1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
TECHNOLOGY (IJEET)
ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 5, Issue 1, January (2014), pp. 44-53
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IJEET
©IAEME
DECENTRALIZED STABILIZATION OF A CLASS OF LARGE SCALE
BILINEAR INTERCONNECTED SYSTEM BY OPTIMAL CONTROL
Ranjana Kumari1,
Ramanand Singh2
1
(Department of Electrical Engineering, Bhagalpur College of Engineering, P.O. Sabour,
Bhagalpur-813210, Bihar, India)
2
(Department of Electrical Engineering (Retired Professor), Bhagalpur College of Engineering,
P.O. Sabour, Bhagalpur-813210, Bihar, India)
ABSTRACT
A computationally simple aggregation procedure based on Algebraic Riccati Equation when
the interaction terms of each subsystem of a large scale linear interconnected system are aggregated
with the state matrix has been very recently reported in literature. The same has been extended for
large scale bilinear interconnected system. Optimal controls generated from the solution of the
Algebraic Riccati Equations for the resulting decoupled subsystems are the desired decentralized
stabilizing controls which guarantee the stability of the composite system with nearly optimal
response and minimum cost of control energy. The procedure has been illustrated numerically.
Keywords: Aggregation, decentralized, decoupled, optimal control.
1. INTRODUCTION
Decentralized stabilization of large scale linear, bilinear, non-linear and stochastic
interconnected systems etc. have been studied by various methods. The aim of the present work is to
continue the further study of a computationally simple method in which the interaction terms of each
subsystem is aggregated with the state matrix resulting in complete decoupling of the subsystems so
that the decentralized stabilizing feedback control gain coefficients can be computed very easily.
Two methods of aforesaid aggregation have been reported in the literature. The first method based on
Liapunov function has been studied in [1] in which the basic methodology has been developed for
linear interconnected system and the same has been extended for non-linear interconnected system in
[2] and stochastic interconnected system in [12]. But there are two drawbacks in the method. The
௡ ሺ௡೔ ାଵሻ
method requires the solution of ೔ ଶ
linear algebraic equations for generating Liapunov function
44

2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
for the interaction free part of each subsystem of order ݊௜ . Secondly, if the interaction free parts are
unstable, these have to be first stabilized by local controls. After a long gap, these two drawbacks
were removed in [3] where the basic methodology for linear interconnected system was developed
and the same was extended for stochastic bilinear interconnected system in [4] and stochastic linear
interconnected system in [5]. This aggregation method is based on Algebraic Riccati equation for the
interaction free part and requires the solution of only ሺ݊௜ െ 1ሻ non-linear algebraic equations for the
interaction free part of the subsystem of order ݊௜ by using the simple method in [6]. The method was
further improved in [7].
However, after another long gap, the present authors in [8] have observed very recently that
in the aggregation procedure of the above authors in [3]-[7], there is a major gap affecting the results.
This has been illustrated considering the case of [3] and [7] .The same is reproduced here for ready
reference. In the aggregation procedure of [3],[7], the authors have considered the following
inequalities:
In [3]:
In [7]:
‫ ܫ‬൑
‫ ܫ‬൒
ௌ೔
ఉ೔
ൌ
ௌ೔
షభ തതത షభ
ீ೔ ோഢ ሺீ೔ ሻ೅
ఉ೔೘ೌೣ
ൌ
ఉ೔
(1a)
షభ
షభ
ீ೔ ோ೔ ሺீ೔ ሻ೅
ఉ೔೘ೌೣ
(2a)
ത
where the real symmetric positive definite matrices ܴ௜ in (1a) and ܴ௜ in (2a) are obtained as
the solution of the algebraic Riccati equation for the interaction free part of the ݅ ௧௛ subsystem. ‫ܩ‬௜
results from the majorisation of the interaction terms and is a real positive definite diagonal matrix. It
follows that ܵ௜ is a real symmetric positive definite matrix whose all elements are positive. Hence the
Eigen values of ܵ௜ are all real and positive. In (1a), ߚ௜ is the minimum Eigen value of ܵ௜ . In (2a),
ߚ௜௠௔௫ is the maximum Eigen value of ܵ௜ . The inequalities (1a) and (2a) cannot be true since ܵ௜ is not
a diagonal matrix and its elements are all positive.
Hence the above said major gap in the aggregation procedure of [3]-[7] based on Algebraic
Riccati Equation when the interaction terms of each subsystem of a large scale interconnected system
are aggregated with the state matrix has been removed very recently by the present authors in [8] by
suggesting an alternative computationally simple aggregation procedure. In [8] the basic
methodology has been developed for large scale linear interconnected system. The aim of the
present work is to extend the same for large scale bilinear interconnected system. The class of
bilinear interconnected system considered is the same as in [9]. On aggregation, the resulting
decoupled subsystems have the same form as in [8]. Hence as in [8] the optimal controls generated
from the solution of the Algebraic Riccati Equations for the decoupled subsystems are the desired
decentralized stabilizing controls which stabilize the composite system with nearly optimal response
and minimum cost of control energy. The procedure has been illustrated numerically for a large scale
bilinear system consisting of three subsystems each of second order as in [9].
