1. Neural Network Based Control System Design TOOLKIT For Use with MATLAB Magnus Nørgaard Department of Automation Department of Mathematical Modelling Technical Report 00-E-892, Department of Automation Technical of Denmark http://kalman.iau.dtu.dk/research/control/nnsysid.html Reference Text: M. Nørgaard, O. Ravn, N.K. Pouilsen, L,K. Hansen, Neural Networks for Modelling and Control of Dynamic Systems, Springer, London, 2003.

2. Single Input Single Output (SISO) Time Domain vs. Frequency Domain Regulators vs. Servos The primary function of a regulatory system is to maintain a constant value of the controlled variable or system output even in the event of severe load inputs. In processing industries most processes can be considered self regulating and have many first order time constants in series as well as a significant dead time between the time the manipulated variable is changed and any change is detected in the process. A servo-system is normally subjected to a continuously varying command signal or set point; its primary function, causing the output to follow the command signal.

3. Regulator Servo

4. Neural Network for Control Inverted neural network controls have been used principally for servo applications. The concept of neural network control is if a neural network model can be defined for the process, this model can be inverted and the inverted model can be used for control. This should be used only when the process is so complex or non linear that conventional controls, such as PID, cannot be used. There is a serious word of caution with using this technique. Because the neural network uses “trained” parameters and not first order principals, the network will not give guaranteed performance within any region where it was not trained. Interlocks should be used to safely shutdown the process in event of any malfunction.

5. An example of this technique is shown below, a conical tank level control:

6. The tank level is a highly non linear process, the outlet flow being a function of the square root of the head while the inlet level change is a function of the inverted square of the head: Watch out for zero head!

7. The inverted neural network and control system are shown as follows:

8. An example of the result of a trained network is shown below

9. In order to avoid model offsets, an improved method would be to add a PI controller before the network. The output of this controller should be scaled to the reference input range. The reset term in the PI controller will act as a bias to offset network inaccuracies.

10. Neural Networks as Compensating Blocks ISA article by Dumbie uses a feed forward block in the output signal to the final controller's setpoint. The example: Temperature controller of a tempered water system with a cold and hot streams mixed. Total flow and temperature of the combined streams are the interactive controls.

11. Compensating Feed Forward Blocks The combined temperature in this equation is the output of the temperature controller T w '

12. Note that the Tw` is the controller output, that is scaled the same as the PV or controller input An alternate to this is to develop a neural network, inverted, that acts as a feed forward compensating block This technique is also used by Rhinehart for distillation control