rules are typical for control applications in that the antecedents consist of the logical combination of the error and error-delta signals, while the consequent is a control command output. The rule outputs can be defuzzified using a discrete centroid computation:.
If you have problems figuring out the centroid equation, remember that a centroid is defined by summing all the moments location times mass around the center of gravity and equating the sum to zero. If they are not the same, i. This system can be implemented on a standard microprocessor, but dedicated fuzzy chips are now available. A block diagram of the chip is shown below:.
As a first example, consider an anti-lock braking system , directed by a microcontroller chip. The microcontroller has to make decisions based on brake temperature , speed , and other variables in the system. The variable "temperature" in this system can be subdivided into a range of "states": The transition from one state to the next is hard to define.