Parameter Estimation of Switched Hammerstein Systems
Jing Zhang
University of Chinese Academy of Sciences (CAS), Beijing 100049, P. R. China;

arXiv:1210.8296v5 [] 17 May 2017

The Key Laboratory of Systems and Control, CAS, Beijing 100080, P. R. China
Email: zhangjing410@mails.ucas.ac.cn

Han-Fu Chen
The Key Laboratory of Systems and Control, CAS, Beijing 100080, P. R. China
Email: hfchen@iss.ac.cn

Abstract This paper deals with the parameter estimation problem of the Single-Input-SingleOutput (SISO) switched Hammerstein system. Suppose that the switching law is arbitrary but
can be observed online. All subsystems are parameterized and the Recursive Least Squares (RLS)
algorithm is applied to estimate their parameters. To overcome the difficulty caused by coupling of
data from different subsystems, the concept intrinsic switch is introduced. Two cases are considered:
i) The input is taken to be a sequence of independent identically distributed (i.i.d.) random variables
when identification is the only purpose; ii) A diminishingly excited signal is superimposed on the
control when the adaptive control law is given. The strong consistency of the estimates in both
cases is established and a simulation example is given to verify the theoretical analysis.
Key words SISO switched Hammerstein system, RLS algorithm, intrinsic switch, diminishing
excitation, strong consistency.

1

Introduction

Because of importance in engineering applications, the identification and control of switched
systems have been active research areas for years[1] . Concerning parameter identification of switched
systems, a survey is given in [7].
The switched systems can roughly be divided into two classes: systems with an arbitrary switching mechanism and systems governed by a constrained switching law, such as the Markovian switching rule. In the existing literature there are many papers on Markov Jump Systems, see, e.g., [8]
and the references therein. The Markov models are also considered in [2, 3] for purposes of anomaly
detection.
By using the algebraic geometry as the key tool and under the assumption that the number of
subsystems, the subsystem orders, and the switching sequence are unknown, the author of [9] provides an algorithm to recursively estimate the unknown parameters of the discrete-time Switched
∗ This study is supported by National Natural Science Foundation of China under Grants 61273193,
61120106011, 61134013, and by the National Center for Mathematics and Interdisciplinary Sciences, Chinese
Academy of Sciences. The draft has been accepted for publication by Acta Mathematicae Applicatae Sinica
(http://link.springer.com/journal/10255).

1

Auto-Regressive eXogenous (SARX) model, and gives the algorithm a convergence analysis. However, in the convergence analysis given in [9] no unpredictable disturbance is taken into account,
despite the examples given there are with noises. While the authors of [10] tackle the SARX model
with noises; they suggest an algorithm that alternates between data designation to submodels
and parameter update, but do not prove its convergence. Recently, in transportation community,
Zhang et. al [4, 5, 6] leverage Least Squares (LS) methods to ensure flow conservation and estimate
Origin-Destination (OD) flow demand matrices, which have been demonstrated pretty effective and
efficient, thus motivating our current work to consider a recursive version of LS.
In this work, we consider parameter estimation of the Single-Input-Single-Output (SISO) switched
Hammerstein system and assume that the switching law is arbitrary but can be observed online.
We will handle two cases:
i) In the case where identifying the system is the only concern, we take the system input as a
sequence of i.i.d. random variables. It is assumed that the nonlinear function of each subsystem
can be expanded to a linear combination of continuous base functions.
ii) In the case where the adaptive control has been designed for the system, we apply the
diminishing excitation technique[11] to recursively estimate the unknown parameters. In this case,
we assume that the continuous base functions, a linear combination of which the nonlinear part of
each subsystem can be expanded to, are monomials.
The rest of the paper is organized as follows. The problem is formulated in Section 2, and the
parameter estimation algorithm is constructed in Section 3. In Section 4 we prove that the estimates
given by the proposed algorithm are strongly consistent, and then we provide a simulation example
in Section 5. Some concluding remarks are given in Section 6. Appendix at the end is used to load
proof details.

2

Problem Formulation

The SISO switched Hammerstein system considered in the paper is presented in Fig. 1. It
contains a finite number of Hammerstein subsystems, each of which consists of a static nonlinear
G (·) followed by an ARX subsystem in cascade.











We assume that there are J subsystems, and consider the case where the switch mechanism is
available. To be precise, the mapping λ (·)
λ

N−
→ {1, 2, . . . , J}
k 7→ λk
2

can be observed online, where N represents the set of all nonnegative integers, and λk denotes the
serial number of the Hammerstein subsystem that operates at time k. Besides, the orders p, q of all
ARX subsystems are supposed to be the same and known. Moreover, Gj (·), ∀j ∈ {1, . . . , J}, can
be expressed as a linear combination of r basis functions: g1 (·) , . . . , gr (·).
By setting
∆

k) p
Aλk (z) =1 + a1 k z + · · · + a(λ
p z ,

∆

(λ )

k ) z q−1 ,
Bλk (z) =b1 k + b2 k z + · · · + b(λ
q
r
X
∆
(λ )
Gλk (·) =
cl k gl (·),
(λ )

(λ )

l=1

the system can be described as


 vk = Gλk (uk ) ,
Aλk (z) yk+1 = Bλk (z) vk + ξk+1 , k ≥ 0;


uk , 0, vk , 0, ξk+1 , 0, yk+1 , 0, k < 0,

(1)

where uk is the input, vk is the unmeasurable internal signal generated by Gλk (·), yk is the output,
ξk is the driven noise, and z denotes the backward shift operator, zyk = yk−1 .
On the other hand, we set


(λ )
−a1 k
1 ··· 0


..
.
..