II PROBLEM FORMULATION
As in [9], a large scale time-invariant system is considered with bilinear interconnection:
‫ݔ‬ప ൌ ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ ܾ௜ ‫ݑ‬௜ ൅ ܽ௜ ‫ݔ‬௟ ் ∑ே
ሶ
௝ୀଵ,௝ஷ௜,௟ ‫ܣ‬௜௝ ‫ݔ‬௝ , ݅ ൌ 1, 2, … , ܰ, ݈ ൌ ቄ
45
1, ݅ ൌ ܰ
ቅ
݅ ൅ 1, ݅ ് ܰ
(1)

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
In (1), in the interaction-free part of the ݅ ௧௛ subsystem, ‫ݔ‬௜ is the ݊௜ ‫ 1ݔ‬state vector, ‫ݑ‬௜ the
scalar control, ‫ܣ‬௜௜ is ܽ݊ ݊௜ ‫݊ ݔ‬௜ coefficient matrix and ܾ௜ is the ݊௜ ܺ 1 driving vector. It is assumed
that (‫ܣ‬௜௜ , ܾ௜ ) is in companion form. In the interaction terms, ‫ݔ‬௝ is the ݊௝ ܺ 1 state vector, ‫ܣ‬௜௝ are
݊௜ ‫݊ ݔ‬௝ constant real matrices, ܽ௜ is an ݊௜ ܺ 1 constant real vector, ‫ݔ‬௟ is an ݊௜ ‫ 1 ݔ‬state vector and as
in [9], it is assumed that:
ԡ‫ݔ‬௜ ԡ ൑ ܷ௜ ଴ , ݅ ൌ 1, 2, … , ܰ
ԡ‫ݔ‬௟ ԡ ൑ ܷ௟ ଴ , ݅ ൌ 1, 2, … , ܰ
where ܷ௜ ଴ , ܷ௟ ଴ are positive constants. The problem to be studied is the determination of the
decentralized (1 x ni) state feedback control gain vector ࢑࢏ for generating the decentralized control:
‫ݑ‬௜ ൌ ࢑࢏ ‫ݔ‬௜ , ݅ ൌ 1, 2, … , ܰ
(2)
for each of the ݅ ௧௛ subsystem of equation (1) such that the composite system is stabilized with
optimal response and minimum cost of control energy.
III.
AGGREGATION-DECOMPOSITION AND DECOUPLED SUBSYSTEMS
It is known that the optimal feedback control ‫ݑ‬௜ for the interaction-free part of the ݅ ௧௛
subsystem of equation (1) which minimizes the quadratic performance criterion:
‫ ܫ‬ൌ ‫׬‬଴ ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ൅ ߣ௜ ‫ݑ‬௜ ଶ ሻ ݀‫ݐ‬
∞
is given by:
‫ݑ‬௜ ൌ ݇௜ ‫ݔ‬௜ , ݅ ൌ 1, 2, … , ܰ
ଵ
݇௜ ൌ െ ఒ ܾ௜ ் ܴ௜
೔
with:
(3)
(4)
where ܴ௜ is an ݊௜ ‫݊ ݔ‬௜ real symmetric positive definite matrix given as the solution of the Algebraic
Riccati equation:
ଵ
ఒ೔
ܴ௜ ܾ௜ ܾ௜் ܴ௜ ൌ ‫ܴ ்ܣ‬௜ ൅ ܴ௜ ‫ܣ‬௜௜ ൅ ܳ௜
௜௜
(5)
In the equations (3), (4) and (5), ߣ௜ is a positive constant and ܳ௜ is an ݊௜ ܺ ݊௜ real symmetric positive
definite matrix. Hence it follows that:
‫ݔ‬௜ ் ሺఒ ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜ ൌ ‫ݔ‬௜ ் ሺ‫ܴ ்ܣ‬௜ ൅ ܴ௜ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜
௜௜
ଵ
೔
(6)
Hence for the interconnected subsystems in (1), (proof in Appendix 1):
∑ே ‫ݔ‬௜ ் ሺ
௜ୀଵ
ଵ
ఒ೔
்
்
ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜ ൌ ∑ே ‫ݔ‬௜ ் ሺ‫ܴ ்ܣ‬௜ ൅ ܴ௜ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ∑ே ∑ே
௜௜
௜ୀଵ
௜ୀଵ ௝ୀଵ,௝ஷ௜,௟ ሺ2‫ݔ‬௜ ܴ௜ ܽ௜ ‫ݔ‬௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൅
൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௜ୀଵ
(7)
46

4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
Interaction terms in (7) can be bounded as (proof in Appendix 2):
ே
்
்
்
∑ே ∑ே
௜ୀଵ ௝ୀଵ,௝ஷ௜,௟ሺ2‫ݔ‬௜ ܴ௜ ܽ௜ ‫ݔ‬௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൑ ∑௜ୀଵሺ‫ݔ‬௜ ‫ܯ‬௜ ‫ݔ‬௜ ሻ
(8)
where ‫ܯ‬௜ is an ݊௜ ‫݊ ݔ‬௜ real positive definite diagonal matrix whose elements depend upon the
elements of ܴ௜ , ܽ௜ , ‫ܣ‬௜௝ , ฮܷ௜ ଴ ฮ, ฮܷ௟ ଴ ฮ. The term ‫ݔ‬௜ ் ‫ܯ‬௜ ‫ݔ‬௜ in the R.H.S. of inequality (8) can be
bounded as (proof in Appendix 3):
‫ݔ‬௜ ் ‫ܯ‬௜ ‫ݔ‬௜ ൒ 2݉௜ ‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜
(9)
where ݉௜ is a real and positive number given by:
݉௜ ൌ
ఈ೔
ଶఉ೔
(10)
ߙ௜ is the lowest diagonal element of ‫ܯ‬௜ and ߚ௜ is the highest diagonal element of ݊௜ ‫݊ ݔ‬௜ real positive
