Closed-loop Identification of MIMO Systems in the Prediction Error Framework: Data Informativity Analysis

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Closed-loop Identification of MIMO Systems in the Prediction Error Framework: Data Informativity

Analysis

Kévin Colin, Xavier Bombois, Laurent Bako, Federico Morelli

To cite this version:

Kévin Colin, Xavier Bombois, Laurent Bako, Federico Morelli. Closed-loop Identification of MIMO

Systems in the Prediction Error Framework: Data Informativity Analysis. Automatica, Elsevier, 2020,

121, pp.109171. �10.1016/j.automatica.2020.109171�. �hal-02351669�

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Closed-loop Identification of MIMO Systems in the Prediction Error Framework: Data Informativity Analysis

Kévin Colin

^a

, Xavier Bombois

^a,b

, Laurent Bako

^a

, Federico Morelli

^a

aLaboratoire Ampère, UMR CNRS 5005, Ecole Centrale de Lyon, Université de Lyon, Ecully, France

bCentre National de la Recherche Scientifique (CNRS), France

Abstract

In the Prediction Error Identification framework, it is essential that the experiment yields informative data with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity property for the identification of Multi-Inputs Multi-Outputs system in closed-loop and we derive conditions to verify if a given external excitation combined with the feedback introduced by the controller yields informative data with respect to the model structure. This study covers the case of the classical model structures used in prediction-error identification and the classical types of external excitation vectors, i.e., vectors whose elements are either multisine or filtered white noises.

Key words: System Identification; Closed-loop Identification; MIMO Systems; Prediction Error Methods; Consistency

1 Introduction

For an identification within the Prediction Error framework, it is crucial to choose an excitation that yields informative data. Indeed, the prediction error estimate is then guaranteed to be consistent (provided the chosen model structure is globally identifiable at the true parameter vector) [10,14]. In this work, we study the data informativity with respect to (w.r.t.) Multi-Input Multi-Output (MIMO) model structures for the identification of MIMO systems in the closed-loop configuration (direct closed- loop identification).

Data informativity is obtained when the input excitation is sufficiently rich to guarantee that the prediction error is distinctive for different models in the considered model structure. For Single-Input Single-Output (SISO) systems, this property has been largely studied. In this case, the data are informative if the input signal is sufficiently rich of an order that depends on the type and the complexity of the considered model structure. More pre- cisely, an input signal is sufficiently rich of an orderηif and only if its input power spectrum has a non-zero amplitude in ηdifferent frequencies in the interval ]−π,π] (see e.g. [10]). Due to renewed interest in optimal exper- Email addresses:kevin.colin@ec-lyon.fr(Kévin Colin), xavier.bombois@ec-lyon.fr(Xavier Bombois),

laurent.bako@ec-lyon.fr(Laurent Bako), federico.morelli@ec-lyon.fr(Federico Morelli).

iment design (see e.g. [3,9]), where the covariance matrix of the identified model is involved, there has been a lot of works to connect the positive definiteness of the covariance matrix to the data informativity [1,8]. In addition, necessary and sufficient conditions have been derived for the data informativity in both the open-loop and closed- loop case in [7,8]. In particular, these papers derive the minimal order of signal richness that the excitation signal must have to ensure data informativity and this is done for all classical model structures (BJ, OE, ARX, ARMAX, FIR). In the closed-loop case, this minimal order is related to the complexity of the controller present in the loop during the identification. In [7,8], it is also shown that, if the controller is sufficiently complex, the data can be informative even if the excitation signal is equal to zero (costless identification).

While the data informativity seems to be a grown-up research area in the SISO case, this cannot be said for the MIMO case. In [12], and more recently in [2], attention has been given to determine the minimal order that the controller must have to ensure that informative data are obtained when the external excitation is zero.

Two conditions have been derived, one being sufficient and the other one necessary. In the majority of the cases, though, a (nonzero) external excitation will be required to yield informative data. Up to our knowledge, in the MIMO case, there is (almost) no result about the minimal richness this external excitation must have to yield

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informative data and about how this minimal signal richness relates to the complexity of the controller. Perhaps, the only result in that matter is given in [2]. In [2], it is indeed said that an external excitation signalr(t) with a strictly positive definite power spectrum matrixΦr(ω) at allωalways yields informative data for direct closed-loop identification. This condition is of course only sufficient and is moreover very restrictive. As an example, a multisine excitation will never respect this condition.

In this paper, we will therefore derive a condition al- lowing to verify whether, for a given MIMO controller, an arbitrary external excitation r(t) yields informative data for the direct closed-loop identification of a given MIMO system in a full-order model structure. We will do that for the classical model structures used in the Prediction Error framework (FIR, ARX, ARMAX, OE, BJ) and for both multisine external excitations and filtered white noise external excitations.