. ..
.
0

(k) ∆ 
Ã = 
,

..
.. . .


.
.
.
1


(λ
)
−ah k+h−1 0 · · · 0
h×h


(λ ) (λ )
(λ ) (λ )
b1 k c 1 k
···
b1 k c r k

∆ 
..
..

B̃ (k) = 
,
.
.


(λk+h−1 ) (λk+h−1 )
(λk+h−1 ) (λk+h−1 )
bh
c1
· · · bh
cr
h×r
∆

∆

∆

(j)
C τ , [1 0 · · · 0]1×h , and ũτk , [g1 (uk ) · · · gr (uk )]1×r , where h = max{p, q}, al(j) = 0, bm
= 0, for
l > p, m > q, j ∈ {1, . . . , J}. Then System (1) can be expressed in the state space form as follows:

(k)
(k)

 xk+1 = Ã xk + B̃ ũk + Cξk+1 ,
yk = C τ xk ,
(2)

 τ
x0 = [y0 0 · · · 0]1×h = [0 0 · · · 0]1×h .

Remark 1 It is seen that Ã(k) and B̃ (k) take values in the finite sets, which will be denoted by
{A(1) , . . . , A(S1 ) } and {B (1) , . . . , B (S2 ) }, respectively.
We make the following assumption on the system.
(H0) For each j ∈ {1, 2, . . . , J}, λ−1 ({j}) is an infinite subsequence of N, and
λ−1 ({j1 }) ∩ λ−1 ({j2 }) = ∅, ∀ 1 ≤ j1 6= j2 ≤ J,
[J
λ−1 ({j}) = N.
j=1

3

Remark 2 By (H0) we preclude those subsystems that only operate for a finite number of times;
this is reasonable when processing parameter identification task.
For System (1), the parameter estimation problem is to recursively estimate the unknown param∞
(j)
(j)
(j)
(j)
(j)
eters a(j)
1 , . . . , ap , b1 , . . . , bq , c1 , . . . , cr , ∀j ∈ {1, . . . , J}, based on the designed input {uk }k=0
∞
and the measured output {yk }k=1 .

3

Estimation Algorithm


∞
Let j ∈ {1, 2, . . . , J} be arbitrarily fixed. By (H0) we are able to write λ−1 ({j}) = kt(j) t=0

∞
with kl(j) < ks(j) whenever 0 ≤ l < s. Clearly kt(j) t=0 denotes all the times at which the jth
Hammerstein subsystem operates; we have kt(j) −−−→ ∞. It is worth noting that yk(j) +1 is generated
t→∞

t

by the jth subsystem, while yk(j) −d , ∀d ∈ {0, . . . , p − 1}, is not necessarily the output of the jth
t
subsystem.
h
i
Let us introduce a concept named intrinsic switch. Corresponding to yk(j) · · · yk(j) +1−p , we
t
t


(j)(t)
set n(j)(t) , n0(j)(t) · · · np−1
, where n(j)(t)
, d ∈ {0, . . . , p − 1} denotes the serial number of the
d
Hammerstein subsystem that generates yk(j) −d . It is seen that n(j)(t) is among J p , K different
t
combinations. From now on, we say an intrinsic switch occurs whenever n(j)(t) changes. Evidently,
∞
we may partition {t}t=0 into K subsequences {t(κ)
m , m ≥ 0}, κ = 1, . . . , K, such that for each κ ∈
(κ)
{1, . . . , K}, n(j)(tm ) is independent of m. It is noticed that there exists at least one κ ∈ {1, . . . , K}
∞

such that {t(κ)
m , m ≥ 0} is an infinite subsequence of {t}t=0 .
Remark 3 The term “intrinsic switch” should be distinguished from “switch;” “switch” indicates the behavior that System (1) jumps from one Hammerstein subsystem to another.
By the notation introduced above, we know the jth Hammerstein subsystem works by the
following equation:

(j) p
1 + a(j)
yk(j) +1
1 z + · · · + ap z
t

(j)
(j) q−1
= b(j)
1 + b2 z + · · · + bq z

r
X

c(j)
l gl (uk(j) ) + ξk(j) +1 , t = 0, 1, . . . .
t

l=1

(3)

t

Denoting by


(j)
(j) (j)
(j)
(j) (j)
(j) (j) τ
θ(j) , −a(j)
b(j)
1 · · · − ap
1 c1 · · · b1 cr · · · bq c1 · · · bq cr
and
h


