International Journal of Mathematics and Statistics Invention (IJMSI) E-ISSN: 2321 – 4767 P-ISSN: 2321 - 4759 www.ijmsi.org ǁ Volume 2 ǁ Issue 3 ǁ March 2014 ǁ PP-14-30 www.ijmsi.org 14 | P a g e The structure of determining matrices for a class of double – delay control systems Ukwu Chukwunenye Department of Mathematics, University of Jos, P.M.B 2084 Jos, Plateau State, Nigeria. ABSTRACT: This paper derived and established the structure of determining matrices for a class of double – delay autonomous linear differential systems through a sequence of lemmas, theorems, corollaries and the exploitation of key facts about permutations. The proofs were achieved using ingenious combinations of summation notations, the multinomial distribution, the greatest integer function, change of variables technique and compositions of signum and max functions. The paper has extended the results on single–delay models, with more complexity in the structure of the determining matrices. KEYWORDS: Delay, Determining, Double, Structure, Systems. I. INTRODUCTION The importance of determining matrices stems from the fact that they constitute the optimal instrumentality for the determination of Euclidean controllability and compactness of cores of Euclidean targets. See Gabasov and Kirillova (1976) and Ukwu (1992, 1996, 2013a). In sharp contrast to determining matrices, the use of indices of control systems on the one hand and the application of controllability Grammians on the other, for the investigation of the Euclidean controllability of systems can at the very best be quite computationally challenging and at the worst, mathematically intractable. Thus, determining matrices are beautiful brides for the interrogation of the controllability disposition of delay control systems. Also see Ukwu (2013a). However up-to-date review of literature on this subject reveals that there is currently no result on the structure of determining matrices for double-delay systems. This could be attributed to the severe difficulty in identifying recognizable mathematical patterns needed for inductive proof of any claimed result. Thus, this paper makes a positive contribution to knowledge by correctly establishing the structure of such determining matrices in this area of acute research need. II. MATERIALS AND METHODS The derivation of necessary and sufficient condition for the Euclidean controllability of system (1) on the interval 1 [0, ], t using determining matrices depends on 1) obtaining workable expressions for the determining equations of the n n matrices for 1 : 0, 0, 1, jt jh k 2) showing that = ( h),for j: 3) where 4) showing that 1 () Q t is a linear combination of 0 1 1 ( ), ( ), , ( ); 0, , ( 1) . n Q s Qs Q s s h n h See Ukwu (2013a). Our objective is to prosecute task (i) in all ramifications. Tasks (ii) and (iii) will be prosecuted in other papers. 2.1 Identification of Work-Based Double-Delay Autonomous Control System We consider the double-delay autonomous control system: 0 1 2 2 ; 0 (1) , 2,0, 0 (2) xt Axt Ax t h Axt h Bu t t xt t t h h
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International Journal of Mathematics and Statistics Invention (IJMSI)
(since the summation with 0r is infeasible and hence equals 0).
1 1 0
1 1 0( ),1(2[ 1] 2 ),2( [ 1])
2( 1)
2
0 ( , , )
with a leading . (27),n
n r n j r r j n
n j
v vr v v P
AA A
If we set 1 ,in (25), then2
jr n 2 1 2 2 1 2 2 1; so the n j r n j n j
Therefore (2.15) is the same expression as:summations with 1 vanish, being infeasible.2
jr n
12
1
0
2( 1)
2
0
1
1 0( ),1(2 1 2 ),2( [ 1])
1 1
1 1 0( ),1(2 1 2 ),2( [ 1])
( , , )
( , , )
, (28)
jn
r
n j
r
n
n r n j r r j n
n
n r n j r r j n
v vv v P
v vv v P
A A A
A A
with a leading 1A .
Clearly (2.16) is the same expression as:
1 1
1 1 0( ),1(2 1 2 ),2( [ 1])
2( 1)2
0 ( , , )
, (29)n
n r n j r r j n
n j
v vr v v P
A A
with a leading 2.A
Add up (27), (28) and (29) to obtain:
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1 1
1 1 0( ),1(2 1 2 ),2( [ 1])
2( 1)2
10 ( , , )
( ) .n
n r n j r r j n
n j
v vnr v v P
Q jh A A
Hence, the theorem is valid for all 1;j n this completes the proof for the case j even.