As we will see in this paper, data informativity will be guaranteed in the MIMO closed-loop case if, for each channel/output, a certain matrix is full row rank. This matrix depends on the model structure complexity, on the controller coefficients and on the external excitation parametrization (i.e., amplitude, phase-shift and frequencies for multisine and filter coefficients for filtered white noise) . We will also observe that this matrix clearly sepa- rates the contributions of the controller and of the external excitation to the informativity of the collected input- output data.

This paper builds upon our previous contributions where we consider the data informativity problem for the open-loop identification of MIMO systems [4–6].

2 Notations

For a complex-valued matrix A, A^T denotes its transpose and A^∗ its conjugate transpose. A positive semi- definite (resp. definite) matrix Ais denoted Aº0 (resp.

AÂ0). We will denoteA_{i k}the (i,k)-entry of the matrixA, A_i:thei-th row of Aand A_:k thek-th column of A. The identity matrix of sizen×nis denotedIn and0n×p is the n×p matrix full of zeros. For the sake of simplicity, we will often drop the indexn×p and just write0. The notation diag(a₁,· · ·,a_n) refers to then×n diagonal matrix whose elements in its diagonal are the scalarsa₁,· · ·,a_n. For a vector x∈Rⁿ, the notation ||x||refers to the Eu- clidean norm, i.e. ||x|| =

q

x₁²+ · · · +x_n². For two integers n≤p, the setJ^n,pKis the set of consecutive integers be- tweenn andp.

For quasi-stationary signalsx(t) [10], we define the operator ¯E[x(t)]= lim

N→+∞

1 N

PN

t=1E[x(t)] where E is the ex- pectation operator.

For discrete-time model, z is the forward-shift operator. The degree of a polynomial P(X) is denoted

deg(P(X)). When X=z⁻¹, we say thatρ is the delay of P(z⁻¹) when the first non-zero coefficient is linked to z^−ρ, i.e. P(z⁻¹)=p_ρz^−ρ+p_ρ+1z^−(ρ+1)+ · · · +pnz⁻ⁿ with p_ρ6=0.

3 Prediction Error Framework in closed-loop

Consider a MIMO systemS with an input vectoru∈ Rⁿ^u and an output vector y∈Rⁿ^y, described by

S : y(t)=G₀(z)u(t)+H₀(z)e(t) (1) where G₀(z) is a stable matrix of transfer functions of dimension ny×nu, H0(z) a stable, inversely stable and monic¹ matrix of transfer functions of dimensionn_y×n_y ande∈Rⁿ^y is a vector made up of zero-mean white noise signals such that ¯E£

e(t)e^T(t)¤

=Σ0Â0. We will make the following assumption for the sake of simplicity.

Assumption 1 Assume that H₀(z) is a diagonal trans- fer function matrix, i.e., H₀(z)=diag(H_0,1(z),· · ·,H_0,n_y(z)) where each scalar transfer function H_0,i(z) (i=1,· · ·,n_y) is stable, inversely stable and monic.

As shown in Fig. 1, the system S is under feedback control with a stabilizing controller described by a matrix of (rational) transfer functionsK(z) of dimensionn_u×n_y. The reference signal is set to 0. For identification pur- pose, a quasi-stationary external excitationr∈Rⁿ^u can be added to the control effort such that the inputu is given by

u(t)= −K(z)y(t)+r(t) (2)

Fig. 1. Closed-loop configuration.

We will assume that the excitation vectorr is uncor- related with the vector of white noisese and that there is no algebraic loop in the closed loop made up ofK(z) andG0(z), i.e., there is at least a delay in all entries of the matrixK(z)G₀(z). In this work, we focus on developing conditions to get a consistent estimate of (G₀(z),H₀(z)) when considering the direct closed-loop identification ap- proach, i.e., by using the data x(t)=(y^T(t),u^T(t))^T for the identification.

We will consider two types of quasi-stationary external excitationr. The first type is a multisine where each entry

1 This means thatH₀andH₀⁻¹are stable andH₀(z= ∞)=I_n_y.

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r_kofr(k=1,· · ·,nu) is a multisine made up of sinusoids ats different frequenciesωl (l=1,· · ·,s), i.e.,

r_k(t)=

s

X

l=1

Λklcos(ωlt+Ψkl) k=1,· · ·,n_u (3)

where Λkl and Ψkl are respectively the amplitude and phase shift of the sinusoid at the frequency ωl. Note that Λkl and Ψkl can be zero for some k (k =1,· · ·,n_u) but, for each l =1,· · ·,s, there exists (at least) a value of k for which Λkl 6=0. In the second type, r is generated as r =M v via a stable transfer matrix M(z) = (M_kq(z))_(k,q)_∈

J^1,nuK×J^1,fK and a vector v=(v₁,· · ·,v_f)^T containing f independent white noises signals vq (q=1,· · ·,f) of covariance Σv Â0. In other words, each entryr_kofr is given by:

r_k(t)=

f

X

q=1

M_kq(z)v_q(t) k=1,· · ·,n_u (4)

Note that some M_kq(z) can be identically zero and that f is not necessarily equal ton_u but, for eachq=1,· · ·,f, there exists (at least) a value ofk for whichM_kq(z)6=0 .