ϕ(j)
···
t , yk(j) · · · yk(j) +1−p g1 uk(j)
t
t
t





 iτ
gr uk(j) · · · g1 uk(j) +1−q · · · gr uk(j) +1−q
t

t

t

the parameters in the jth regression subsystem and the regressor, respectively, we rewrite (3) as
yk(j) +1 = θ(j)τ ϕ(j)
t + ξk(j) +1 , t ≥ 0.
t

t

4

(4)


Let {θt(j) }t≥1 be the estimates of θ(j) . Set θ0(j) arbitrarily and P0(j) , α0(j) I with some α0(j) ∈ 0, 1e .
The RLS algorithm[11] estimating θ(j) is defined as follows
(j) (j)
(j)τ
(j)
(j)
=θt(j) + ã(j)
θt+1
t Pt ϕt (yk(j) +1 − θt ϕt ),

(5)

t

1
,
1 + ϕt Pt(j) ϕ(j)
t
(j) (j) (j)τ
(j)
=Pt(j) − ã(j)
t Pt ϕt ϕt Pt ,
h


= yk(j) · · · yk(j) +1−p g1 uk(j) · · ·
t
t
t





 iτ
gr uk(j) · · · g1 uk(j) +1−q · · · gr uk(j) +1−q
.

ã(j)
t =
(j)
Pt+1

ϕ(j)
t

(j)τ

t

t

(j)
By (6) and (7) it follows that Pt+1

−1

=

t

Pt

i=0

+
ϕi(j) ϕ(j)τ
i

(6)
(7)

(8)

1
(j) I.
α0

(j)
(j)
(j)
(j)
The estimates of b(j)
1 , . . . , bq , c1 , . . . , cr can be derived from {θt }t≥1 under some identifiable
[12][13][14]
.
conditions

4

Convergence Analysis
Let (Ω, F , P ) be the basic probability space. The following assumptions are to be used.
(H1) {ξk , Fk } is a martingale difference sequence[15] with
h
i
β
supk E |ξk+1 | Fk < ∞ a.s., β ≥ 2,

where {Fk } is a sequence of nondecreasing sub σ-algebras of F .
(H1') {ξk , Fk } is a martingale difference sequence with
supk |ξk | ≤ W < ∞ a.s.,
where W is a positive constant, and {Fk } is a sequence of nondecreasing sub σ-algebras of F .
Pk
(H2) limk→∞ k1 i=1 ξi2 = Rξ > 0 a.s., where Rξ is a constant.
(H3) {1, g1 (·) , . . . , gr (·)} is linearly independent over some interval [a, b], and gl (·), ∀l ∈
{1, . . . , r} , is continuous on [a, b].
t
P
(H5) There exists a γ > 0 such that as t → ∞,
y 2(j) = O (tγ ) a.s., ∀d ∈ {0, . . . , p − 1}.
i=0 ki −d

(H5') There exists a finite positive integer ñ such that kA(j1 ) A(j2 ) · · · A(jñ ) k < 1, ∀A(jm ) ∈
(1)
{A , . . . , A(S1 ) } , for m = 1, . . . , ñ, where k·k is the induced 1-norm:
kAk , max1≤d2 ≤`2

X`1
d1 =1

|ad1 d2 |, ∀A = (ad1 d2 )`1 ×`2 ∈ R`1 ×`2 .

Remark 4 Note that (H5'), as well as (H5), is a condition concerning stability of System (1).
Stability of time-varying systems is discussed in [16] by introducing an assumption similar to (H5').
For convenience of citation, we list a lemma here:
Lemma 1 (Theorem 2.8 of [11]) Let {Xk , Gk } be a matrix martingale difference sequence and
let {Mk , Gk } be an adapted sequence of random matrices with kMk k < ∞ a.s., ∀k ≥ 0. If
α

supk E [ kXk+1 k | Gk ] < ∞ a.s.
5

for some α ∈ (0, 2], then as k → ∞
k
X



1
Mi Xi+1 = O sk (α) log α +η (sα
a.s., ∀η > 0,
k (α) + e)

(9)

i=0

where sk (α) =

P

k
i=0

α

kMi k

 α1

.

We give the convergence analysis of Algorithm (5)-(8) for two cases as follows.

