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Fuzzy Logic Controller

Evaluate fuzzy inference system

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  • Fuzzy Logic Controller block

Libraries:
Fuzzy Logic Toolbox

Description

The Fuzzy Logic Controller block implements a fuzzy inference system (FIS) in Simulink®. You specify the FIS to evaluate using the FIS name parameter.

For more information on fuzzy inference, see Fuzzy Inference Process.

To display the fuzzy inference process during simulation, use the Fuzzy Logic Controller with Ruleviewer block.

Examples

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This example uses:

Open Live Script

The simulateTipper model computes tip values for specified food and service ratings using the Fuzzy Logic Controller block, which is configured to use the FIS stored in the tipper.fis file. For more information on how to create this FIS, see Build Fuzzy Systems Using Fuzzy Logic Designer.

model = "simulateTipper";
open_system(model)

In this model:

  • You specify the food and service ratings using the Food Rating and Service Rating blocks, respectively

  • The input signals are constrained to the expected input range of the FIS, which is [0,10] for both signals.

  • The constrained input signals are combined using a Mux block.

  • The Fuzzy Logic Controller block computes the tip output value based on the input signals.

Extended Examples

Simulate Fuzzy Inference System

Simulate Fuzzy Inference System

Once you have implemented a fuzzy inference system using Fuzzy Logic Designer, using Neuro-Fuzzy Designer, or at the command line, you can simulate the system in Simulink.

Water Level Control in a Tank

Water Level Control in a Tank

Implement a water level controller using the Fuzzy Logic Controller block in Simulink.

Temperature Control in a Shower

Temperature Control in a Shower

Implement a water temperature controller using the Fuzzy Logic Controller block in Simulink.

Fuzzy PID Control with Type-2 FIS

Fuzzy PID Control with Type-2 FIS

Create a type-2 fuzzy logic PID controller and compare its performance with a type-1 fuzzy PID controller and a conventional PID controller.

Ports

Input

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For a single-input fuzzy inference system, the input is a scalar signal. For a multi-input fuzzy system, combine the inputs into a vector signal using blocks such as:

Output

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For a single-output FIS, the output is a scalar signal. For a multi-output FIS, the output is a vector signal. To split system outputs into scalar signals, use the Demux (Simulink) block.

Fuzzified input values, obtained by evaluating the input membership functions of each rule at the current input values.

For a type-1 FIS, fi is an NR-by-NU matrix signal, where NR is the number of FIS rules. Element (i,j) of fi is the value of the input membership function for the jth input in the ith rule.

For a type-2 FIS, fi is an NR-by-(2*NU) matrix signal. The first NU columns contain the fuzzified values of the upper membership function for each rule, and the last NU columns contain the fuzzified values from the lower membership functions.

For more information on fuzzifying input values, see Fuzzify Inputs.

Dependencies

To enable this port, select the Fuzzified inputs (fi) parameter.

Rule firing strengths, obtained by evaluating the antecedent of each rule; that is, applying the fuzzy operator to the values of the fuzzified inputs.

For a type-1 FIS, rfs is a column vector signal of length NR, where NR is the number of rules, and element i is the firing strength of the ith rule.

For a type-2 FIS, rfs is an NR-by-2 matrix signal. The first column contains the rule firing strengths generated using upper membership functions, and the second column contains the rule firing strengths generated using lower membership functions.

For more information on applying fuzzy operators, see Apply Fuzzy Operator.

Dependencies

To enable this port, select the Rule firing strengths (rfs) parameter.

Rule outputs, obtained by applying the rule firing strengths to the output membership functions using the implication method specified in the FIS.

For a type-1 Mamdani FIS, ro is an NS-by-(NRNY) matrix signal, where NR is the number of rules, NY is the number of outputs, and NS is the number of sample points used for evaluating output variable ranges. Each column of ro contains the output fuzzy set for one rule. The first NR columns contain the rule outputs for the first output variable, the next NR columns correspond to the second output variable, and so on.

For a type-2 Mamdani FIS, ro is an NS-by-(2*NR*NY) matrix signal. The first NR*NY columns contain the rule outputs generated using upper membership functions, and the last NR*NY columns contain the rule outputs generated using lower membership functions.

For a type-1 Sugeno system, each rule output is a scalar value. In this case, ro is an NR-by-NY matrix signal. Element (j,k) of ro is the value of the kth output variable for the jth rule.

For a type-2 Sugeno system, ro is an NR-by-(3*NY) array. The first NY columns contain the rule output levels. The next NY columns contain the corresponding rule firing strengths generated using upper membership functions. The last NY columns contain the rule firing strengths generated using lower membership functions. For example, in a three-output system, columns 4 and 7 contain the firing strengths for the output levels in column 1.

For more information on fuzzy implication, see Apply Implication Method.

Dependencies

  • To enable this port, select the Rule outputs (ro) parameter.

  • To specify NS, use the Number of samples for output discretization parameter.

Aggregate output for each output variable, obtained by combining the corresponding outputs from all the rules using the aggregation method specified in the FIS.

For a type-1 Mamdani fuzzy inference system, the aggregate result for each output variable is a fuzzy set. In this case, ao is as an NS-by-NY matrix signal, where NY is the number of outputs and NS is the number of sample points used for evaluating output variable ranges. Each column of ao contains the aggregate fuzzy set for one output variable.

For a type-2 Mamdani FIS, the aggregate result for each output variable is a fuzzy set. In this case, ao is as an NS-by-(2*NY) matrix signal. The first NY columns contain the aggregated outputs generated using upper membership functions, and the last NY columns contain the aggregated outputs generated using lower membership functions.