Now consider the case: j odd. Then 2n – is odd. Therefore,
1 1 1(2 ) (2 1) ( 1),
2 2 2
1 1 12 ( 2) is odd;so, (2 ( 2) (2 ( 2) 1) 1 ( 1)
2 2 2
n j n j n j
n j n j n j n j
1 1 1(2 ) (2 1) ( 1). Clearly, 2 ( 1) is even;
2 2 2
1 1 1 1so, (2 ( 1) (2 ( 1) (2[ 1] 1 ) 1 ( 1)
2 2 2 2
n j n j n j n j
n j n j n j n j
1 1 12( 1) is odd; so, (2[ 1] ) (2[ 1] 1) 1 ( 1).
2 2 2n j n j n j n j
Hence: 1( )nQ jh
1
1 0( ),1(2 2 ),2( )
( 1)
2
00 ( , , )
(30)n
n r n j r r j n
jn
v vr v v P
A A A
1
1 0( ),1(2 1 2 ),2( 1 )
( 1)1
2
10 ( , , )
(31)n
n r n j r r j n
jn
v vr v v P
A A A
1
1 0( ),1(2[ 1] 2 ),2( [ 1] 1)
( 1)1
2
20 ( , , )
(32)n
n r n j r r j n
jn
v vr v v P
A A A
Note that
as earlier established2( 1)1
1 ( 1) , 2 2
n jn j
.Therefore using the
change of variables 1r r ,in (30),we see that (30) is exactly the same expression as
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0
1
1 0( 1),1(2[ 1] 2 ),2( [ 1])
1 1
1 1 0( ),1(2[ 1] 2 ),2( [ 1])
11 ( 1)
2
00 ( , , )
2( 1)2
0 ( , , )
with a leading . (33),
n
n r n j r r j n
n
n r n j r r j n
n j
v vr v v P
n j
v vr v v P
A
A A A
A A
(31) is exactly the same expression as:
1 1
1 1 0( ),1(2[ 1] 2 ),2( [ 1])
1
2( 1)
2
0 ( , , )
with a leading ., (34)n
n r n j r r j n
n j
v vr v v P
AA A
(32) is exactly the same expression as:
1 1
1 1 0( ),1(2[ 1] 2 ),2( [ 1])
2
2( 1)
2
0 ( , , )
with a leading ., (35)n
n r n j r r j n
n j
v vr v v P
AA A
Add up (33), (34) and (35) to obtain:
1 1
1 1 0( ),1(2[ 1] 2 ),2( [ 1])
2( 1)
2
10 ( , , )
( ) , (36)n
n r n j r r j n
n j
v vnr v v P
Q jh A A
proving the theorem for j odd, for the contingency 2 .j n
Last case: – 2 < n . Then 2;but 1,forcing 1.j n j n j n We invoke theorem 3.1 to conclude
that
1 1
1 1 0( 1 ( 1)),1( 1 2 ),2( )
1)2
10 ( , , )
([ 1] ) .n
n r n n n r r
n
v vnr v v P
Q n h A A
Now set 1j n , in the expression for 1( )nQ jh , in theorem 3.2, to get
1 1
1 1 0( )),1( 1 2 ),2( )
1)2
10 ( , , )
([ 1] ) ,n
n r n r r
n
v vnr v v P
Q n h A A
exactly the same expression as in theorem 3.1. This completes the proof of theorem 3.2.
Remarks
The expressions for ( )kQ jh in theorems 3.1 and 3.2 coincide when 0,j k as should be expected.
IV. CONCLUSION
The results in this article bear eloquent testimony to the fact that we have comprehensively extended
the previous single-delay result by Ukwu (1992) together with appropriate embellishments through the
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unfolding of intricate inter–play of the greatest integer function and the permutation objects in the course of
deriving the expressions for the determining matrices.
By using the greatest integer function analysis, change of variables technique and deft application of
mathematical induction principles we were able to obtain the structure of the determining matrices for the
double–delay control model, without which the computational investigation of Euclidean controllability would
be impossible.
The mathematical icing on the cake was our deft application of the max and sgn functions and their
composite function sgn (max {.,.}) in the expressions for determining matrices. Such applications are optimal, in
the sense that they obviate the need for explicit piece–wise representations of those and many other discrete
mathematical objects and some others in the continuum.
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