4.1

Case I—Using the i.i.d.-Type Input

The i.i.d.-type input is taken satisfying:
(H4) {uk } is a sequence of i.i.d. random variables with density p (·), which is positive and
continuous over [a, b], and vanishes outside [a, b]. Besides, {uk } is independent of {ξk }.
Before proving our first result (Theorem 1), we need lemmas 2-5.
Lemma 2 (Lemma 1 of [12]) If (H3) and (H4) hold, then
R , E[g1 (uk ) − µ1 · · · gr (uk ) − µr ]τ [g1 (uk ) − µ1 · · · gr (uk ) − µr ] > 0,
where µl , Egl (uk ), ∀l ∈ {1, . . . , r}.
Lemma 3 If (H1'), (H3), (H4), and (H5') hold, then yk = O (1) a.s., as k → ∞.
Proof The proof is straightforward since System (2), and thereby System (1), is a contraction
mapping.
−1
(j)
(j)
, respectively.
By λ(j)
max (t) and λmin (t) we denote the largest and smallest eigenvalue of Pt+1
The following two lemmas are motivated by Theorems 4.1 and 6.2 in [11], respectively.
Lemma 4 Assume that (H0) and (H1) hold, and that un is Fn -measurable for all n ≥ 0.
Then as t → ∞ the convergence (or divergence) rate of the estimate given by Algorithm (5)–(8) is
expressed by
!
δ(β−2)
(j)
(j)
log λmax
(t)(log log λmax
(t))
(j)
(j) 2
θt+1 − θ
=O
a.s.,
(10)
(j)
λmin
(t)
(
0, x 6= 0;
where δ (x) ,
with arbitrary constant c > 1.
c, x = 0,
Proof Applying the same method as that used in the proof of Theorem 4.1 in [11], we arrive at
the desired result.
Lemma 5 If (H0)–(H4) hold, then the following assertions are true.
1) It holds that
lim inf
t→∞

(j)
λmin
(t)
> 0 a.s.
t

(11)

2) If, in addition, (H5) holds, then the RLS estimate given by Algorithm (5)–(8) is strongly
consistent and has the following convergence rate:
!
δ(β−2)
log
t(log
log
t)
2
(j)
θt+1
− θ(j) = O
a.s.
(12)
t
6

Proof Analogously to the proof of Theorem 3 in [12], which is motivated by the proof of Theorem
6.2 in [11], we give the detailed proof of the lemma in Appendix.
We are now in a position to give and prove our first theorem.
Theorem 1 If (H0), (H1'), (H2)–(H4), and (H5') hold, then the RLS estimate given by
Algorithm (5)–(8) is strongly consistent and has the following convergence rate:
(j)
θt+1
− θ(j)

2

= O ((log t)/t) a.s.

(13)

Proof Combining Lemmas 3 and 5 yields the theorem.

4.2

Case II—Integrating the Given Adaptive Control with a Diminishingly Excited Signal

Assume the following assumption holds:
(H3') gl (x) , xl , ∀x ∈ R, ∀l ∈ {1, . . . , r}.
Let {εk } be a sequence of i.i.d. random variables with continuous distribution, and let {εk } be
independent of {ξk } with Eεk = 0, Eε2k = 1, and |εk | ≤ δ0 , where δ0 > 0 is a constant. Define[17]
vk(d) ,

εk
k /2

(14)


with  > 0 sufficiently small such that the interval 12 , 1 − (M + 1) r is nonempty, where M =
Jp + q − 1.
0
Without loss of generality, we assume {Fk } is rich enough such that ξk , vk(d) ∈ Fk . Set Fk−1 ,
σ {ξi1 , 0 ≤ i1 ≤ k, εi2 , 0 ≤ i2 ≤ k − 1}.
Motivated by Theorem 6.2 in [11], we introduce the following hypothesis.
0
0
(c)
(c)
(H4') The given adaptive control u(c)
k is Fk−1 -measurable, i.e., uk ∈ Fk−1 , ∀k, and uk =
O (1) a.s., as k → ∞.
The diminishing excitation technique[11] suggests to take
(d)
uk , u(c)
k + vk

as the actual input, where vk(d) is given by (14).
Define[17]

1
1 (c)

C
uk
1
2

U (k) , 
..

.
r−1
r−2
r−1
Cr
u(c)
Crr−2 u(c)
k
k

..

(15)

.

···

1












and v k , 


r×r

vk(d)
2
vk(d)
..
.
r
vk(d)







.

r×1

The following lemma is a corollary of Lemma 4 in [17].
∞
∞
∞
Lemma 6 Let {ks }s=0 be an infinite subsequence of {k}k=0 and let {tn }n=0 be an infinite
∞
subsequence of {t}t=0 . If (H4') holds, then we have
1

tn
X

1−r

tn

τ

U (ks ) (v ks − Ev ks ) (v ks − Ev ks ) U τ (ks ) ≥ c̃0 I a.s.

s=0

7

(16)

for all large enough tn , where c̃0 > 0 may depend on sample paths.
Proof Noticing (H4'), we obtain (16) by investigating its counterpart in [17] with s replaced
by r and δ set as 0.
Modified from Theorem 2 in [17], we have the following theorem in parallel to Theorem 1.
Theorem 2 If (H0), (H1'), (H2), and (H3')–(H5') hold, then the RLS estimate given by
Algorithm (5)–(8) is strongly consistent and has the following convergence rate:




log t
1
(j)
(j) 2
=O
a.s., ∀α ∈
θt+1 − θ
, 1 − (M + 1) r .
(17)
tα
2
Proof (outline) Bearing a resemblance to the proof of Lemma 5 (see Appendix), for simplicity
of notation, we omit the superscript (j). Reviewing the proofs of Lemma 5 and Theorem 1, we see
that to prove the present theorem, it suffices to show
!