For a type-1 Sugeno system, the aggregate result for each output variable is a scalar value. In this case, ao is a row vector of length NY, where element k is the sum of the rule outputs for the kth output variable.

For a type-2 Sugeno system, ao is an NR-by-(3*NY) array. ro contains the same data as ao with the columns sorted based on the output levels. For example, in a three-output system, when the output levels in column 1 are sorted, the corresponding firing strengths in columns 4 and 7 are adjusted accordingly.

For more information on fuzzy aggregation, see Aggregate All Outputs.

Dependencies

  • To enable this port, select the Aggregated outputs (ao) parameter.

  • To specify NS, use the Number of samples for output discretization parameter.

Parameters

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To edit block parameters interactively, use the Property Inspector. From the Simulink Toolstrip, on the Simulation tab, in the Prepare gallery, select Property Inspector.

General

Fuzzy inference system to evaluate, specified as one of the following:

  • mamfis or sugfis object — Specify the name of a type-1 FIS object in the MATLAB® workspace.

  • mamfistype2 or sugfistype2 object — Specify the name of a type-2 FIS object in the MATLAB workspace.

  • Name of a FIS file (*.fis) in the current working folder or on the MATLAB path. Including the file extension in the file name is optional. Specify the filename in quotes.

  • Name of a MAT file (*.mat) in the current working folder or on the MATLAB path. The MAT file must contain only one FIS object. Including the file extension in the file name is optional. Specify the filename in quotes. (since R2024a)

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: FIS
Values: '"tipper.fis"' (default) | variable name in quotes | file name in quotes

Number of samples for discretizing the range of FIS output variables, specified as an integer greater than 1. This value corresponds to the number of points in the output fuzzy set for each rule.

To reduce memory usage while evaluating Mamdani FISs, specify a lower number of samples. Doing so sacrifices the accuracy of the defuzzified output value. Specifying a low number of samples can make the output area for defuzzification zero. In this case, the defuzzified output value is the midpoint of the output variable range.

Note

The block ignores this parameter when evaluating Sugeno FISs.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: OutputSampleNumber
Values: "101" (default) | integer greater than 1 in quotes

Signal data type, specified as one of the following:

  • double — Double-precision signals

  • single — Single-precision signals

  • fixdt(1,16,0) — Fixed-point signals with binary point scaling

  • fixdt(1,16,2^0,0) — Fixed-point signals with slope and bias scaling

  • Expression — Expression that evaluates to one of these data types

For fixed-point data types, you can configure the signedness, word length, and scaling parameters using the Data Type Assistant. For more information, see Specifying a Fixed-Point Data Type (Simulink).

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: DataType
Values: "double" (default) | "single" | "fixdt(1,16,0)" | "fixdt(1,16,2^0,0)"

Enable fi output port for accessing intermediate fuzzified input data.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: FuzzifiedInputs
Values: "off" (default) | "on"

Enable rfs output port for accessing intermediate rule firing strength data.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: RuleFiringStrengths
Values: "off" (default) | "on"

Enable ro output port for accessing intermediate rule output data.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: RuleOutputs
Values: "off" (default) | "on"

Enable ao output port for accessing intermediate aggregate output data.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: AggregatedOutputs
Values: "off" (default) | "on"

Simulation mode, specified as one of the following:

  • Interpreted execution — Simulate fuzzy systems using precompiled MEX files for single and double data types. Using this option reduces the initial compilation time of the model.

  • Code generation — Simulate fuzzy system without precompiled MEX files. Use this option when simulating fuzzy systems for code generation applications.

For fixed-point data types, the Fuzzy Logic Controller block always simulates using Code generation mode.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: SimulateUsing
Values: "Interpreted execution" (default) | "Code generation"

Diagnostics

Diagnostic message behavior when an input is out of range, specified as one of the following:

  • warning — Report the diagnostic message as a warning.

  • error — Report the diagnostic message as an error.

  • none — Do not report the diagnostic message.

When an input value is out of range, corresponding rules in the fuzzy system can have unexpected firing strengths.

Dependencies

  • Diagnostic messages are provided only when the Simulate using parameter is Interpreted execution.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: OutOfRangeInputValueMessage
Values: "warning" (default) | "error" | "none"

Diagnostic message behavior when no rules fire for a given output variable, specified as one of the following:

  • warning — Report the diagnostic message as a warning.

  • error — Report the diagnostic message as an error.

  • none — Do not report the diagnostic message.

When No rule fired is warning or none and no rules fire for a given output, the defuzzified output value is set to its mean range value.

Dependencies

  • Diagnostic messages are provided only when the Simulate using parameter is Interpreted execution.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: NoRuleFiredMessage
Values: "warning" (default) | "error" | "none"

Diagnostic message behavior when an output fuzzy set is empty, specified as one of the following:

  • warning — Report the diagnostic message as a warning.

  • error — Report the diagnostic message as an error.

  • none — Do not report the diagnostic message.

When Empty output fuzzy set is warning or none and an output fuzzy set is empty, the defuzzified value for the corresponding output is set to its mean range value.

Dependencies

  • This diagnostic message applies to Mamdani systems only.

  • Diagnostic messages are provided only when the Simulate using parameter is Interpreted execution.

Programmatic Use

To set the block parameter value programmatically, use the set_param (Simulink) function.

Parameter: EmptyOutputFuzzySetMessage
Values: "warning" (default) | "error" | "none"

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

PLC Code Generation
Generate Structured Text code using Simulink® PLC Coder™.

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.

Version History

Introduced before R2006a

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See Also

Blocks

Apps

Functions

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