1. Dynamic Neural Network Control (DNNC): A Non-Conventional Neural Network Model
Masoud Nikravesh
EECS Department, CS Division
BISC Program
University of California
Berkeley, California
Abstract:
In this study, Dynamic Neural Network Control methodology for model identification
and control of nonlinear processes is presented. The methodology uses several
techniques: Dynamic Neural Network Control (DNNC) network structure,
neuro-statistical (neural network & non-parametric statistical technique such as
ACE; Alternative Conditional Expectation) techniques, model-based control
strategy, and stability analysis techniques such as Liapunov theory. In this
study, the DNNC model is used because it is much easier to update and adapt the
network on-line. In addition, this technique in conjunction with
Levenberge-Marquardt algorithm can be used as a more robust technique for network
training and optimization purposes. The ACE technique is used for scaling the
networks input-output data and can be used to find the input structure of the
network. The result from Liapunov theory is used to find the optimal neural
network structure. In addition, a special neural network structure is used to
insure the stability of the network for long-term prediction. In this model, the
current information from the input layer is presented into a pseudo hidden layer.
This model minimizes not only the conventional error in the output layer but also
minimizes the filtered value of the output. This technique is a tradeoff between
the accuracy of the actual and filtered prediction, which will result in the
stability of the long-term prediction of the network model. Even though, it is
clear that DNNC will perform better than PID control, it is useful to compare PID
with DNNC to illustrate the extreme range of the non linearity of the processes
were used in this study. The integration of the DNNC and the
shortest-prediction-horizon nonlinear model-predictive control is a great
candidate for control of highly nonlinear processes including biochemical
reactors.
References:
1. M. Nikravesh, A. E. Farell, T. G. Stanford, Control of Nonisothermal CSTR with
time varying parameters via dynamic neural nework control (DNNC), Chemical
Engineering Journal, vol. 76, 2000, pp
2. M. Nikravesh, Artificial neural networks for nonlinear control of industrial
processes, " Nonlinear Model Based Process Control", Book edited by Ridvan Berber
and Costas
Karavaris, NATO Advanced Science Institute Series, Vol 353, 1998 Kluwer Academic
Publishers, pp
3. S. Valluri, M. Soroush, and M. Nikravesh, Shortest-prediction-horizon nonlinear
model-predictive control, Chemical Engineering science, Vol 53, No2, pp ,
1998.

2. Dynamic Neural Network Control (DNNC):
A Non-Conventional Neural Network Model
Masoud Nikravesh
EECS Department, CS Division
BISC Program
University of California
Berkeley, California

3. Dynamic Neural Network Control (DNNC)
Introduction
Theory
Applications and Results
Conclusions
Future Works

5. IMC d
ysp + u w y e’ + P Q + - d + - P y

6. Modified IMC, Zheng
et al. (1994) d
ysp + u w1 y e’ w + + P Q1 + - - w2 d + - Q2 P y
To address integral windup.

7. Non-linear model state-feedback control structure
ysp + u
w’1 y e’ w + + P
Q’1 + - -
w’2 d P +
Q’2 x -
x=f(x)+g(x) u
h(x(t-) y

8. On-Line Adaptation

10. Controller CA CA
Trajectory
Trajectory qc CA
Model
Setpoint
Setpoint
Filter
Filter

11. Model Predictive ControlY ai
Time i

12. k = discrete time
y(k) = model output
u(k) = change in the input (manipulated variable) defined as u(k)- u(k-1)
d(k) = unmodelled disturbance effects on the output
ai = unit step response coefficients
N = number of time intervals needed to describe the process dynamics (Note: )
ym(k) = current feedback measurement
y* (k+j) = predicted output at k+j due to input moves up to k.
In the absence of any additional information, it is assumed that

14. The Backpropagation Neural Network

15. Comparing DMC with the neural network, the following analogy may be drawn,

16. The DNNC Process Model

17. The DNNC Process Model

18. State-Space Representation of DNNC

19. State-Space Representation of DNNC

20. State-Space Representation of DNNC

21. Stability of the DNNC Process Model

22. DNNC Controller

23. Stability of the DNNC Process Model

24. Stability of the DNNC Process Model

25. Extension of the DNNC Model to the MIMO Case in IMC Framework

26. Neuro-Statistical Model

29. Upper
Mean
Actual
MPV
Lower

33. Typical multi-layer DNNC process model.

34. Alternative Conditional ExpectationY
(Y)
(X)

35. NN Prediction
No. Epochs: 200
No. Hidden Nodes: 10
No. Epochs: 5
No. Hidden Nodes: 1

37. Typical multi-layer DNNC process model.

38. Controller CA CA
Trajectory
Trajectory qc CA
Model
Setpoint
Setpoint
Filter
Filter

39. Process Model c (t)= exp ( -  t ). h(t) : Fouling coefficient
c(t) : Deactivation coefficient
CA : effluent concentration, the controlled variable
qc : coolant flow rate, the manipulated variable
q : feed flow rate, disturbance
CAf : feed concentration
Tf : feed temperatures
Tcf : coolant inlet temperature

57. Conclusions
The DNNC strategy differs from previous neural network controllers because the network structure is very simple, having limited nodes in the input and hidden layers.
As a result of its simplicity, the DNNC design and implementation are easier than other control strategies such as conventional and hybrid neural networks.
In addition to offering a better initialization of network weights, the inverse of the process is exact and does not involve approximation error.
DNNC’s ability to model nonlinear process behavior does not appear to suffer as a result of its simplicity.
For the nonlinear, time varying case, the performance of DNNC was compared to the PID control strategy.
In comparison with PID control, DNNC showed significant improvement with faster response time toward the setpoint for the servo problem.
The DNNC strategy is also able to reject unmodeled disturbances more effectively.
DNNC showed excellent performance in controlling the complex processes in the region where the PID controller failed.
It has been shown that the DNNC controller strategy is robust enough to perform well over a wide range of operating conditions.

58. IMC d
ysp + u w y e’ + P Q + - d + - P y

59. Modified IMC, Zheng
et al. (1994) d
ysp + u w1 y e’ w + + P Q1 + - - w2 d + - Q2 P y
To address integral windup.

60. Non-linear model state-feedback control structure
ysp + u
w’1 y e’ w + + P
Q’1 + - -
w’2 d P +
Q’2 x -
x=f(x)+g(x) u
h(x(t-) y

61. Future Works
The integration of the DNNC and the shortest-prediction-horizon nonlinear model-predictive
No assumption regarding the uncertainty in input and output
Use of fuzzy logic techniques.