t
X
1
1
τ
fi fi > 0 a.s., ∀α ∈
, 1 − (M + 1) r ,
(18)
lim inf α λmin
t→∞ t
2
i=0
QJ
where fi , s=1 As (z)ϕi . Applying once again the method of reduction to absurdity and the
procedure of subsequence partitioning and seeking (see Remark 6 at the end of Appendix) as that
used in the proof of Lemma 5, and reasoning similarly to the proof of Theorem 2 in [17] with
Lemmas 1 and 6 used repeatedly, we obtain the expected result.

5

Simulation Example
Consider the following system:

y2t = 1.1y2t−1 − 0.28y2t−2 + 0.5u2t−1 − 1.5u22t−1 + 2u32t−1





− 2(0.5u2t−2 − 1.5u22t−2 + 2u32t−2 ) + ξ2t ,



y2t−1 = 0.8y2t−2 − 0.15y2t−3 + 0.4u2t−2 + 1.6u22t−2 − 0.8u32t−2









(19)

− 3(0.4u2t−3 + 1.6u22t−3 − 0.8u32t−3 ) + ξ2t−1 ,
t ≥ 2.

Let us verify (H5') for System (19) first. It is seen that




1.1
1
1.1
1
(1)
(2)
A =
,A =
,
 −0.28 0 
 −0.15 0 
0.8
1
0.8
1
A(3) =
, A(4) =
.
−0.15 0
−0.28 0
Using MATLAB to calculate, we find that (H5') holds with ñ = 9.
We now assign the noise {ξk } and the excitation source {εk }, and set the initial values for
Algorithm (5)–(8). Let {ξk }k≥3 be i.i.d. and uniformly distributed on [−3, 3]. Take {εk }k≥1 to
be i.i.d. and uniformly distributed on [−2, 2] and independent of {ξk }. Set θ0(1) = θ0(2) = 0 and
P0(1) = P0(2) = 0.2I8 , where I8 denotes the 8 × 8 identity matrix.
Two types of input are taken separately to serve the parameter estimation task:
8

Case I Set uk , εk . It is noticed that all the conditions (H0), (H1'), and (H2)–(H4) are
fulfilled. Thus, by Theorem 1, the estimate given by Algorithm (5)–(8) is strongly consistent.
On the other hand, using the designed input and the collected output to execute Algorithm
(5)–(8) twice, each running 2000 steps, we obtain the recursive estimation for the parameters of
System (19) as shown by Fig. 2.
Recursive estimation of the parameters of the regression subsystem (1)

Recursive estimation of the parameters of the regression subsystem (2)

4

3
(1) (1)

b2 c2

3

(2)
b(2)
2 c3
(2)
b(2)
1 c2

2

(1) (1)

b1 c3

Estimates

1
0

(2) (2)

(1)
b(1)
1 c1

−2

b1 c1 (2)
−a2
b1 c3

0

−a(1)
2
(1)
b(1)
2 c1
(1) (1)
b1 c2

−1

−a(2)
1

1

−a(1)
1

Estimates

2

(2) (2)

−1

(2)
b(2)
2 c1

−2
−3

−3
(1) (1)

b2 c3

−4
−5
0

−4
(2)
b(2)
2 c2

200

400

600

800 1000 1200
Number of steps

1400

1600

1800

−5
0

2000

200

400

600

800 1000 1200
Number of steps

1400

1600

1800

2000

Fig. 2. Simulation results (I)
1
is the given
2 +|y
yk
k−1 |+1
εk
1
+ k0.001 . Clearly, all
2 +|y
yk
k−1 |+1

Case II Disregarding the specific control cost, we suppose that u(c)
k ,

(d)
εk
adaptive control at time k. Set vk(d) , k0.001
and uk , u(c)
k + vk =
the assumptions needed by Theorem 2 hold; hence, by Theorem 2, the estimate given by Algorithm
(5)–(8) is strongly consistent.
In this case, the corresponding simulation results are presented in Fig. 3.

Recursive estimation of the parameters of the regression subsystem (1)

Recursive estimation of the parameters of the regression subsystem (2)

4

3

(2)
b(2)
2 c3

(1) (1)

b2 c2

3

(1)
b(1)
1 c3

2

Estimates

0

(1)
b(1)
1 c1
−a(1)
2

−1

(1)
b(1)
2 c1

(2)
b(2)
1 c3

−1
(2)
b(2)
2 c1

−2

b1 c2

(2)
b(2)
1 c1

−a(2)
2

0

(1) (1)

−2

−a(2)
1

1

Estimates

−a(1)
1

1

(2)
b(2)
1 c2

2

−3

−3
(1) (1)

−4
−5
0

200

400

600

800 1000 1200
Number of steps

1400

b2 c3

−4

1600

−5
0

1800

2000

(2)
b(2)
2 c2

200

400

600

800 1000 1200
Number of steps

1400

1600

1800

2000

Fig. 3. Simulation results (II)
It is seen that in either case the simulation outcome convincingly validates the theoretical
9

analysis.
(1)
(1)
(1)
(1)
(2)
(2)
(2)
(2)
(2)
Remark 5 To derive the estimates of b(1)
1 , b2 , c1 , c2 , c3 , b1 , b2 , c1 , c2 , c3 from the simulation results, we need to introduce appropriate identifiable conditions, see, e.g., [12] or [13] or [14]
for details.