The system S is identified within a full-order model structureM=©

(G(z,θ),H(z,θ))|θ∈D_θª

whereθ∈Rⁿ is a parameter vector and the set Dθ restricts the parameter vector θ to those values for which G(z,θ) is stable andH(z,θ) is stable and inversely stable. The model structure is said to be full-order if ∃θ0∈D_θ such that (G(z,θ0),H(z,θ0))=(G₀(z),H₀(z)). We will suppose that M is globally identifiable at the true parameter vectorθ0, i.e. (G(z,θ),H(z,θ))=(G₀(z),H₀(z))⇒θ=θ0[1,2].

Assume that we have collected a set ofNinput-output data:

Z^N= n

x(t)=¡

y^T(t), u^T(t)¢T

|t=1,· · ·,No For each (G(z,θ),H(z,θ))∈M, we can define the one-step ahead predictor ˆy(t,θ) for the output y(t) usingZ^N:

ˆ

y(t,θ)=Wy(z,θ)y(t)+Wu(z,θ)u(t)=W(z,θ)x(t) (5) W_u(z,θ)=H⁻¹(z,θ)G(z,θ) (6) W_y(z,θ)=I_n_y−H⁻¹(z,θ) (7) whereW(z,θ)=(W_y(z,θ),W_u(z,θ)).

As will be illustrated in the sequel, the data x(t)= (y^T(t),u^T(t))^T must be informative with respect toM to get the consistency of the estimate.

Definition 2 (Data Informativity [2]) Consider the framework defined above with the data x(t)=(y^T(t),u^T(t))^T collected on the true system S operated in closed-loop with a controller K(z) and a quasi-stationary external excitation r (see (2)). Consider also a model structure

M yielding the predictor y(t,ˆ θ)=W(z,θ)x(t). Define the set∆W=©

∆W(z)=W(z,θ⁰)−W(z,θ⁰⁰)|θ⁰ andθ⁰⁰ inD_θª . The data x(t)are said to be informative w.r.t. the model structureM if, for all∆W(z)∈∆W, we have

E¯£

||∆W(z)x(t)||²¤

=0=⇒∆W(z)≡0 (8) where the notation∆W(z)≡0means that∆W(e^j^ω)=0at all or almost allω∈]−π,π].

Data informativity combined with global identifiability atθ0are important properties since they imply that the prediction error criterion [10,14] defined below yields a consistent estimate ˆθN forθ0:

θˆN=arg min

θ∈Dθ

V_N¡ θ,Z^N¢

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θ,Z^N¢

= 1 N

N

X

t=1

²^T(t,θ)Σ⁻¹0 ²(t,θ) (10)

where ²(t,θ)=y(t)−yˆ(t,θ) and where Σ0 is assumed known for simplicity². The estimate ˆθN is consistent if it converges toθ0with probability equal to 1 whenN→ +∞. In [4–6], we derived conditions to ensure the data informativity in open-loop (K(z)≡0) for input vectorsu=r of the types (3)-(4) and for the classical types of model structuresMin open-loop i.e. FIR, ARX, OE and BJ model structures. In the sequel, we will study the closed-loop case. In the next section, we present the model structures considered in this study.

4 Considered model structures

For the sake of simplicity, we will here also restrict attention to the classical types of model structures i.e.

FIR, ARX, ARMAX, OE and BJ. For this purpose, let us decomposeθas follows

θ=



 θ˜ η



 (11)

where ˜θis made up by the parameters uniquely found in G(z,θ) andηcontains the rest of the parameters.

The ARMAX model structure can be described as fol-

lows 





G(z,θ) = A(z,η)⁻¹B(z, ˜θ)

H(z,θ) = A(z,η)⁻¹C(z,η) (12) where A(z,η) andC(z,η) are diagonal and monic polynomial matrices of dimension ny×ny and B(z,θ) is a polynomial matrix of dimension n_y×n_u. Note that the ARX and the FIR model structures are special cases of

2 It can however be estimated together with ˆθN (see e.g. [10, Chapter 15])

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the ARMAX one with C(z,η)=In_y in the ARX case and C(z,η)=A(z,η)=I_n_y in the FIR case.

