Introduction to the Hankel -based model order reduction for linear systems D.Vasilyev Massachusetts Institute of Technology, 2004

State-space description MIMO LTI CT dynamical system: Here we assume a system to be stable, i.e matrix A is Hurwitz.

Model order reduction problem G(s) (original) G r (s) (reduced) + - Problem: find a dynamical system G r (s) of a smaller degree q (McMillan degree – size of a minimal realization), such that the error of approximation e is “small” over all inputs! u(t) y r (t) y(t) e(t) Question: small in what sense??

Signals/system norms t u L 2 – space of square-summable functions with 2- norm, or energy: LTI system, as a linear operator on this space, has an induced 2-norm (maximum energy amplification, or L 2 gain), which equals H-infinity-norm of a system’s transfer function: Now we know how to state our problem!

Model order reduction problem G(s) (original) G r (s) (reduced) + - Problem formulation: find G r (s) of a smaller degree that minimizes u(t) y r (t) y(t) e(t) Unfortunately, we cannot solve this problem  Instead, we use another system norm.

Hankel operator LTI SYSTEM X (state) t u t y Hankel operator Past input (u(t>0)=0) Future output Hankel operator: - maps past inputs to future system outputs - ignores any system response before time 0. - Has finite rank (connection only by the state at t=0) - As an operator on L 2 it has an induced norm (energy amplification)!

Hankel optimal MOR G(s) (original) G r (s) (reduced) + - u(t) y r (t) y(t) e(t) Problem formulation: find G r (s) of a smaller degree that minimizes (Hankel norm of an error) This problem has been solved and explicit algorithm is given for state-space LTI systems in Glover[84].

Controllability/observability LTI SYSTEM X (state) t u t y Hankel operator Past input Future output P (controllability) Which states are easier to reach? Q (observability) Which states produces more output? Since we are interested in Hankel norm, we need to know how energy is transferred between input, state and output

Observability X (0) t y Future output How much energy in the output we shall observe if the system is released from some state x(0) ? Observability Gramian Satisfies Lyapunov equation: A T Q +QA = -C T C Q is SPD iff system is observable

Controllability X (0) Past input What is the minimal energy of input signal needed to drive system to the state x(0)? Controllability Gramian Satisfies Lyapunov equation: t u State AP + PA T = -BB T P is SPD iff system is controllable

Side note about Lyapunov equations Assume some LTI CT system: AP + PA ′ = -R, R - hermitian This equation has a unique solution P=P ′ if and only if: Moreover, if R is SPD and A is Hurwitz, then P is SPD. Lyapunov function: V(x)=x’Px >0 (reformulation of stability criterion)

Hankel singular values X (state) t u t y Hankel operator - This operator has finite rank (equal to degree of G). Hankel singular values are square roots of an eigenvalues of the product PQ - If we approximate this operator by different one with lower rank, we cannot do better in the Hankel norm than the first removed HSV: Amazingly, this bound is tight (Adamjan et al., 71)

Truncated balanced reduction Gramians are transformed with the change of basis as a quadratic forms: x → Tx, P → TPT T, Q → T -T QT -1 We can find a basis, in which both gramians are equal and diagonal. Such transformation is called balancing transformation. P = R T RUΣ 2 U T = RQR T T = R T U Σ -1/2 P = Q = diag(σ 1, …, σ n ), σ 1 ≥ … ≥ σ n

Truncated balanced reduction- cont’d In the balanced realization we can perform truncation of the least observable and controllable modes! Truncated system (A 11, B 1, C 1, D) will be stable (if σ q ≠ σ q+1, otherwise stable for almost all T ) and have the following H-infinity error bound: “twice sum of a tail” rule

Truncated balanced reduction vs. Hankel optimal reduction For the balanced truncation procedure we have the following error bounds: TBR is not optimal in terms of the Hankel norm! For the Hankel optimal MOR the following bounds hold:

References: Keith Glover, “All optimal Hankel-norm approximations of linear multivariable systems and their L-infinity error bounds”