6

Concluding Remarks

In this study, we apply the RLS algorithm to estimate the parameters of each parameterized
subsystem of the SISO switched Hammerstein system, and under reasonable conditions we establish
the strong consistency of the estimates. Especially, in the second case, by using the diminishing
excitation technique, we also cater to adaptive control demands. For further work, it is of interest to
consider the case where the switch mechanism is not exactly available and to weaken the restrictions
on the noise, for example, to remove the boundedness assumption. It is also of interest to consider
the closed-loop identification problems with control costs associated[17] .

7

Appendix

Proof of Lemma 5 For simplicity of notation, we omit the superscript (j) wherever it is used
to indicate the Q
serial number of the chosen subsystem.
QJ
QJ
PJp
J
Define fi , s=1 As (z)ϕi . By expanding s=1 As (z) as s=1 As (z) = s=0 νs z s with ν0 , 1,
PJp
we have fi = s=0 νs ϕi−s and
fiτ =

J
Y


As (z) yki · · · yki +1−p g1 (uki ) · · ·

s=1


gr (uki ) · · · g1 (uki +1−q ) · · · gr (uki +1−q )
"Q
QJ
r
J
X
As (z)
(n(i) )
s=1 As (z)
Bn(i) (z)
cl 0 gl (uki −1 ) + s=1
ξki · · ·
=
0
An(i) (z)
An(i) (z)
l=1
0
0
QJ
QJ
r
X
As (z)
(n(i)
p−1 )
s=1 As (z)
B (i) (z)
cl
ξki +1−p
gl (uki −p ) + s=1
An(i) (z) np−1
An(i) (z)
p−1

J
Y
s=1
J
Y
s=1

As (z)g1 (uki ) · · ·

l=1

J
Y

p−1

As (z)gr (uki ) · · ·

s=1

As (z)g1 (uki +1−q ) · · ·

J
Y

#
As (z)gr (uki +1−q ) ,

(20)

s=1

where for each d ∈ {0, . . . , p − 1}, by n(i)
d we denote the serial number of the Hammerstein subsystem
that generates yki −d . Clearly, n(i)
∈
{1,
. . . , J}.
d

10

Using the Cauchy-Schwarz inequality, we see that
!
Jp
t
t
X
X
X
2
τ
λmin
fi fi ≤ inf
(1 + Jp)
νs2 (xτ ϕi−s )
i=0

kxk=1

s=0

i=0

≤ (1 + Jp)

Jp
X

!
νs2

λmin (t) .

(21)

s=0

Thus, in order to prove (11), we need only to show that
!
t
X
1
τ
fi fi > 0 a.s.
lim inf λmin
t→∞ t
i=0

(22)

We use the method of reduction to absurdity. If (22) were not true, then there would exist a
measurable set D such that P {D} > 0 and
!
t
X
1
fi fiτ = 0, ∀ω ∈ D.
(23)
lim inf λmin
t→∞ t
i=0
We arbitrarily choose ω0 ∈ D and fix it. By (23) we know that there exist a subsequence {tn }n≥0
of {t}t≥0 and a sequence of vectors {ηtn }n≥0 with kηtn k = 1 such that on the sample path ω0 we
have
tn
2
1 X
ηtτn fi = 0.
lim
n→∞ tn
i=0

(24)


τ
ηtn , αt(0)
· · · αt(p−1)
βt(1,1)
· · · βt(1,r)
· · · βt(q,1)
· · · βt(q,r)
.
n
n
n
n
n
n

(25)

Write ηtn as

The boundedness of {ηtn } implies the existence of its convergent subsequence. We arbitrarily choose
such a subsequence and still use the same notation as {ηtn } to denote it; accordingly, we are able
to write
τ

ηtn −−−−→ η , [α(0) · · · α(p−1) β (1,1) · · · β (1,r) · · · β (q,1) · · · β (q,r) ] ,
n→∞

where kηk = 1.
From (20) and (25) we obtain

11

(26)

("
ηtτn fi

=

QJ

(i)
n0
As (z)
αtn
Bn(i) (z) zc1
0
An(i) (z)
0
#
QJ
s=1 As (z)
αt(0)
n
An(i) (z)

QJ



s=1

(0)

(0)

· · · αtn

(i)
n0
As (z)
Bn(i) (z) zcr
0
An(i) (z)



s=1

0

0

+ ···
"

