MATLAB Examples

# 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.

For this example, you control the level of water in a tank using a fuzzy inference system implemented using a Fuzzy Logic Controller block. Open the sltank model.

```open_system('sltank') ```

For this system, you control the water that flows into the tank using a valve. The outflow rate depends on the diameter of the output pipe, which is constant, and the pressure in the tank, which varies with water level. Therefore, the system has nonlinear characteristics.

The two inputs to the fuzzy system are the water level error, level, and the rate of change of the water level, rate. The output of the fuzzy system is the rate at which the control valve is opening or closing, valve.

To implement a fuzzy inference system, specify the FIS name parameter of the Fuzzy Logic Controller block as the name of a FIS structure in the MATLAB® workspace. In this example, the block uses the FIS structure tank.

As a first attempt to control the water level, set the following rules in the FIS. These rules adjust the valve based on only the water level error.

• If the water level is okay, then do not adjust the valve.
• If the water level is low, then open the valve quickly.
• If the water level is high, then close the valve quickly.
```rule1 = "If level is okay then valve is no_change"; rule2 = "If level is low then valve is open_fast"; rule3 = "If level is high then valve is close_fast"; rules = [rule1 rule2 rule3]; tank = parsrule(tank,rules); ```

Simulate the model and view the water level.

```open_system('sltank/Comparison') sim('sltank',100) ```

These rules are insufficient for controlling the system, since the water level oscillates around the setpoint.

To reduce the oscillations, add two more rules to the system. These rules adjust the valve based on the rate of change of the water level when the water level is near the setpoint.

• If the water level is okay and increasing, then close the valve slowly.
• If the water level is okay and decreasing, then open the valve slowly.
```rule4 = "If level is okay and rate is positive then valve is close_slow"; rule5 = "If level is okay and rate is negative then valve is open_slow"; rules = [rule1 rule2 rule3 rule4 rule5]; tank = parsrule(tank,rules); ```

Simulate the model.

```sim('sltank',100) ```

The water level now tracks the setpoint without oscillating.

You can also simulate fuzzy systems using the Fuzzy Logic Controller with Ruleviewer block. The sltankrule model is the same as the sltank model, except that it uses the Fuzzy Logic Controller with Ruleviewer block.

```open_system('sltankrule') ```

During simulation, this block displays the Rule Viewer from the Fuzzy Logic Designer app.

```sim('sltankrule',100) ```

If you pause the simulation, you can examine the FIS behavior by manually adjusting the input variable values in the Rule Viewer, and observing the inference process and output.

You can also access the Fuzzy Logic Designer editors from the Rule Viewer. From the Rule Viewer, you can then adjust the parameters of your fuzzy system using these editors, and export the updated system to the MATLAB workspace. To simulate the updated FIS, restart the simulation. For more information on using these editors, see docid:fuzzy.FP243DUP9.