definite diagonal matrix ܵ௜ which is given by:
‫ۍ‬
‫ێ‬
ܵ௜ ൌ ‫ێ‬
‫ێ‬
‫ۏ‬
∑௡೔ ‫ݎ‬௜௪ଵ
௪ୀଵ
‫ڭ‬
0
‫ڮ‬
௡೔
∑௪ୀଵ ‫ݎ‬௜௪ଶ
‫ڮ‬
‫ڰ‬
0
‫ې‬
‫ۑ‬
‫ڭ‬
‫ۑ‬
‫ۑ‬
௡೔
∑௪ୀଵ ‫ݎ‬௜௪௡೔ ‫ے‬
where ‫ ݒ ,ݓ‬ൌ 1, 2, . . . , ݊௜ and ‫ݎ‬௜௪௩ is the element in the ‫ ݓ‬௧௛ row and ‫ ݒ‬௧௛ column of ܴ௜ . Using (8)
and (9), the equation (7) is converted to the following inequality:
∑ே ‫ݔ‬௜ ் ሺ ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜
௜ୀଵ
ଵ
ఒ೔
ழ
வ
∑ே ‫ݔ‬௜ ் ሺ‫ܴ ்ܣ‬௜ ൅ ܴ௜ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ∑ே 2݉௜ ‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜ ൅
௜ୀଵ
௜௜
௜ୀଵ
൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௜ୀଵ
(11)
The inequality (11) can be replaced by an equation by replacing ܴ௜ by another ݊௜ ܺ ݊௜ real symmetric
positive definite matrix ࡾ࢏ :
∑ே ‫ݔ‬௜ ் ሺ ࡾ࢏ ܾ௜ ܾ௜் ࡾ࢏ ሻ‫ݔ‬௜ ൌ ∑ே ‫ݔ‬௜ ் ሺ‫ ࢏ࡾ ்ܣ‬൅ ࡾ࢏ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ∑ே 2݉௜ ‫ݔ‬௜ ் ࡾ࢏ ‫ݔ‬௜ ൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௜ୀଵ
௜ୀଵ
௜௜
௜ୀଵ
௜ୀଵ
ఒ
ଵ
೔
(12)
which is reduced to the following equation (Appendix 4):
ଵ
ఒ೔
where:
ࡾ࢏ ܾ௜ ܾ௜் ࡾ࢏ ൌ ‫ ࢏ࡾ ்ܣ‬൅ ࡾ࢏ ‫ܣ‬௠௜ ൅ ܳ௜
௠௜
(13)
‫ܣ‬௠௜ ‫ܣ ؜‬௜௜ ൅ ݉௜ ‫ܫ‬
This is the Algebraic Riccati equation for the decoupled subsystems:
‫ݔ‬ప ൌ ‫ܣ‬௠௜ ‫ݔ‬௜ ൅ ܾ௜ ‫ݑ‬௜ , ݅ ൌ 1, 2, … , ܰ
ሶ
(14)
If (14) is compared with (1), it is observed that the effects of interactions have been
aggregated as ݉௜ ‫ ܫ‬into the coefficient matrix of the interaction free part of the ݅ ௧௛ subsystem so that
the N interconnected subsystems have been decomposed into the N decoupled subsystems of (14).
47

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
The positive number ݉௜ is, therefore, designated as interaction coefficient and the procedure is
called aggregation-decomposition.
IV.
DECENTRALIZED STABILIZATION BY OPTIMAL FEEDBACK CONTROL
‫ܣ‬௠௜ in equation (14) is the modified coefficient matrix of the ݅௧௛ subsystem incorporating the
maximum possible interaction effects. Hence the decentralized stabilization of interconnected
subsystems in (1) implies the stabilization of decoupled subsystems in (14). It is noted that ሺ‫ܣ‬௠௜ , ܾ௜ ሻ
in (14) is not in companion form. Hence on applying similarity transformation [10]:
‫ݔ‬ప ൌ ܲ௜ ‫ݔ‬௜
ഥ
ܲ௜ being an ݊௜ ܺ ݊௜ transformation matrix, subsystems (14) are transformed to:
‫ݔ‬ҧሶ௜ ൌ ‫ܣ‬ҧ௠௜ ‫ݔ‬ҧ௜ ൅ ܾ௜ ‫ݑ‬௜ , ݅ ൌ 1, 2, … , ܰ
(15)
where ሺ‫ܣ‬ҧ௠௜ , ܾ௜ ሻ is in companion form. Referring to [11], the optimal control function ‫ݑ‬௜ , which
minimizes the quadratic performance criteria so that the subsystems (15) and hence (14) are
stabilized with optimal response and minimum cost of control energy, is given by:
ത ഥ
‫ݑ‬௜ ൌ െ ఒ ܾ௜் ܴ௜ ‫ݔ‬ప
ଵ
ത
ൌ െ ఒ ܾ௜் ܴ௜ ܲ௜ ‫ݔ‬௜
೔
ଵ
֜ ‫ݑ‬௜ ൌ ࢑࢏ ‫ݔ‬௜ ,
ଵ
ത
‫ ࢏࢑ ݁ݎ݄݁ݓ‬ൌ െ ఒ ܾ௜் ܴ௜ ܲ௜ , ݅ ൌ 1,2, … , ܰ
೔
೔
ത
In equation (16), ܴ௜ is the solution of the Algebraic Riccati equation:
ଵ
ఒ೔
ത
ത
ത
ത
ܴ௜ ܾ௜ ܾ௜் ܴ௜ ൌ ‫ܣ‬ҧ் ܴ௜ ൅ ܴ௜ ‫ܣ‬ҧ௠௜ ൅ ܳ௜
௠௜
(16)
(17)
Hence the decentralized stabilizing control gain vectors ࢑࢏ to generate the controls ‫ݑ‬௜ as per
equation (2), which guarantees the stability of the interconnected subsystems (1), are optimal control
gain vectors for the decoupled subsystems (14) and are given by (16). Response will be slightly
deviated from the optimal and cost of control energy slightly higher than minimum due to
majorization.
