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Title: Observability  
Author: World Heritage Encyclopedia
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Subject: Control theory, State observer, Controllability, Cross Gramian, Kalman decomposition
Collection: Control Theory
Publisher: World Heritage Encyclopedia


In control theory, observability is a measure for how well internal states of a system can be inferred by knowledge of its external outputs. The observability and controllability of a system are mathematical duals. The concept of observability was introduced by American-Hungarian engineer Rudolf E. Kalman for linear dynamic systems.[1][2]


  • Definition 1
  • Continuous time-varying system 2
  • Nonlinear case 3
  • Static systems and general topological spaces 4
  • See also 5
  • References 6
  • External links 7


Formally, a system is said to be observable if, for any possible sequence of state and control vectors, the current state can be determined in finite time using only the outputs (this definition is slanted towards the state space representation). Less formally, this means that from the system's outputs it is possible to determine the behaviour of the entire system. If a system is not observable, this means the current values of some of its states cannot be determined through output sensors. This implies that their value is unknown to the controller (although they can be estimated through various means).

For time-invariant linear systems in the state space representation, there is a convenient test to check if a system is observable. Consider a SISO system with n states (see state space for details about MIMO systems), if the row rank of the following observability matrix

\mathcal{O}=\begin{bmatrix} C \\ CA \\ CA^2 \\ \vdots \\ CA^{n-1} \end{bmatrix}

is equal to n, then the system is observable. The rationale for this test is that if n rows are linearly independent, then each of the n states is viewable through linear combinations of the output variables y(k).

A module designed to estimate the state of a system from measurements of the outputs is called a state observer or simply an observer for that system.

Observability index

The Observability index v of a linear time-invariant discrete system is the smallest natural number for which is satisfied that \text{rank}{(O_v)} = \text{rank}{(O_{v+1})}, where

\mathcal{O}_v=\begin{bmatrix} C \\ CA \\ CA^2 \\ \vdots \\ CA^{v-1} \end{bmatrix}.

A slightly weaker notion than observability is detectability. A system is detectable if all the unstable modes are observable.[3]

Continuous time-varying system

Consider the continuous linear time-variant system

\dot{\mathbf{x}}(t) = A(t) \mathbf{x}(t) + B(t) \mathbf{u}(t) \,
\mathbf{y}(t) = C(t) \mathbf{x}(t). \,

Suppose that the matrices A,B, \text{ and } C are given as well as inputs and outputs u \text{ and } y for all t \in [t_0,t_1] then it is possible to determine x(t_0) to within an additive constant vector which lies in the null space of M(t_0,t_1) defined by

M(t_0,t_1) = \int_{t_0}^{t_1} \phi(t,t_0)^{T}C(t)^{T}C(t)\phi(t,t_0) dt

where \phi is the state-transition matrix.

It is possible to determine a unique x(t_0) if M(t_0,t_1) is nonsingular. In fact, it is not possible to distinguish the initial state for x_1 from that of x_2 if x_1 - x_2 is in the null space of M(t_0,t_1).

Note that the matrix M defined as above has the following properties:

\frac{d}{dt}M(t,t_1) = -A(t)^{T}M(t,t_1)-M(t,t_1)A(t)-C(t)^{T}C(t), \; M(t_1,t_1) = 0
  • M(t_0,t_1) satisfies the equation
M(t_0,t_1) = M(t_0,t) + \phi(t,t_0)^T M(t,t_1)\phi(t,t_0)[4]

Nonlinear case

Given the system \dot{x} = f(x) + \sum_{j=1}^mg_j(x)u_j , y_i = h_i(x), i \in p. Where x \in \mathbb{R}^n the state vector, u \in \mathbb{R}^m the input vector and y \in \mathbb{R}^p the output vector. f,g,h are to be smooth vectorfields.

Now define the observation space \mathcal{O}_s to be the space containing all repeated Lie derivatives. Now the system is observable in x_0 if and only if \textrm{dim}(d\mathcal{O}_s(x_0)) = n.

Note: d\mathcal{O}_s(x_0) = \mathrm{span}(dh_1(x_0), \ldots , dh_p(x_0), dL_{v_i}L_{v_{i-1}}, \ldots , L_{v_1}h_j(x_0)),\ j\in p, k=1,2,\ldots.[5]

Early criteria for observability in nonlinear dynamic systems were discovered by Griffith and Kumar,[6] Kou, Elliot and Tarn,[7] and Singh.[8]

Static systems and general topological spaces

Observability may also be characterized for steady state systems (systems typically defined in terms of algebraic equations and inequalities), or more generally, for sets in \mathbb{R}^n ,.[9][10] Just as observability criteria are used to predict the behavior of Kalman filters or other observers in the dynamic system case, observability criteria for sets in \mathbb{R}^n are used to predict the behavior of data reconciliation and other static estimators. In the nonlinear case, observability can be characterized for individual variables, and also for local estimator behavior rather than just global behavior.

See also


  1. ^ Kalman R. E., "On the General Theory of Control Systems", Proc. 1st Int. Cong. of IFAC, Moscow 1960 1 481, Butterworth, London 1961.
  2. ^ Kalman R. E., "Mathematical Description of Linear Dynamical Systems", SIAM J. Contr. 1963 1 152
  3. ^
  4. ^
  5. ^ Lecture notes for Nonlinear Systems Theory by prof. dr. D.Jeltsema, prof dr. J.M.A.Scherpen and prof dr. A.J.van der Schaft.
  6. ^ Griffith E. W. and Kumar K. S. P., "On the Observability of Nonlinear Systems I, J. Math. Anal. Appl. 1971 35 135
  7. ^ Kou S. R., Elliott D. L. and Tarn T. J., Inf. Contr. 1973 22 89
  8. ^ Singh S.N., "Observability in Non-linear Systems with immeasurable Inputs, Int. J. Syst. Sci., 6 723, 1975
  9. ^ Stanley G.M. and Mah, R.S.H., "Observability and Redundancy in Process Data Estimation, Chem. Engng. Sci. 36, 259 (1981)
  10. ^ Stanley G.M., and Mah R.S.H., "Observability and Redundancy Classification in Process Networks", Chem. Engng. Sci. 36, 1941 (1981)

External links

  • Observability at
  • MATLAB function for checking observability of a system
  • Mathematica function for checking observability of a system
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