instlin

基于神经网络的控制工具箱

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          OVERVIEW OF FILES ASSOCIATED WITH SIMULATION OF CONTROL SYSTEMS
                   BASED ON INSTANTANEOUS LINEARIZATION
                   
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MATLAB functions for simulating control systems based on the instantanous
linearization technique. The following designs have been implemented:
o Approximate pole placement with all zeros canceled.
o Approximate pole placement without zero cancellation.
o Approximate MV1 (simple extension to minimum variance control).
o Approximate Generalized Predictive Control.


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DESCRIPTION                             
Assume that the process to be controlled has been modelled by a NNARX model:
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     y(t|t-1) = g(y(t-1),...,y(t-n),u(t-1),...,u(t-m))

If the network architecture definition and the weights have been saved in a
mat-file these tools can be used for simulating control systems based on a
linearization of the process model derived at each sampling instant. The
"process" can be a SIMULINK model or a model described by a set of differential
equations specified in a MATLAB-function. If the training data has been
obtained from a "real world" experiment (i.e., a MATLAB/SIMULINK model is not
available) model is not available the closed-loop system can also be
simulated by using the network model for simulating the process.

The NNARX model can be generated with the functions available in the
NNSYSID toolbox.


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REFERENCES
o  K.J. Astrom & B. Wittenmark: "Adaptive Control," Addison Wesley, 1995.

o  D.W. Clarke, C. Mothadi, P.S. Tuffs: "Generalized Predictive Control - 
   Part I. The Basic Algorithm," Automatica, Vol. 23, No. 2, pp.137-148, 1987.

o  M. Norgaard, P.H. Sorensen, N.K. Poulsen, O. Ravn, L.K. Hansen:
   "Intelligent Predictive Control of Nonlinear Processes Using Neural
   Networks," submitted for the 11th IEEE Int. Symp. on Int. Control (ISIC),
   Dearborn, Michigan, USA, 1996.
   
o  O. Sorensen: "Neural Networks in Control Applications," Ph.D. thesis,
   Aalborg University, Department of Control Engineering, Denmark, 1994.


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FILE OVERVIEW
Instlin  - This file
lincon   - Program for simulating approximate pole placement and minimum
           variance controllers.
lininit  - File containing design parameters for 'lincon'.
apccon   - Program for simulating approximate generalized predictive control.
apcinit  - File containing the design parameters for 'apccon'.
diophant - Function for solving Diophantine equations.
dio      - Interface to 'diophant' that is used in 'lincon'.
lintest  - A program for demonstrating lincon.
predtest - A program for demonstrating apccon.


The following functions must be available as well:
NNSYSID toolbox
shift, siggener                    - Functions used by lincon
smout, springm, spm1               - Files used for demonstration


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SUGGESTED WORKING PROCEDURE FOR TESTING THE CONCEPT ON A KNOWN PROCESS

1: BUILD THE PROCESS
Build the process in SIMULINK or write the differential equations in a
MATLAB-function. If SIMULINK is used, specify input to and output from the
model by an inport and an outport, respectively. If SIMULINK is not available, 
write the differential equations in a MATLAB-function in the format required
by the ODE45 differential equation solver.

2: MAKE AN EXPERIMENT
Use the function 'experim' for generating a data set to be used
for inferring a neural network model of the process.

3. IDENTIFY A MODEL OF THE PROCESS
Identify a neural network model with the 'nnarx' function in the NNSYSID
toolbox. 

4: SIMULATION OF CLOSED-LOOP SYSTEM
The closed-loop system is simulated using 'lincon' or 'apccon'. The design
parameters necessary for running these programs must be defined in 'lininit'
or 'apcinit', respectively. For example, the user must specify the name
of the MATLAB/SIMULINK model, controller design, sampling period, reference
signal and name of file containing architecture and weights of NNARX model