V.
NUMERICAL EXAMPLE
The class of bilinear interconnected system consisting of three subsystems each of second
order as in [9] is considered corresponding to (1) as follows:
0 1
0
0
0 0
‫ݔ‬ሶ ଵ ൌ ቂ
ቃ ‫ݔ‬ଵ ൅ ቂ ቃ ‫ݑ‬ଵ ൅ ቂ
ቃ ‫ݔ‬ଶ ் ቂ
ቃ‫ݔ‬
0 0
80
െ0.3
0 1 ଷ
0 1
0
0
0 0
‫ݔ‬ଶ ൌ ቂ
ሶ
ቃ ‫ ݔ‬൅ ቂ ቃ ‫ݑ‬ଶ ൅ ቂ
ቃ‫ ் ݔ‬ቂ
ቃ‫ݔ‬
0 0 ଶ
15
െ0.2 ଷ 0 1 ଵ
0 1
0
0
0 0
‫ݔ‬ଷ ൌ ቂ
ሶ
ቃ ‫ ݔ‬൅ ቂ ቃ ‫ݑ‬ଷ ൅ ቂ
ቃ‫ ் ݔ‬ቂ
ቃ‫ݔ‬
0 0 ଷ
10
െ0.1 ଵ 0 1 ଶ
ܷଵ ଴ ൌ ܷଶ ଴ ൌ ܷଷ ଴ ൌ 0.5
48

6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
With ܳଵ ൌ ܳଶ ൌ ܳଷ ൌ ‫ܫ‬ଶ and ߣ ൌ ൅1, on solving the Algebraic Riccati equations corresponding to
(5), values of ܴଵ , ܴଶ , ܴଷ are obtained as:
1.0124228
0.0125
1.06458
ܴଵ ൌ ቂ
ቃ , ܴଶ ൌ ቂ
0.0125
0.126553
0.066667
1.0954451
0.1
ܴଷ ൌ ቂ
ቃ
0.1
0.1095441
Solving the inequality (8) ‫ܯ‬ଵ , ‫ܯ‬ଶ , ‫ܯ‬ଷ are computed as:
0.001875
‫ܯ‬ଵ ൌ ቂ
0
0
0.05
ቃ , ‫ܯ‬ଶ ൌ ቂ
0.0157
0
0.066667
ቃ,
0.070972
0.0067
0
ቃ , ‫ܯ‬ଷ ൌ ቂ
0
0.0093
0
ቃ
0.0176
ܵଵ , ܵଶ , ܵଷ are then computed.
Hence, one gets: ߙଵ ൌ 0.0019, ߙଶ ൌ 0.0067, ߙଷ ൌ 0.0093
and ߚଵ ൌ 1.0249, ߚଶ ൌ 1.1312, ߚଷ ൌ 1.1954. Then using (10),
݉ଵ ൌ 9.2692‫ ܧ‬െ 4, ݉ଶ ൌ 0.0030, ݉ଷ ൌ 0.0039. Hence the three decoupled subsystems
corresponding to (14) are obtained. Then with the transformation matrices:
ܲଵ ൌ ቂ
0.0125
0
0.0667
0
ቃ , ܲଶ ൌ ቂ
0.0002
0.0125
0
0.100
ቃ , ܲଷ ൌ ቂ
0.0667
0.0004
0
ቃ
0.100
co-efficient matrices of the transformed decoupled subsystems corresponding to (15) are obtained as:
0
‫ܣ‬ҧ௠ଵ ൌ ቂ
0
1.0000
0
ቃ , ‫ܣ‬ҧ௠ଶ ൌ ቂ
0.0019
0
1.0000
0
ቃ , ‫ܣ‬ҧ௠ଷ ൌ ቂ
0.0060
0
1.0000
ቃ
0.0078
ത ത ത
Hence ܴଵ , ܴଶ , ܴଷ are computed by solving (17). Finally, corresponding to (16), the desired
decentralized stabilizing control gain vectors are computed as:
࢑૚ ൌ ሾെ0.0125 െ 0.0127ሿ, ࢑૛ ൌ ሾെ0.0670 െ 0.1159ሿ, ࢑૜ ൌ ሾെ0.1007 െ 0.174ሿ
VI.
CONCLUSION
Computationally simple aggregation procedure developed for large scale linear
interconnected system in [8] to remove the major gap in literature has been successfully extended for
large scale bilinear interconnected system. The desired decentralized feedback co-efficients
generated from the solution of the Algebraic Riccati equations for the resulting transformed
decoupled subsystems will guarantee the stability of the composite system with optimal response and
minimum cost of control energy with slight deviation due to majorization of the interaction terms.
The results can be further extended for non-linear and stochastic interconnected systems and
even for time varying, uncertain and robust control interconnected systems and for the case with
output feedback and pole-placement. The results can also be applied for improvement of dynamic
and transient stability of multi-machine power systems etc. The procedure of the paper can be
computerized and hence is applicable for higher order systems.
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7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
REFERENCES
Journal Papers
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
A K Mahalanabis and R Singh, On decentralized feedback stabilization of large-scale
interconnected systems, International Journal of Control, Vol. 32, No. 1, 1980, 115-126.
A K Mahalanabis and R Singh, On the analysis and improvement of the transient stability of
multi-machine power systems, IEEE Transactions on Power Apparatus and Systems, Vol.
PAS-100, No. 4, April 1981, 1574-80.
K Patralekh and R Singh, Stabilization of a class of large scale linear system by suboptimal
decentralized feedback control, Institution of Engineers, Vol. 78, September 1997, 28-33.