For BJ model structures, G(z,θ) and H(z,θ) do not share common parameters:







G(z,θ) =G(z, ˜θ)

H(z,θ) = H(z,η) (13) withH(z,η)=diag(H₁(z,η),· · ·,H_n_y(z,η)).

Let us introduce some further notations for these MIMO model structures.

ARMAX/ARX/FIR: For the MIMO ARMAX/ARX/FIR model structure in (12), B(z, ˜θ) is a matrix made up of n_yn_u independently parametrized polynomials B_{i k}(z, ˜θi k)=z^−ρ^{i k}B˚_{i k}(z, ˜θi k) (i =1,· · ·,ny, k =1,· · ·,nu) where the delays ρi k can be all different and ˚B_{i k}(z, ˜θi k) is a polynomial. All H_i(z,η) = C_i(z,η)/A_i(z,η) are parametrized independently with a parameter vectorηi, i.e,Hi(z,η)=Hi(z,ηi)=Ci(z,ηi)/Ai(z,ηi) whereCi(z,ηi) and A_i(z,ηi) are monic polynomials.

The coefficients of the polynomial ˚B_{i k}are the parameters in ˜θi kwhile the ones in the polynomialsAi(z,ηi) and C_i(z,ηi) are the ones inηi. Consequently, we have that:

B˚_{i k}(z, ˜θi k)=θ˜i k,1+

deg( ˚Bi k)

X

m=1

θ˜i k,(m+1)z^−m (14)

C_i(z,ηi)=1+

deg(Ci)

X

m=1

ηi,mz⁻^m (15)

A_i(z,ηi)=1+

deg(A_i)

X

m=1

ηi,(m+deg(C_i))z^−m (16) where ˜θi k,m denotes them-th entry of ˜θi k andηi,m the m-th entry ofηi. The number of parameters to identify in ˚B_{i k},C_i and A_i is thus equal to deg( ˚B_{i k})+1, deg(C_i) and deg(A_i) respectively.

BJ/OE: For the MIMO BJ/OE model structure in (13), G(z, ˜θ) is a matrix made up of n_yn_u independently parametrized transfer functions G_{i k}(z, ˜θi k)= z^−ρ^{i k}B˚_{i k}(z, ˜θi k)/F_{i k}(z, ˜θi k) where ˚B_{i k}(z, ˜θi k) andF_{i k}(z, ˜θi k) are polynomials. The diagonal matrix H(z,θ) is com- posed by n_y independently parametrized transfer functions Hi(z,ηi) with Hi(z,ηi)=Ci(z,ηi)/Di(z,ηi) where C_i(z,ηi) andD_i(z,ηi) are monic polynomials.

The coefficients of the polynomials ˚B_{i k}andF_{i k}are the parameters in ˜θi kwhile the coefficients of the polynomi- alsC_i(z,ηi) andD_i(z,ηi) are the ones inηi. Consequently,

we have that:

B˚_{i k}(z, ˜θi k)=θ˜i k,1+

deg( ˚Bi k)

X

m=1

θ˜i k,(m+1)z^−m (17)

F_{i k}(z, ˜θi k)=1+

deg( ˚F_{i k})

X

m=1

θ˜_{i k}_{,(m+deg( ˚}_B_{i k}₎₊₁₎z^−m (18)

C_i(z,ηi)=1+

deg(C_i)

X

m=1

ηi,mz⁻^m (19)

Di(z,ηi)=1+

deg(Di)

X

m=1

ηi,(m+deg(Ci))z^−m (20)

where ˜θi k,m denotes them-th entry of ˜θi k andηi,m the m-th entry ofηi. The number of parameters to identify in B˚_{i k},F_{i k},D_i andC_i is thus equal to deg( ˚B_{i k})+1, deg(F_{i k}), deg(D_i) and deg(C_i) respectively.

For all classical model structures, the parameter vector ˜θ∈Rⁿ^˜ is the concatenation of ˜θi k (i=1,· · ·,ny,k= 1,· · ·,n_u) i.e. ˜θ =( ˜θ₁₁^T, ˜θ^T₁₂,· · ·, ˜θ_1n^T _u,· · ·,· · ·, ˜θ^T_n_y₁, ˜θ^T_n_y₂,· · ·, θ˜^Tnynu)^T. The parameter vector η∈Rⁿ^η is also the concatenation ofηi (i=1,· · ·,n_y) i.e.η=(η^T₁,η^T₂,· · ·,η^Tny)^T.

These classical model structures are globally identifiable at the true parameter vectorθ0if θ0does not lead to a pole/zero cancellation.

Index notations: In the sequel, we will use the index i∈J^1,ⁿyKto specify the entries y_i of y whilek∈J^1,nuK will be used to specify the entriesu_kofu.

