NONLINEAR CONTROLLER COMPARISON ON A BENCHMARK ...
NONLINEAR CONTROLLER COMPARISON ON A BENCHMARK ... NONLINEAR CONTROLLER COMPARISON ON A BENCHMARK ...
control Lyapunov function globally asymptotically stable. Backstepping essentially tries to reduce the system to a number of subsystems in series. Then the stabilizing inputs for the subsystem closest to the output are computed, and these inputs are then treated as the outputs of the previous system, and a new set of inputs to this next subsystem are computed to guarantee stability. This process is repeated until the original control input is computed. Control laws generated by this methodol- ogy, as shown in [1], are proven to stabilize the system globally and asymptotically. However, this design procedure is the most di cult to compute and implement, and the resulting control law is both complicated and cumbersome. The rst step is to apply a variable transformation to the exible beam's non-dimensionalized state equations (2.5) to obtain: z 1 = x 1 + sin( ) z 2 = x 2 + _ cos( ) y 1 = x 3 y 2 = x 4 v = 1 1 ; 2 cos 2 ( ) cos( )(z1 ; (1 + _2 ) sin( )) + k2 mk2 g Rmmc (z2 ; _ cos( )) + kmkgu R ; m (3.18) This variable substitution and the feedback transformation from u to v (which is a non-singular transformation because the parameter is always smaller than 1) simpli ed the system equations to the following form: z_ 1 = z2 z_ 2 = ;z1 + sin( ) y_ 3 = _ y_ 4 = = v 22 (3.19)
Now we regard as the control variable and construct a control law y 1( ) that will make the translational coordinates (z 1 and z 2) globally asymptotically stable. Jankovic et.al. [1] choose y 1( )=; arctan(c 0z 2). However, y 1 is not the control vari- able, and it follows its own dynamic equations. Therefore we de ne new angular variables to implement the desired trajectories of and _ : 1 = + arctan(c 0z 2) and 2 = _ 1. Substituting into the previous system equation yields the following modi ed system equation: z_ 1 = z2 z_ 2 = ;z1 + sin( 1 ; arctan(c0z2)) _ 1 = 2 _ 2 = w w = v ; 2c3 0z2 1+c2 0z2 (;z1 + sin( )) 2 2 + c 0 1+c 2 0z2 2 (;z 2 + _ cos( )): (3.20) Now all that is required is to stabilize the -subsystem which can be done with the simple feedback w = ;K . By following the approach outlined in [1], but with the system modi ed for voltage as the input instead of torque, the following control law is obtained: u = k(I + mc2 ) m + M = ;k1(y1 + arctan(c0z2)) ; k _ k2c0(;z1 + sin( ) 2 ; 1+c2 0z2 2 + 2c0z2(;z1 + sin( )) 2 (1 + c2 0z2 2) 2 + c0(;z2 + _ cos( ) 1+c2 0z2 2 mc = p 2 (I + mc )(m + M) (3.21) The primary di culty with implementing this control is understanding the purposes of the various transformations and substitutions. Additionally, this last expression for the control input is quite complicated { this makes it di cult to assign physical mean- ing to the control parameters, c 0, k 1, k 2. The parameters used in the commissioning of this design are as follows: c 0 =1,k 1 = 10, and k 2 =1. 23
- Page 1 and 2: NONLINEAR CONTROLLER COMPARISON ON
- Page 3 and 4: BRIGHAM YOUNG UNIVERSITY GRADUATE C
- Page 5 and 6: ABSTRACT NONLINEAR CONTROLLER COMPA
- Page 7 and 8: Contents Acknowledgments vi List of
- Page 9: List of Tables 4.1 Tabular Comparis
- Page 12 and 13: 4.19 SGA: Nonlinear H1 vs. Linear O
- Page 14 and 15: Figure 1.1: TORA System One of the
- Page 16 and 17: 1.3 Literature Review In surveying
- Page 19 and 20: Chapter 2 Plant Speci cations and M
- Page 21 and 22: Figure 2.2: Mechanical Model of Fle
- Page 23 and 24: Figure 2.4: Translational Oscillati
- Page 25 and 26: 2.3 Software Set-up All of the cont
- Page 27 and 28: Chapter 3 Overview of Control Strat
- Page 29 and 30: The optimal gain matrix was given b
- Page 31 and 32: 3.3 Passivity-Based Control Certain
- Page 33: limitations, k 1 and k 2 are tuning
- Page 37 and 38: where V satis es the well known Ham
- Page 39 and 40: ecause one can quickly adjust the Q
- Page 41 and 42: Chapter 4 Simulation Results 4.1 Th
- Page 43 and 44: disturbances, whereas the hardware
- Page 45 and 46: Response in cm 4 3 2 1 0 −1 −2
- Page 47 and 48: Response in cm Response in cm 4 3 2
- Page 49 and 50: Response in cm Response in cm 5 4 3
- Page 51 and 52: Response in cm 4 3 2 1 0 −1 −2
- Page 53 and 54: Response in cm Response in cm 4 3 2
- Page 55 and 56: Response in cm Response in cm 4 3 2
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Now we regard as the control variable and construct a control law y 1( ) that<br />
will make the translational coordinates (z 1 and z 2) globally asymptotically stable.<br />
Jankovic et.al. [1] choose y 1( )=; arctan(c 0z 2). However, y 1 is not the control vari-<br />
able, and it follows its own dynamic equations. Therefore we de ne new angular<br />
variables to implement the desired trajectories of and _ : 1 = + arctan(c 0z 2) and<br />
2 = _ 1. Substituting into the previous system equation yields the following modi ed<br />
system equation:<br />
z_ 1 = z2 z_ 2 = ;z1 + sin( 1 ; arctan(c0z2)) _ 1 = 2<br />
_ 2 = w<br />
w = v ; 2c3 0z2 1+c2 0z2 (;z1 + sin( ))<br />
2<br />
2 +<br />
c 0<br />
1+c 2<br />
0z2 2<br />
(;z 2 + _ cos( )):<br />
(3.20)<br />
Now all that is required is to stabilize the -subsystem which can be done with the<br />
simple feedback w = ;K . By following the approach outlined in [1], but with the<br />
system modi ed for voltage as the input instead of torque, the following control law<br />
is obtained:<br />
u = k(I + mc2 )<br />
m + M<br />
= ;k1(y1 + arctan(c0z2)) ; k _<br />
k2c0(;z1 + sin( )<br />
2 ;<br />
1+c2 0z2 2<br />
+ 2c0z2(;z1 + sin( )) 2<br />
(1 + c2 0z2 2) 2 + c0(;z2 + _ cos( )<br />
1+c2 0z2 2<br />
mc<br />
= p<br />
2 (I + mc )(m + M)<br />
(3.21)<br />
The primary di culty with implementing this control is understanding the purposes<br />
of the various transformations and substitutions. Additionally, this last expression for<br />
the control input is quite complicated { this makes it di cult to assign physical mean-<br />
ing to the control parameters, c 0, k 1, k 2. The parameters used in the commissioning<br />
of this design are as follows: c 0 =1,k 1 = 10, and k 2 =1.<br />
23