K Patralekh and R Singh, Stabilization of a class of stochastic bilinear interconnected system
by suboptimal decentralized feedback controls, Sadhana, Vol. 24, Part 3, June 1999, 245-258.
K Patralekh and R Singh, Stabilization of a class of stochastic linear interconnected system
by suboptimal decentralized feedback controls, Institution of Engineers, Vol. 84, July 2003,
33-37.
R Singh, Optimal feedback control of Linear Time-Invariant Systems with Quadratic
criterion, Institution of Engineers, Vol. 51, September 1970, 52-55.
B C Jha, K Patralekh and R Singh, Decentralized stabilizing controllers for a class of largescale linear systems, Sadhana, Vol. 25, Part 6, December 2000, 619-630
Ranjana kumari and R singh, Decentralized Stabilization of a class of large scale linear
interconnected system by optimal control, International Journal of Electrical Engineering and
technology(IJEET), Volume 4, Issue 3, May – June 2013, pp.156-166
Siljak, D.D.,and Vukcevic, M.B. (1977), “Decentrally stabilizable linear and bilinear largescale systems”, International Journal of Control, Vol.26, pp.289-305.
Books:
[10] B C Kuo, Automatic Control Systems, (PHI, 6th Edition, 1993), 222-225.
[11] D G Schultz and J L Melsa, State Functions and Linear Control Systems, (McGraw Hill
Book Company Inc, 1967).
Proceeding papers
[12] A K Mahalanabis and R singh, On the stability of Interconnected Stochastic Systems, 8th
IFAC World Congress, Kyoto, Japan, 1981, No. 248
APPENDIX 1
To derive the equation (7), equation (6) is rewritten:
ଵ
‫ݔ‬௜ ் ሺ ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜ ൌ ‫ݔ‬௜ ் ሺ‫ܴ ்ܣ‬௜ ൅ ܴ௜ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜
௜௜
ఒ೔
ൌ ‫ݔ‬௜ ் ‫ܴ ்ܣ‬௜ ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܴ௜ ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜
௜௜
ൌ ሺ‫ݔ‬௜ ் ‫ܴ ்ܣ‬௜ ‫ݔ‬௜ ሻ் ൅ ‫ݔ‬௜ ் ܴ௜ ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ (Since ‫ݔ‬௜ ் ‫ܴ ்ܣ‬௜ ‫ݔ‬௜ is a scalar)
௜௜
௜௜
ൌ ‫ݔ‬௜ ் ܴ௜ ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܴ௜ ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜
Hence for the interaction-free parts of the ݅ ௧௛ subsystems in (1)
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8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
‫ݔ‬௜ ் ሺఒ ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜ ൌ 2‫ݔ‬௜ ் ܴ௜ ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜
ଵ
೔
Thus for the ݅ ௧௛ bilinear interconnected subsystem in (1)
்
‫ݔ‬௜ ் ሺ ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜ ൌ 2‫ݔ‬௜ ் ܴ௜ ሺ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ ܽ௜ ‫ݔ‬௟ ் ∑ே
௝ୀଵ,௝ஷ௜,௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൅ ‫ݔ‬௜ ܳ௜ ‫ݔ‬௜
ଵ
ఒ೔
்
ൌ 2‫ݔ‬௜ ் ܴ௜ ‫ܣ‬௜௜ ‫ݔ‬௜ ൅ 2‫ݔ‬௜ ் ܴ௜ ܽ௜ ‫ݔ‬௟ ் ∑ே
௝ୀଵ,௝ஷ௜,௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ൅ ‫ݔ‬௜ ܳ௜ ‫ݔ‬௜
்
்
்