5 Data informativity for MIMO systems in closed-loop

5.1 Simplification of the study

We will first see in the next theorem how the controller K(z) and the external excitationr contribute to the data informativity in the closed-loop MIMO case.

Theorem 3 Consider Definition 2 and one of the model structuresM defined in Section 4. Recall that r and e are independent. For each ∆W(z)∈∆W, we define similarly

∆Wy(z)and∆Wu(z). Then, the data x(t)are informative w.r.t. the model structureM if and only if, for all∆W(z)= (∆W_y(z),∆W_u(z))∈∆W,







∆W_y(z)−∆W_u(z)K(z)≡ 0 E¯£

||∆W_u(z)r(t)||²¤

= 0 =⇒(∆Wy,∆Wu)≡(0,0) (21)

PROOF. See Appendix A.

Remark 4 As mentioned in the introduction, a sufficient condition for the informativity is to choose r such that

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its power spectrum matrix is strictly positive definite at all frequencies, i.e. Φr(ω)Â0 ∀ω. Indeed, by using Parseval theorem E¯[||∆Wu(z)r(t)||²]=0 is equivalent to tr¡ ₁

2π

R_π

−π∆W_u(e^j^ω)Φr(ω)∆W_u^∗(e^j^ω)dω¢

=0where tr is the trace operator. Consequently, if Φr(ω)Â0 for allω, then E¯[||∆W_u(z)r(t)||²]=0 implies ∆W_u ≡0. When ∆W_u ≡0, the first equation of the left hand side of(21)is equivalent to ∆Wy ≡0. Consequently, we have informativity when Φr(ω)Â0∀ω(or at almost allω).

However, this sufficient condition for data informativity is in fact very restrictive. It will indeed never be verified for multisine excitation such as (3) or for filtered white noise excitation of the type (4)when f <n_u, while such excitation signals can of course yield informative data (see, e.g., Section 9).

In the next theorem, we will show that we can simplify the result of Theorem 3.

Theorem 5 Consider Definition 2, Theorem 3 and one of the model structuresMdefined in Section 4. Define the sets

∆W,i ={∆W_i:(z) | ∆W_i:(z)is the ith row of∆W(z)∈∆W} (i =1,· · ·,n_y). For each ∆W_i:(z)∈∆W,i, we define simi- larly ∆Wu,i:(z) and ∆Wy,i:(z) which are the i -th row of

∆W_u(z)and ∆W_y(z)respectively. Then, the data x(t)are informative w.r.t. the model structure M if and only if, for all i =1,· · ·,ny, the following property holds for all

∆Wi:(z)=(∆Wy,i:(z),∆Wu,i:(z))∈∆W,i:







∆Wy,i:(z)−∆Wu,i:(z)K(z) ≡ 0 E¯£

||∆W_u,i:(z)r(t)||²¤

= 0 =⇒(∆W_y,i_:,∆W_u,i_:)≡(0,0) (22)

PROOF. See Appendix B.

Theorem 5 allows to simplify the data informativity ver- ification: it can be done channel-by-channel (or output- by-output). Hence, in the sequel we will consider an arbi- traryi and restrict attention to (22) for that particulari. In its actual form, (22) is function of the rational transfer functions matrix (∆W_y,i:(z),∆W_u,i:(z))∈∆W,i. We need to transform (22) into a polynomial matrix form for the development of the conditions on data informativity. Let us for this purpose first observe that the first equation of the left hand side of (22) can be rewritten as follows

³∆W_y,i:(z),−∆W_u,i:(z)

´



 In_y

K(z)



≡0 (23) By considering a right-factorization of K(z) and a left- factorization of (∆W_y,i_:(z),−∆W_u,i:(z)), we will prove that we can transform (23) into a polynomial matrix form. Moreover, we will see that this left-factorization of (∆W_y,i_:(z),−∆W_u,i:(z)) will allow us to transform the

second equation of the left hand side of (22) into a polynomial matrix form.

To simplify the presentation in the sequel, we will often use shorthand notations for each transfer of the form M(z), M(z,θ⁰) and M(z,θ⁰⁰) by dropping the argument and we will denote them byM,M⁰andM⁰⁰respectively.

5.2 Factorization of (22)into a polynomial matrix form We are going to consider a right-factorization for K(z) and a left-factorization for the rational block matrix (∆W_y,i_:(z),−∆W_u,i:(z)) which should be valid for all ∆Wi:(z)=(∆Wy,i:(z),∆Wu,i:(z))∈∆W,i. The one that we will choose is obtained by putting all entries of (∆Wy,i:(z),−∆Wu,i:(z)) on the same denominator. It is given in the next lemma.