QJ


(i)
np−1
As (z)
p
···
+ αtn
B (i) (z) z c1
An(i) (z) np−1
p−1
#

QJ
QJ
(i)
np−1
(p−1)
(p−1)
p
s=1 As (z)
s=1 As (z) p−1
αtn
αtn
B (i) (z) z cr
z
An(i) (z) np−1
An(i) (z)
p−1
p−1
#
"
J
J
Y
Y
(1,r)
(1,1)
As (z) 0
As (z) · · · βtn
+ βtn
s=1

(p−1)

s=1

s=1

+ ···
"
+ β tn

(q,1)

J
Y

#)

J
Y

As (z)z q−1 · · · βtn

(q,r)

As (z)z q−1 0

s=1

s=1


τ
· g1 (uki ) · · · gr (uki ) ξki ,

(27)

which can be rewritten as
"
ηtτn fi

,

M
X

(1,m)(i)

h̃tn

z

m

M
X

···

m=0

(r,m)(i)

h̃tn

z

m

m=0

M
X

#
(0,m)(i)

h̃tn

z

m

m=0
τ

· [g1 (uki ) · · · gr (uki ) ξki ] ,

(28)

where M = Jp + q − 1,
M
X

h̃(l,m)(i)
zm =
tn

m=0

p−1
X

QJ
αt(m)
n

m=0

+

As (z)
(n(i) )
Bn(i) (z) z m+1 cl m
m
An(i) (z)
s=1

m

q−1
X

βt(m+1,l)
n

YJ
s=1

m=0

As (z)z m , l = 1, . . . , r,

(29)

and
M
X
m=0

(0,m)(i)

h̃tn

z

m

=

p−1
X

QJ
(m)

αtn

m=0

s=1 As (z) m
z .
An(i) (z)

(30)

m

Recalling the concept intrinsic switch introduced in Section 3, we
there exist K subseT see(κthat
∞
(κ1 )
2 ) , s ≥ 0} = ∅, ∀1 ≤ κ 6=
such
that
{i
,
s
≥
0}
{i
quences {i(κ)
,
s
≥
0}
,
κ
=
1,
.
.
.
,
K,
of
{i}
1
s
s
s
i=0
 (κ)

SK
(is )
(i(κ)
s )
∞
(κ)
κ2 ≤ K, κ=1 {is , s ≥ 0} = {i}i=0 , and n0
· · · np−1 , ∀κ ∈ {1, . . . , K} , is independent of
12

(i(κ)
s )
s. Since for each d ∈ {0, . . . , p − 1}, nd
depends only on κ, let us rewrite it as n(κ)
d from now on.
Obviously, there exists at least one κ ∈ {1, . . . , K} such that {i(κ)
s , s ≥ 0} is an infinite subsequence
∞
of {i}i=0 . Without loss of generality, we may assume that for each κ ∈ {1, . . . , K}, {i(κ)
s , s ≥ 0} is
∞
an infinite subsequence of {i}i=0 .
Let us rewrite (28) as
" M
#
M
M
(κ)
(κ)
X (1,m)(i(κ)
X
X
(r,m)(is )
(0,m)(is )
s ) m
τ
m
m
ηtn fi(κ) ,
h̃tn
z ···
h̃tn
z
h̃tn
z
s

m=0

m=0

m=0

 



τ
· g1 uk (κ)
· · · gr uk (κ)
ξk (κ) , κ = 1, . . . , K,
is

is

is

or, equivalently,
"
ηtτn fi(κ)
s

,

M
X

(1,m)(κ)

htn

z

m

M
X

···

m=0

(r,m)(κ)

htn

z

m

m=0

 



· g1 uk (κ)
· · · gr uk (κ)
ξk (κ)
is

is

M
X

#
(0,m)(κ)

htn

z

m

m=0
τ

is

, κ = 1, . . . , K,

(31)

(κ)
(l,m)(is )
by noticing that h̃tn
, ∀l ∈ {0, 1, . . . , r}, is independent of s.
Corresponding to (29) and (30), we have

M
X

h(l,m)(κ)
zm =
tn

m=0

p−1
X

QJ

As (z)
(n(κ) )
Bn(κ) (z) z m+1 cl m
m
An(κ) (z)
s=1

αt(m)
n

m=0

+

m

q−1
X

βt(m+1,l)
n

YJ
s=1

m=0

As (z)z m , l = 1, . . . , r, κ = 1, . . . , K,

(32)

and
M
X

p−1
X

ht(0,m)(κ)
zm =
n

m=0

QJ

As (z) m
z , κ = 1, . . . , K,
An(κ) (z)

(33)

s=1

αt(m)
n

m=0

m

where
h(l,m)(κ)
∈ R, ∀l ∈ {0, 1, . . . , r} , m ∈ {0, . . . , M } , κ ∈ {1, . . . , K}. It is seen that
tn
 (l,m)(κ)
htn
: l ∈ {0, 1, . . . , r} , m ∈ {0, . . . , M } , κ ∈ {1, . . . , K} is bounded.
∞
We now derive from (24) that there exist a κ ∈ {1, . . . , K}, an infinite subsequence of
{t}t=0 ,

∞
∞
∞
and an infinite subsequence of {tn }n=0 , where the latter two are denoted by t̃(κ)
and t̃n n=0 ,
n
n=0
respectively, such that
(κ)

lim

t̃n 
1 X

n→∞ t̃(κ)
n s=0

ηt̃τn fi(κ)
s

2

= 0.