֜ ‫ݔ‬௜ ் ሺ ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜ ൌ ‫ݔ‬௜ ் ሺ‫ܣ‬௜௜ ் ܴ௜ ൅ ܴ௜ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ∑ே
௝ୀଵ,௝ஷ௜,௟ ሺ2‫ݔ‬௜ ܴ௜ ܽ௜ ‫ݔ‬௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൅ ‫ݔ‬௜ ܳ௜ ‫ݔ‬௜
ଵ
ఒ೔
For the N bilinear interconnected subsystems (1)
்
்
∑ே ‫ݔ‬௜ ் ሺ ܴ௜ ܾ௜ ܾ௜் ܴ௜ ሻ‫ݔ‬௜ ൌ ∑ே ‫ݔ‬௜ ் ሺ‫ܣ‬௜௜ ் ܴ௜ ൅ ܴ௜ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ∑ே ∑ே
௜ୀଵ
௜ୀଵ
௜ୀଵ ௝ୀଵ,௝ஷ௜,௟ሺ2‫ݔ‬௜ ܴ௜ ܽ௜ ‫ݔ‬௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൅
ଵ
ఒ೔
൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௜ୀଵ
APPENDIX 2
To derive the inequality (8), it is noted that:
2‫ݔ‬௜ ் ܴ௜ ܽ௜ ‫ݔ‬௟ ் ‫ܣ‬௜௝ ‫ݔ‬௝ ൌ
ܽ௜௝ଵଵ ‫ݔ‬௝ଵ ൅ ܽ௜௝ଵଶ ‫ݔ‬௝ଶ ൅ ‫ ڮ‬൅ ܽ௜௝ଵ௡ೕ ‫ݔ‬௝௡ೕ
‫ݎ‬௜ଵଵ ܽ௜ଵ ൅ ‫ݎ‬௜ଶଵ ܽ௜ଶ ൅ ‫ ڮ‬൅ ‫ݎ‬௜௡ ଵ ܽ௜௡
‫ۍ‬
‫ې‬
‫ ܽ ݎ ۍ‬൅ ‫ ܽ ݎ‬൅ ‫ڮ‬൅ ‫ ݎ‬೔ ܽ ೔ ‫ې‬
ܽ ‫ ݔ‬൅ ܽ௜௝ଶଶ ‫ݔ‬௝ଶ ൅ ‫ ڮ‬൅ ܽ௜௝ଶ௡ೕ ‫ݔ‬௝௡ೕ
௜ଵଶ ௜ଵ
௜ଶଶ ௜ଶ
௜௡೔ ଶ ௜௡೔
‫ۑ‬
‫ ۑ‬ሾ‫ݔ‬௟ଵ ‫ݔ‬௟ଶ … ‫ݔ‬௟௡ ሿ ‫ ێ‬௜௝ଶଵ ௝ଵ
ൌ 2ሾ‫ݔ‬௜ଵ ‫ݔ‬௜ଶ … ‫ݔ‬௜௡೔ ሿ ‫ێ‬
೔
‫ڭ‬
‫ڭ‬
‫ێ‬
‫ۑ‬
‫ێ‬
‫ۑ‬
‫ݎۏ‬௜ଵ௡೔ ܽ௜ଵ ൅ ‫ݎ‬௜ଶ௡೔ ܽ௜ଶ ൅ ‫ ڮ‬൅ ‫ݎ‬௜௡೔ ௡೔ ܽ௜௡೔ ‫ے‬
‫ܽۏ‬௜௝௡೔ ଵ ‫ݔ‬௝ଵ ൅ ܽ௜௝௡೔ଶ ‫ݔ‬௝ଶ ൅ ‫ ڮ‬൅ ܽ௜௝௡೔ ௡௝ ‫ݔ‬௝௡௝ ‫ے‬
If the multiplications are carried out in the RHS one gets the terms of the form:
೔
2ሺ∑௪ୀଵ r୧୵୴ a୧୵ ሻሺa୧୨୴୵′ ሻx୧୴ x୨୵′ x௟୴ ;
௡
i, j ൌ 1,2, … , N; w, v ൌ 1, 2, … … , ݊௜ ; w’ ൌ 1,2, … , ݊௝ .
which can be bounded as below :
௡೔
௡೔
2ሺ∑௪ୀଵ r୧୵୴ a୧୵ ሻሺa୧୨୴୵′ ሻx୧୴ x୨୵′ x௟୴ ≤ หሺ∑௪ୀଵ r୧୵୴ a୧୵ ሻሺa୧୨୴୵′ ሻหሺx ଶ ୧୴ ൅ x ଶ ୨୵′ ሻ|x௟୴ |
Where r୧୵୴ represents the ‫ ݒ ,ݓ‬௧௛ element of the matrix ܴ௜ and aijvw′ the v, w′th element of the matrix
‫ܣ‬௜௝ and:
|x௟v| ൑ ሺx2 ௟1 ൅ x2 ௟2 ൅x2 ௟3 ൅ ‫ ڮ‬൅ x2 ௟௩ ൅ ‫ ڮ‬൅ x2 ௟௡௜ ሻ2 ൌ ԡx௟ ԡ ൑ ܷ௟ ଴
֜ |x௟୴ | ൑ ܷ௟ ଴
1
It is then possible to get the inequality:
2‫ݔ‬௜ ் ܴ௜ ܽ௜ ‫ݔ‬௟ ் ‫ܣ‬௜௝ ‫ݔ‬௝ ൑ ‫ݔ‬௜ ் ‫′ܦ‬௜௜ ‫ݔ‬௜ ൅ ‫ݔ‬௝ ் ‫ܦ‬௜௝ ‫ݔ‬௝
where ‫′ܦ‬௜௜ and ‫ܦ‬௜௝ are diagonal matrices whose elements are real non-negative numbers depending
upon the values of ܷ௟ ଴ and the elements of the matrices ܴ௜ , ‫ܣ‬௜௝ and ai. It then follows that the
following inequality must hold:
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9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
்
்
ே
்
்
∑ே
௝ୀଵ,௝ஷ௜,௟ ሺ2‫ݔ‬௜ ܴ௜ ܽ௜ ‫ݔ‬௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൑ ∑௝ୀଵ,௝ஷ௜,௟ ሺ‫ݔ‬௜ ‫′ܦ‬௜௜ ‫ݔ‬௜ ൅ ‫ݔ‬௝ ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ
்
்
ே
ൌ ∑ே
௝ୀଵ,௝ஷ௜,௟ ሺ‫ݔ‬௜ ‫′ܦ‬௜௜ ‫ݔ‬௜ ሻ ൅ ∑௝ୀଵ,௝ஷ௜,௟ ሺ‫ݔ‬௝ ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ
்
ൌ ሺܰ െ 2ሻሺ‫ݔ‬௜ ் ‫′ܦ‬௜௜ ‫ݔ‬௜ ሻ ൅ ∑ே
௝ୀଵ,௝ஷ௜,௟ ሺ‫ݔ‬௝ ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ
்
ൌ ‫ݔ‬௜ ் ሼሺܰ െ 2ሻ‫′ܦ‬௜௜ ሽ‫ݔ‬௜ ൅ ∑ே
௝ୀଵ,௝ஷ௜,௟ ሺ‫ݔ‬௝ ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ
்
ൌ ‫ݔ‬௜ ் ‫ܦ‬௜௜ ‫ݔ‬௜ ൅ ∑ே
௝ୀଵ,௝ஷ௜,௟ ሺ‫ݔ‬௝ ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ ሾwhere ‫ܦ‬௜௜ ൌ ሺܰ െ 2ሻ‫′ܦ‬௜௜ ሿ
்
= ∑ே
௝ୀଵ,௝ஷ௟ ሺ‫ݔ‬௝ ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ
=∑ே ሺ‫ݔ‬௝ ் ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ , taking Dil = 0, a null matrix.
