TheoremBase

Superposition Decomposition of the Controlled State and Observations

Statement

Consider a linear-Gaussian state-observation model on [0,T][0,T], a control dimension k≥1k\ge1, a control matrix assignment BB, an admissible control α\alpha, and the controlled state and observations XαX^{\alpha}, uαu^{\alpha}, with all notation and fixed versions as in those items. Let Φ\Phi and Ψ=Φ−1\Psi=\Phi^{-1} be the fundamental solution of AA on [0,T][0,T] and its inverse from Variation of Constants for Linear Systems. Define, with fixed versions of the mean-square Riemann integrals, the correction processes

ct:=Φ(t) ht,htj:=∫0t(Ψ(r)B(r)αr)j dr(1≤j≤l),γtj:=∫0t(E~(r)cr)j dr(1≤j≤l~),c_t:=\Phi(t)\,h_t,\qquad h^{j}_t:=\int_0^t\bigl(\Psi(r)B(r)\alpha_r\bigr)^{j}\,dr\quad(1\le j\le l),\qquad \gamma^{j}_t:=\int_0^t\bigl(\tilde E(r)c_r\bigr)^{j}\,dr\quad(1\le j\le\tilde l),

for t∈[0,T]t\in[0,T], with the matrix-vector product formed componentwise; the integrals defining hh exist by Existence and Uniqueness of the Mean-Square Riemann Integral for Mean-Square Continuous Families and claims 1-2 of Basic Properties of the Mean-Square Riemann Integral, and those defining γ\gamma exist by the same results together with the mean-square continuity of cc established in claim 1 below. Then:

1. (Regularity and dynamics of the correction) Each component family (cti)t∈[0,T](c^{i}_t)_{t\in[0,T]} is mean-square continuous, c0i=0c^{i}_0=0 almost surely, and, componentwise and almost surely,

ct=∫0t(A(r)cr+B(r)αr) dr(0≤t≤T);c_t=\int_0^t\bigl(A(r)c_r+B(r)\alpha_r\bigr)\,dr\qquad(0\le t\le T);

that is, cc is a mean-square solution of the linear stochastic differential equation with coefficient AA, forcing (B(r)αr)r(B(r)\alpha_r)_r, zero noise matrix, and zero initial value.

2. (Superposition) For every t∈[0,T]t\in[0,T] and every i∈{1,…,l}i\in\{1,\dots,l\}: Xtα,i=Xti+ctiX^{\alpha,i}_t=X^{i}_t+c^{i}_t almost surely.

3. (Adaptedness of the corrections) For every tt, each ctic^{i}_t and each γtj\gamma^{j}_t is almost surely equal to a Gt\mathcal{G}_t-measurable square-integrable random variable.

4. (Observation shift) Each component family (γtj)t∈[0,T](\gamma^{j}_t)_{t\in[0,T]} is mean-square continuous, and for every tt and jj: utα,j=utj+γtju^{\alpha,j}_t=u^{j}_t+\gamma^{j}_t almost surely.

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