(34)
∞

∞

Actually, it is obvious that there exist K infinite subsequences of {t}t=0 , denoted by {t(1)
n }n=0 , . . . ,
∞
and {tn(K) }n=0 , respectively, such that for each n ∈ N, it holds that
t

(κ1 )

n
1)
{i(κ
s }s=0

\

(κ2 )

t

n
= ∅, ∀1 ≤ κ1 6= κ2 ≤ K,
{is(κ2 ) }s=0

13

[K

t(κ)

κ=1
tn
X

t

n
n
{i(κ)
s }s=0 = {i}i=0 ,

(κ)

2
ηtτn fi

tn 
K X
X

=

ηtτn fi(κ)

2

s

,

(35)

κ=1 s=0

i=0

and

K
X

tn + 1 =

tn(κ) + K.

κ=1

From (35) and (24) we see that
(κ)

tn 
tn
K X
2
X
2
1
1 X
ηtτn fi = PK (κ)
ηtτn fi(κ) −−−−→ 0.
s
n→∞
tn + 1 i=0
κ=1 tn + K κ=1 s=0

(36)

We now show (34). Assume the converse: For every κ ∈ {1, . . . , K},
(κ)

lim inf
n→∞

tn 
1 X

t(κ)
n

ηtτn fi(κ)

2

s

> 0.

(37)

s=0

Then there exist a positive constant c0 and a sufficiently large positive integer N such that
(κ)

tn 
X

ηtτn fi(κ)
s

2

≥ c0 t(κ)
n , ∀κ ∈ {1, . . . , K} , ∀n ≥ N,

(38)

s=0

which leads to
(κ)

tn 
K X
X

1
PK

(κ)

κ=1 tn

+K

ηtτn fi(κ)

2

s

κ=1 s=0

or
lim inf PK
n→∞

(κ)

tn 
K X
X

1
(κ)

κ=1 tn

PK (κ)
c0
tn
, ∀n ≥ N
≥ PK κ=1
(κ)
κ=1 tn + K

+K

ηtτn fi(κ)

2

s

≥ c0 > 0,

κ=1 s=0

contradicting (36). Thus, (37) is not true, and we have shown that there exists a κ ∈ {1, . . . , K}
such that
(κ)
tn

2
1 X
ηtτn fi(κ) = 0,
lim inf (κ)
s
n→∞ tn
s=0
which implies (34).
From now on, let the κ in (34) be fixed. For simplicity of notation, we omit the superscript “˜”
in (34) and thereafter:
(κ)

lim

tn 
1 X
(κ)

n→∞ tn

ηtτn fi(κ)
s

s=0

14

2

= 0.

(39)

Recall Lemmas 1 and 2. Arguing similarly to the proof of Theorem 3 in [12], which is motivated
by the proof of Theorem 6.2 in [11], we derive from (26), (31), (32), (33), and (39) that
τ

η , [α(0) · · · α(p−1) β (1,1) · · · β (1,r) · · · β (q,1) · · · β (q,r) ] = 0,

(40)

which contradicts kηk = 1. Thus 1) is established.
We now prove 2).
To this end, recalling (H4), without loss of generality, for each n ≥ 0, we may assume un is
Fn -measurable, and therefore by Lemma 4 and 1) of Lemma 5, we need only to show there exists
a % > 0 such that
λmax (t) = O (t% ) a.s.

(41)

In fact, by (H5) it follows that
λmax (t) ≤tr

t
X

ϕi ϕτi

i=0

=O tr

t
X

1
I
+
α0
!

ϕi ϕτi

!

=O

i=0

=O

p−1
t X
X

t
X

!
2

kϕi k

i=0

yk2i −m

+

i=0 m=0
γ

q−1 X
t X
r
X
i=0 m=0 l=1
%

!
(gl (uki −m ))

=O (O (t ) + O (t)) = O (t ) a.s.,

2

(42)

where % , max (γ, 1); hence, 2) is true and the proof of Lemma 5 is completed.
Remark 6 It is observed that throughout the proof of Lemma 5, the procedure of deriving (31)–
(34), which can be characterized as “subsequence partitioning and seeking,” plays an important role;
combining this procedure with the existing techniques applied in the proofs of Theorem 6.2 in [11]
and Theorem 3 in [12] leads to the desired result.

8

Acknowledgment

The authors would like to thank Professor Hai-Tao Fang for helpful discussions and valuable
suggestions, and Dr. Bi-Qiang Mu for helpful discussions.

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16