௝ୀଵ
்
ே
்
֜ ∑௝ୀଵ,௝ஷ௜,௟ሺ2‫ݔ‬௜ ܴ௜ ܽ௜ ‫ݔ‬௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൑ ∑ே ሺ‫ݔ‬௝ ் ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ
௝ୀଵ
்
்
ே
ே
்
Hence: ∑ே ∑ே
௜ୀଵ
௝ୀଵ,௝ஷ௜,௟ ሺ2‫ݔ‬௜ ܴ௜ ܽ௜ ‫ݔ‬௟ ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൑ ∑௜ୀଵ ∑௝ୀଵ ሺ‫ݔ‬௝ ‫ܦ‬௜௝ ‫ݔ‬௝ ሻ
ൌ ∑ே ∑ே ሺ‫ݔ‬௜ ் ‫ܦ‬௝௜ ‫ݔ‬௜ ሻ
௜ୀଵ
௝ୀଵ
ൌ ∑ே ‫ݔ‬௜ ் ሺ∑ே ‫ܦ‬௝௜ ሻ‫ݔ‬௜
௜ୀଵ
௝ୀଵ
֜ ∑ே ∑ே
ሺ2‫ݔ‬௜ ் ܴ௜ ܽ௜ ‫ݔ‬௟ ் ‫ܣ‬௜௝ ‫ݔ‬௝ ሻ ൑ ∑ே ሺ‫ݔ‬௜ ் ‫ܯ‬௜ ‫ݔ‬௜ ሻ
௜ୀଵ
௝ୀଵ,௝ஷ௜,௟
௜ୀଵ
where ‫ܯ‬௜ ൌ ∑ே ‫ܦ‬௝௜ is in general a real positive definite ݊௜ ‫݊ ݔ‬௜ diagonal matrix.
௝ୀଵ
APPENDIX 3
In order to prove that ‫ݔ‬௜ ் ‫ܯ‬௜ ‫ݔ‬௜ ൒ 2݉௜ ‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜ , it is noted that:
‫ݎ‬௜ଵଵ ‫ݎ‬௜ଶଵ … ‫ݎ‬௜௡೔ଵ
‫ݔ‬௜ଵ
‫ݎ‬௜ଵଶ ‫ݎ‬௜ଶଶ … ‫ݎ‬௜௡೔ଶ
‫ݔ‬௜ଶ
‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜ ൌ ൣ‫ݔ‬௜ଵ ‫ݔ‬௜ଶ … ‫ݔ‬௜௡೔ ൧ ൦
൪൦ ‫ ڭ‬൪
‫ڭ‬
‫ݎ‬௜ଵ௡೔ ‫ݎ‬௜ଶ௡೔ … ‫ݎ‬௜௡೔ ௡೔ ‫ݔ‬௜௡೔
௡೔
௡೔
ൌ ∑௩ୀଵ ∑௪ୀଵ
௡೔
௡೔
≤ ∑௩ୀଵ ∑௪ୀଵ
௡೔
௡೔
= ∑௩ୀଵ ∑௪ୀଵ
௡೔
௡೔
= ∑௩ୀଵ ∑௪ୀଵ
‫ݎ‬௜௪௩ ‫ݔ‬௜௪ ‫ݔ‬௜௩
‫ݎ‬௜௪௩ ሺ‫ ݔ‬ଶ ௜௪ ൅ ‫ ݔ‬ଶ ௜௩ ሻ/2
௡೔
௡೔
ሺ‫ݎ‬௜௪௩ ‫ ݔ‬ଶ ௜௪ ሻ/2 ൅ ∑௩ୀଵ ∑௪ୀଵ ሺ ‫ݎ‬௜௪௩ ‫ ݔ‬ଶ ௜௩ ሻ/2
௡೔
௡೔
ሺ‫ݎ‬௜௩௪ ‫ ݔ‬ଶ ௜௩ ሻ/2 ൅ ∑௩ୀଵ ∑௪ୀଵ ሺ ‫ݎ‬௜௪௩ ‫ ݔ‬ଶ ௜௩ ሻ/2
Since ܴ௜ is a real symmetric positive definite matrix: ‫ݎ‬௜௩௪ ൌ ‫ݎ‬௜௪௩ , ‫ ݒ ,ݓ‬ൌ 1, 2, … , ݊௜
௡೔
௡೔
௡೔
௡೔
֜ ‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜ ൑ ∑௩ୀଵ ∑௪ୀଵ ሺ‫ݎ‬௜௪௩ ‫ ݔ‬ଶ ௜௩ ሻ/2 ൅ ∑௩ୀଵ ∑௪ୀଵ ሺ ‫ݎ‬௜௪௩ ‫ ݔ‬ଶ ௜௩ ሻ/2
௡೔
௡೔
ൌ ∑௩ୀଵ ∑௪ୀଵ ሺ‫ݎ‬௜௪௩ ‫ ݔ‬ଶ ௜௩ ሻ
௡೔
௡೔
ൌ ∑௩ୀଵሺ ∑௪ୀଵ ‫ݎ‬௜௪௩ ሻ‫ ݔ‬ଶ ௜௩
௡೔
௡೔
ൌ ∑௩ୀଵ ‫݌‬௜௩ ‫ ݔ‬ଶ ௜௩ , (where ‫݌‬௜௩ ൌ ∑௪ୀଵ ‫ݎ‬௜௪௩ )
‫ݔ‬௜ଵ
‫݌‬௜ଵ 0
… 0
‫ݔ‬௜ଶ
0 ‫݌‬௜ଶ
0
ൌ ൣ‫ݔ‬௜ଵ ‫ݔ‬௜ଶ … ‫ݔ‬௜௡೔ ൧ ൦
൪൦ ‫ ڭ‬൪
‫ڭ‬
‫ڰ‬
‫ڭ‬
… ‫݌‬௜௡௜ ‫ݔ‬௜௡೔
0
்
= ‫ݔ‬௜ ܵ௜ ‫ݔ‬௜
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10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 1, January (2014), © IAEME
∑୬୧ r
‫݌‬௜ଵ 0
… 0
‫୵ ۍ‬ୀଵ ୧୵ଵ
0 ‫݌‬௜ଶ
0
‫ڭ‬
ܵ௜ ൌ ൦
൪ ൌ ‫ێ‬
‫ڭ‬
‫ڰ‬
‫ڭ‬
‫ڭ‬
‫ێ‬
… ‫݌‬௜௡௜
0
‫ۏ‬
0
֜ ‫ݔ‬௜ ் ܵ௜ ‫ݔ‬௜ ൒ ‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜
where:
It is noted that Si is a real positive definite ݊௜ ܺ ݊௜ diagonal matrix.
