Avd Control 1-8

Define the terms Known Knows, Known Unknowns, and Unknown Unknowns, and then apply them to context of a control system (Cruise Control).

Known Knowns: Variables of the system from sensors or actuators

Known Unknowns: Uncertainties that we are aware of

Unknown Unknowns: Uncertanties that we have not anticipated

Cruise Control

KK: We know from the sensor the speed of the system, desired speed set by driver.

KU: These are typically derived from the enviroment, ie road conditions, incline decline, road surface.

UU: Typically none as this is a relatively simple system, however may be partial engine damage which is affecting controller.

Define a suitable design process that a control system should follow.

1. Establish control goals

2. Identify the variables to control

3. Write the specifications for the variables.

4. Establish the system configuration and identify the actuator

5. Obtain a model of the process, the actuator, and the sensor

6. Describe a controller and select key parameters to be adjusted

7. Optimise the parameters and analyse the performance

If performance does not need specifications, reconfigure and redesign (step 4).

Define verification, describe why it is important and what steps should be involved in the verification process.

The process ensuring that the simulation model is an accurate representation of the physical equations that represent the dynamics of the system being modelled.

It is important as it checks that the model has been implemented correctly and actually matches the intended system equations. If the model is wrong, controller designed or tested may behave incorrectly on the real system.

Inspection: Checks each stage of your model and ensures that the system is represented correctly in each line of your model. Each line of your model should reflect something about the real system. This stage can catch basic errors.

Demonstration: Your model should be written in such away that your can test elements of it as you go. At this stage you are able to put in known stimulus that should generate a known output. This demonstrates that your model can work and provides a sanity check on the logic and basic operation of your model. This is different from test as during testing you apply a wide range of test stimulus and you are looking to test the system.

Test: The first stage is to identify a series of suitable test scenarios that you know what the output should be. These tests should be clear and there should be no ambiguity in the output to ensure that the results are what you expect. The tests should prove that the controlled system meets the requirements set down within the original remit.

Define validation.

The process of ensuring that the simulated output response from the model is an accurate representation of the output response from the actual system.

Describe the concept of Analogue matching with a sketch.

Analogue matching is a comparison done visually of the actual and simulated response for the same input stimuli. Differences between the responses can be assessed and changed to the model parameters made. This is repeated until the system is within the required tolerances, a best fit is achieved. As its done visually this requires expertise.

Describe the concept Least Mean Squares (Validation).

A method that can be used in conjunction with analogue matching is least mean squares. It is a quantitative measure of the models accuracy by calculating the sum of errors squared. This provides a measure of the error over the complete test and can provide additional insight to the comparison.

Describe the concept of Parameter Estimation.

Model parameters are adjusted and outputs of the simulated and real systems are compared via a cost function. The model parameters are adjusted to allow the cost functions used to tend toward zero. Process is carried out automatically.

When simulating control systems, why should you include ‘real’ models of the sensors and actuators? Provide three concepts of what can be added to the model to replicate the real world.

The control systems developed in simulation need to be ready for the real world. This means that the system needs to be tested with models that are representative of both the sensors and actuators. The sensors and actuators in the real world will not be ideal, as is often the case with modelled devices.

Sensor noise: Random fluctuations added to the measured signal, so the controller does not see the true value exactly. which can reduce tracking accuraucy.

Bias drift: A slowly changing offset in the measurement over time. Controller may gradually respond to a false trend over time.

Actuator limit: Actuators can not respond infinitely fast (speed) or produce unlimited output.