Lemma 6 Consider the model structures defined in Section 4. For these model structures, the rational block- matrix (∆W_y,i_:(z),−∆W_u,i_:(z))can be left-factorized into (∆W_y,i_:(z),−∆W_u,i:(z))=Q⁻¹_i (z)(Υy,i(z),Υu,i(z))where the row polynomial vector Υy,i(z) of dimension n_y has all its entries equal to 0 except possibly the i -th one denoted Υy,i i(z). The row polynomial vector Υu,i(z) is of dimen- sion nu. The scalar polynomials Qi(z) and Υy,i i(z) and the row polynomial vectorΥu,i(z)are given by

• for FIR: Q_i=1,Υy,i i=0,Υu,i=B_i⁰⁰_:−B_i⁰_:.

• for ARX: Qi=1,Υy,i i=A⁰⁰_i −A⁰_i,Υu,i=B_i:⁰⁰−B_i:⁰.

• for ARMAX: Q_i =C_i⁰C⁰⁰_i, Υy,i i =C⁰_iA⁰⁰_i −C_i⁰⁰A⁰_i, Υu,i = C_i⁰B_i⁰⁰_:−C_i⁰⁰B_i⁰_:.

• for OE: Q_i = Qnu

k=1F_{i k}⁰ F_{i k}⁰⁰, Υy,i i = 0 and Υu,i = (Υu,i k)_k∈×

J^1,nuKwith

Υu,i k=(B_{i k}⁰⁰F_{i k}⁰ −B_{i k}⁰ F_{i k}⁰⁰)

nu

Y

l=1,l6=k

F_{i l}⁰F_{i l}⁰⁰

• for BJ: Q_i=C_i⁰C_i⁰⁰Qnu

k=1F_{i k}⁰ F_{i k}⁰⁰,Υy,i i=D⁰⁰_iC_i⁰Qnu

k=1F_{i k}⁰ F_{i k}⁰⁰− D⁰_iC⁰⁰_i Qnu

k=1F_{i k}⁰ F_{i k}⁰⁰ andΥu,i=(Υu,i k)_k∈

J^1,nuK with Υu,i k=D⁰⁰_iC_i⁰B⁰⁰_{i k}F_{i k}⁰

nu

Y l=1,l6=k

F_{i l}⁰ F_{i l}⁰⁰−D⁰_iC_i⁰⁰B_{i k}⁰ F_{i k}⁰⁰ nu

Y l=1,l6=k

F_{i l}⁰F_{i l}⁰⁰

PROOF. See Appendix C.

Based on the set∆W,i (i=1,· · ·,n_y) (see Theorem 5), let us define the set Yi made up of all polynomial matrices (Υy,i i(z),Υu,i(z)) obtained by considering the left- factorization of∆W_i_:(z)∈∆W,i (see Lemma 6).

For the controller K(z), we will consider the right- factorization consisting in putting all entries ofK(z) on the least common multiple J(z) of the denominator of the entries ofK(z) (J(z) is a polynomial). Therefore,K(z)

(7)

can be rewritten as

K(z)=N(z)V⁻¹(z) (24) where N(z) is a FIR matrix of dimension nu×ny and V(z)=diag(J(z),· · ·,J(z)

| {z }

ny times

).

Based on the factorization of K(z) in (24) and of

∆Wi:(z)∈∆W,i in Lemma 6, we can transform (22) into a polynomial matrix form.

Theorem 7 Consider Definition 2, Theorem 5 and one of the model structuresM defined in Section 4. Consider the right-factorization of K(z) in (24). For all i =1,· · ·,n_y, denote

Ξi(z)=



 Vi:(z)

N(z)



 (25)

where V_i_:(z)is the i -th row of V(z).

For each ∆W_i_:(z)=(∆W_y,i_:(z),∆W_u,i:(z))∈∆W,i, con- sider the left-factorization of (∆Wy,i:(z),−∆Wu,i:(z)) = Q⁻_i¹(z)(Υy,i(z),Υu,i(z))given in Lemma 6 and denoteΥy,i i

the i -th element of the row polynomial vectorΥy,i. Then, the data x(t)are informative w.r.t. the model structureM if and only if, for all i=1,· · ·,ny,







(Υy,i i(z),Υu,i(z))Ξi(z) ≡ 0 E¯£

||Υu,i(z)r(t)||²¤

= 0 =⇒(Υy,i i,Υu,i)≡(0,0) (26) for all(Υy,i i,Υu,i)∈Y_i.

PROOF. See Appendix D.

5.3 Main result for data informativity

In this section, we derive the main result of this paper. This result will allow us to check data informativity by verifying for each channel i=1,· · ·,n_y whether a given matrix is full row rank. As we will see in the sequel, this matrix will depend on the complexity of the model structure, on the controller coefficients and on the external excitation parametrization (amplitudes, phase-shifts, frequencies for multisine excitation and filter coefficients for filtered white noise excitation).