֜‫ ܫ‬൒
‫ڮ‬
∑୬୧ r୧୵ଶ
୵ୀଵ
…
‫ڰ‬
0
‫ې‬
‫ڭ‬
‫ۑ‬
‫ڭ‬
‫ۑ‬
∑୬୧ r୧୵୬୧ ‫ے‬
୵ୀଵ
ଵ
ܵ
ఉ೔ ௜
where ߚ௜ is the highest diagonal element of Si. Now referring to Appendix 2, since Mi also is a real
positive definite diagonal matrix,
‫ܯ‬௜ ൒ ߙ௜ ‫ ,ܫ‬where ߙ௜ is the lowest diagonal element of ‫ܯ‬௜
ఈ
֜ ‫ܯ‬௜ ൒ ఉ೔ ܵ௜
೔
೔
֜ ‫ܯ‬௜ ൒2݉௜ ܵ௜ , where ݉௜ ൌ ଶఉ is a real and positive number
֜ ‫ݔ‬௜ ் ‫ܯ‬௜ ‫ݔ‬௜ ൒ 2݉௜ ‫ݔ‬௜ ் ܵ௜ ‫ݔ‬௜
൒ 2݉௜ ‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜
்
֜ ‫ݔ‬௜ ‫ܯ‬௜ ‫ݔ‬௜ ൒ 2݉௜ ‫ݔ‬௜ ் ܴ௜ ‫ݔ‬௜
APPENDIX 4
ఈ
೔
We have equation (12)
∑ே ‫ݔ‬௜ ் ሺ ࡾ࢏ ܾ௜ ܾ௜் ࡾ࢏ ሻ‫ݔ‬௜ ൌ ∑ே ‫ݔ‬௜ ் ሺ‫ ࢏ࡾ ்ܣ‬൅ ࡾ࢏ ‫ܣ‬௜௜ ሻ‫ݔ‬௜ ൅ ∑ே 2݉௜ ‫ݔ‬௜ ் ࡾ࢏ ‫ݔ‬௜ ൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௜ୀଵ
௜ୀଵ
௜௜
௜ୀଵ
௜ୀଵ
ଵ
ఒ೔
where ‫ܣ‬௠௜ ‫ܣ ؜‬௜௜ ൅݉௜ ‫ܫ‬
ൌ ∑ே ‫ݔ‬௜ ் ሺ‫ܣ‬௜௜ ൅݉௜ ‫ܫ‬ሻ் ࡾ࢏ ‫ݔ‬௜ ൅ ‫ݔ‬௜ ் ࡾ࢏ ሺ‫ܣ‬௜௜ ൅݉௜ ‫ܫ‬ሻ‫ݔ‬௜ ൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௜ୀଵ
௜ୀଵ
ൌ ∑ே ‫ݔ‬௜ ் ሺ‫ ࢏ࡾ ்ܣ‬൅ ࡾ࢏ ‫ܣ‬௠௜ ሻ‫ݔ‬௜ ൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௠௜
௜ୀଵ
௜ୀଵ
֜ ∑ே ‫ݔ‬௜ ் ሺ ఒ ࡾ࢏ ܾ௜ ܾ௜் ࡾ࢏ ሻ‫ݔ‬௜ ൌ ∑ே ‫ݔ‬௜ ் ሺ‫ ࢏ࡾ ்ܣ‬൅ ࡾ࢏ ‫ܣ‬௠௜ ሻ‫ݔ‬௜ ൅ ∑ே ሺ‫ݔ‬௜ ் ܳ௜ ‫ݔ‬௜ ሻ
௜ୀ1
௜ୀ1
௜ୀ1
௠௜
1
೔
֜
ఒ೔
1
ࡾ࢏ ܾ௜ ܾ௜் ࡾ࢏ ൌ ‫ ࢏ࡾ ்ܣ‬൅ ࡾ࢏ ‫ܣ‬௠௜ ൅ ܳ௜
௠௜
which is the Algebraic Riccati equation for the decoupled subsystems:
‫ݔ‬ప ൌ ‫ܣ‬௠௜ ‫ݔ‬௜ ൅ ܾ௜ ‫ݑ‬௜ , ݅ ൌ 1, 2, … , ܰ where ‫ܣ‬௠௜ ‫ܣ ؜‬௜௜ ൅ ݉௜ ‫ܫ‬
ሶ
53