For this purpose, a first step is to give a formal expression of the polynomialΥy,i iand the row vector of polyno- mialsΥu,i=(Υu,i1,· · ·,Υu,i nu) inY_i. Using Lemma 6 and the notations introduced in Section 4, we can determine the scalarsηy,i,ηu,i k (i=1,· · ·,n_y,k=1,· · ·,n_u) such that allΥy,11and the entries ofΥu,i inYican be expressed as follows:

Υy,i i(z)=δ˜^T_y,iZ_y,i(z) (27) Υu,i k(z)=δ˜^T_{u,i k}Z_{u,i k}(z) (28)

where Zy,i(z) = (z⁻¹,· · ·,z^−η^y,i)^T, Z_{u,i k}(z) = (z^−ρ^{i k},· · ·, z^−η^{u,i k})^T and ˜δy,i, ˜δu,i k are vectors of coefficients. The values of ηy,i, ηu,i k (i =1,· · ·,n_y, k =1,· · ·,n_u) as a function of the model structure type and of the model structure complexity are given in Appendix E.

Using (27) and (28) and defining ˜δu,i = ( ˜δ^T_u,i1,· · ·, δ˜^T_{u,i n}_u)^T, the vectors of polynomials (Υy,i i,Υu,i) in the set Yi can be rewritten as follows:

(Υy,i i(z),Υu,i(z))=δ˜^T_i bdiag(Zy,i(z),Zu,i(z)) (29) where ˜δi =( ˜δ^T_y,i, ˜δ^T_u,i)^T and Z_u,i =bdiag(Z_u,i1,· · ·,Z_{u,i n}_u) (with bdiag(X₁,· · ·,X_n) a block diagonal matrix whose blocks are given by X_i (i =1,· · ·,n)). We will denote by ζi (resp. ζu,i) the dimension of ˜δi (resp. ˜δu,i). These di- mensions can be easily deduced based onηy,i,ηu,i k (i= 1,· · ·,ny,k=1,· · ·,nu). Let us also introduce the setD_i= { ˜δi | (29)∈Y_i}. Note that, except in the FIR case where D_i=R^ζⁱ,D_i is a subset ofR^ζⁱ.

In Sections 6 and 7, we will show that, using (27)- (29), we can determine matricesAi,Bi andCi such that the left hand side of (26) for a given (Υy,i i,Υu,i)∈Yi can be expressed as a function of the parameter vector ˜δi∈ D_i defining (Υy,i i,Υu,i) (see (29)). In particular, the first equation of the left hand side of (26) is equivalent to:











³δ˜^T_y,i δ˜^T_u,i´

| {z }

δ˜^T_i



 Ai

Bi



 =

³ 0 0

´

(BJ/ARX/ARMAX case)

δ˜^T_u_,iBi =0 (OE/FIR case)

(30)

The difference in the above equation follows from the fact thatΥy,i i=0 for OE/FIR model structures (see Lemma 6).

The second equation of the left hand side of (26) is equivalent to:

δ˜^T_u,iCi=0 (31) As will be shown in Sections 6 and 7, the dimension and the elements of the matrices Ai, Bi and Ci

will be function of the complexity of the model structure, the controller coefficients and the external excitation parametrization.

Theorem 8 Consider the data x(t) generated as in Sec- tion 3 with an external excitation r(t). Consider also Def- inition 2, Theorem 7 and one of the model structuresM defined in Section 4. Consider finally the notations intro- duced in Section 5.3 and, in particular, the equations(30) and (31) that are respectively equivalent to the first and second equations of the left hand side of (26). Then, the data x(t)are informative with respect toM if, for each i=1,· · ·,n_y, at least one of the matrices P_i^(a),P_i^(b) and P_i^(c)defined below are full row rank.

(8)

• (a)The matrixP_i^(a)is equal to



 Ai

Bi



for the BJ/ARX/ARMAX case and toBi for the OE/FIR case.

• (b)The matrixP_i^(b)is equal toCi.

• (c) The matrix P_i^(c) is equal to



 Ai 0 Bi Ci



 for the BJ/ARX/ARMAX case and to³

Bi Ci

´

for the OE/FIR case.

Moreover, when r =0, the data x(t) are informative with respect toM if, for each i=1,· · ·,n_y, the matrixP_i^(a) defined above is full row rank.

PROOF. We will prove the theorem in the BJ/ARX/ARMAX case. The proof for the OE/FIR case can be derived using the same argumentation. Note first that ˜δi=( ˜δy,i, ˜δu,i) in (30)-(31) is constrained to lie inDi. When ˜δi∈Di, hav- ing a full row rank P_i^(a) is a sufficient condition³ for δ˜i=0to be the unique solution of (30). Note also that δ˜i=0is equivalent to (Υy,i iΥu,i)=(0,0) (see (29)). Conse- quently, using the equivalence recalled in the statement of the theorem, whenP_i^(a)is full row rank, we have also that (Υy,i i(z),Υu,i(z))Ξi(z)≡0=⇒(Υy,i i,Υu,i)≡(0,0) for all (Υy,i i,Υu,i)∈Yi. It is clear by Theorem 7 that the latter implies thatx(t) is informative (in particular whenr=0).

Note now that (30) and (31) can be combined into δ˜^T_iP_i^(c)=0. IfP_i^(c)is full row rank, then ˜δ^T_i P_i^(c)=0im- plies ˜δi=0. Consequently, using the equivalence recalled in the statement of the theorem, when P_i^(c) is full row rank, we have also that (26) holds for all (Υy,i i,Υu,i)∈Y_i. It is clear by Theorem 7 that the latter implies that x(t) is informative.

Finally, ifP_i^(b)is full row rank, the equation (31) implies δ˜u,i =0. Consequently, using the equivalence recalled in the statement of the theorem, whenP_i^(b)is full row rank, we have also that: ¯E£

||Υu,i(z)r(t)||²¤

=0=⇒Υu,i ≡0for allΥu,i(z)∈Yi. SinceΥu,i≡0, the first equation of the left hand side of (26) reduces to Υy,i i(z)J(z)≡0 where J(z) is a given polynomial defined before in (24). The latter always implies that Υy,i i≡0. Consequently, by virtue of Theorem 7, we have also data informativity whenP_i^(b)is

full row rank.

Let us now show how we can rewrite the left hand side of (26) into (30)-(31).

3 IfD_i would be equal toR^ζⁱ, this would be a necessary and sufficient condition.

6 Rewriting of(Υy,i i,Υu,i)Ξi(z)≡0 into(30)

We will consider the BJ/ARX/ARMAX case since it is the more general. Using (29) and (25), we have that:

(Υy,i i(z),Υu,i(z))Ξi(z)=( ˜δ^T_y,i, ˜δ^T_u,i)L(z) (32) withL(z) a polynomial matrix of dimensionζi×n_y:

L(z)=





Z_y,i(z)V_i:(z) Z_u,i(z)N(z)





Each entryLm j(z) ofL(z) (m=1,· · ·,ζi, j =1,· · ·,ny) is a polynomial in z⁻¹ and can therefore be rewritten as λ^T_{m j}Z_L(z) with Z_L(z)=(z^−β^{mi n},z^−β^{mi n+1},· · ·,z^−β^max)^T whereλm j is a vector containing the coefficients⁴ of the polynomial L_{m j}(z) and z^−β^{mi n}, z^−β^max are the smallest and largest value of the monomialsz^−βamong all entries L_{m j} ofL. This yields to the following expression forL(z):

L(z)=





Z_y,i(z)V_i_:(z) Zu,i(z)N(z)



=



 Ai

Bi



(I_n_y⊗Z_L(z)) (33)

where⊗represents the Kronecker product and the matrix (A_i^T,B_i^T)^T is such that itsm-th row (m=1,· · ·,ζi) is given by (λ^T_m1,· · ·,λ^Tmny). Using now (32) and (33), we see that (Υy,i i(z),Υu,i(z))Ξi(z)≡0is equivalent to (30). The matrix (A_i^T,B_i^T)^T hasζi rows and a number of columns equal ton_y(βmax−βmi n+1). This number of columns therefore depends both on the complexity of the controller and on the complexity of the model structures (viaβmi n,βmax, ηu,i k,ηy,i). The more complex the model structure and the controller, the larger this number of columns is.

Example 9 Consider the following ARX model structure M with nu=2inputs and ny=1output:

G(z,θ)=

µ θ˜11,1z⁻¹ 1+η1,1z⁻¹,

θ˜12,1z⁻¹+θ˜12,2z⁻² 1+η1,1z⁻¹

¶

H(z,θ)= 1 1+η1,1z⁻¹

where θ = ( ˜θ11,1, ˜θ12,1, ˜θ12,2,η1,1)^T ∈ Dθ. In this case, we have the left-factorization of (∆Wy,1:(z),−∆Wu,1:(z)) in Lemma 6 of the form (∆W_y,1:(z),−∆W_u,1:(z)) = Q⁻¹₁ (z)(Υy,1(z),Υu,1(z))with Q₁(z)=1. From Appendix E, the polynomialΥy,1(z)=Υy,11(z)has an order of ηy,1=1 and is given by

4 These coefficients correspond to the ones inN(z) and J(z) of the right-factorization of the controllerK(z) in (24).