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| In [[control theory]], a '''state observer''' is a system that provides an estimate of the [[state space (controls)|internal state]] of a given real system, from measurements of the [[Input/output|input]] and [[output]] of the real system. It is typically computer-implemented, and provides the basis of many practical applications.
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| Knowing the system state is necessary to solve many [[control theory]] problems; for example, stabilizing a system using [[Full state feedback|state feedback]]. In most practical cases, the physical state of the system cannot be determined by direct observation. Instead, indirect effects of the internal state are observed by way of the system outputs. A simple example is that of vehicles in a tunnel: the rates and velocities at which vehicles enter and leave the tunnel can be observed directly, but the exact state inside the tunnel can only be estimated. If a system is [[Observability|observable]], it is possible to fully reconstruct the system state from its output measurements using the state observer.
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| == Typical observer model ==
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| The state of a linear, time-invariant physical discrete-time system is assumed to satisfy
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| : <math>x(k+1) = A x(k) + B u(k)</math>
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| : <math>y(k) = C x(k) + D u(k)</math>
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| where, at time <math>k</math>, <math>x(k)</math> is the plant's state; <math>u(k)</math> is its inputs; and <math>y(k)</math> is its outputs. These equations simply say that the plant's current outputs and its future state are both determined solely by its current states and the current inputs. (Although these equations are expressed in terms of [[discrete mathematics|discrete]] time steps, very similar equations hold for [[continuous function|continuous]] systems). If this system is [[Observability|observable]] then the output of the plant, <math>y(k)</math>, can be used to steer the state of the state observer.
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| The observer model of the physical system is then typically derived from the above equations. Additional terms may be included in order to ensure that, on receiving successive measured values of the plant's inputs and outputs, the model's state converges to that of the plant. In particular, the output of the observer may be subtracted from the output of the plant and then multiplied by a matrix <math>L</math>; this is then added to the equations for the state of the observer to produce a so-called ''[[David Luenberger|Luenberger]] observer'', defined by the equations below. Note that the variables of a state observer are commonly denoted by a "hat": <math>\hat{x}(k)</math> and <math>\hat{y}(k)</math> to distinguish them from the variables of the equations satisfied by the physical system.
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| <!-- insert plant with observer systems schematic -->
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| : <math>\hat{x}(k+1) = A \hat{x}(k) + L \left[y(k) - \hat{y}(k)\right] + B u(k)</math>
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| : <math>\hat{y}(k) = C \hat{x}(k) + D u(k)</math> | |
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| The observer is called asymptotically stable if the observer error <math>e(k) = \hat{x}(k) - x(k)</math> converges to zero when <math> k \rightarrow \infty </math>. For a Luenberger observer, the observer error satisfies <math> e(k+1) = (A - LC) e(k)</math>. The Luenberger observer for this discrete-time system is therefore asymptotically stable when the matrix <math> A - LC </math> has all the eigenvalues inside the unit circle.
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| For control purposes the output of the observer system is fed back to the input of both the observer and the plant through the gains matrix <math>K</math>.
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| : <math>u(k)= -K \hat{x}(k)</math>
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| The observer equations then become:
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| : <math>\hat{x}(k+1) = A \hat{x}(k) + L \left(y(k) - \hat{y}(k)\right) - B K \hat{x}(k)</math>
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| : <math>\hat{y}(k) = C \hat{x}(k) - D K \hat{x}(k)</math>
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| or, more simply,
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| : <math>\hat{x}(k+1) = \left(A - B K \right) \hat{x}(k) + L \left(y(k) - \hat{y}(k)\right)</math>
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| : <math>\hat{y}(k) = \left(C - D K\right) \hat{x}(k)</math><!--
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| Substituting in the equation for <math>y(k)</math> from the plant system
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| : <math>\hat{x}(k+1) = \left(A - B K - L C) \right) \hat{x}(k) + L \hat{y}(k)</math>
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| : <math>\hat{y}(k) = \left(C - D K\right) \hat{x}(k)</math>
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| -->
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| Due to the [[separation principle]] we know that we can choose <math>K</math> and <math>L</math> independently without harm to the overall stability of the systems. As a rule of thumb, the poles of the observer <math>A-LC</math> are usually chosen to converge 10 times faster than the poles of the system <math>A-BK</math>.
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| === Continuous-time case ===
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| The previous example was for an observer implemented in a discrete-time LTI system. However, the process is similar for the continuous-time case; the observer gains <math>L</math> are chosen to make the continuous-time error dynamics converge to zero asymptotically (i.e., when <math>A-LC</math> is a [[Hurwitz matrix]]).
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| For a continuous-time linear system
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| : <math>\dot{x} = A x+ B u, </math>
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| : <math>y = C x, </math> ,
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| where <math>x \in \mathbb{R}^n, u \in \mathbb{R}^m ,y \in \mathbb{R}^r</math>, the observer looks similar to discrete-time case described above:
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| : <math>\dot{\hat{x}} = A \hat{x}+ B u + L \left(y - C \hat{x}\right) </math>.
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| The observer error <math>e=\hat{x}-x</math> satisfies the equation
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| : <math> \dot{e} = (A - LC) e</math>.
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| The eigenvalues of the matrix <math>A-LC</math> can be made arbitrarily by appropriate choice of the observer gain <math>L</math> when the pair <math>[A,C]</math> is observable, i.e. [[observability]] condition holds. In particular, it can be made Hurwitz, so the observer error <math>e(t) \rightarrow 0</math> when <math>t \rightarrow \infty</math>.
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| === Peaking and other observer methods ===
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| When the observer gain <math>L</math> is high, the linear Luenberger observer converges to the system states very quickly. However, high observer gain leads to a peaking phenomenon in which initial estimator error can be prohibitively large (i.e., impractical or unsafe to use).<ref name="Khalil02">{{Citation
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| | last = Khalil
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| | first = H.K.
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| | authorlink = Hassan K. Khalil
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| | year = 2002
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| | edition = 3rd
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| | url = http://www.egr.msu.edu/~khalil/NonlinearSystems/
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| | isbn = 0-13-067389-7 | |
| | title = Nonlinear Systems
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| | publisher = [[Prentice Hall]]
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| | location = Upper Saddle River, NJ}}</ref> As a consequence, nonlinear high gain observer methods are available that converge quickly without the peaking phenomenon. For example, [[sliding mode control]] can be used to design an observer that brings one estimated state's error to zero in finite time even in the presence of measurement error; the other states have error that behaves similarly to the error in a Luenberger observer after peaking has subsided. Sliding mode observers also have attractive noise resilience properties that are similar to a [[Kalman filter]].<ref name="UtkinGS99">{{citation|title=Sliding Mode Control in Electromechanical Systems|last1=Utkin|first1=Vadim|last2=Guldner|first2=Jürgen|last3=Shi|first3=Jingxin|year=1999|publisher=Taylor & Francis, Inc.|location=Philadelphia, PA|isbn=0-7484-0116-4}}</ref><ref name="Drakunov83">{{citation|title=An adaptive quasioptimal filter with discontinuous parameters|journal=Automation and Remote Control|last1=Drakunov|first1=S.V.|year=1983|volume=44|issue=9|pages=1167–1175}}</ref>
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| == State observers for nonlinear systems ==
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| Sliding mode observers can be designed for the non-linear systems as well. For simplicity, first consider the no-input non-linear system:
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| : <math>\dot{x} = f(x)</math>
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| where <math>x \in \mathbb{R}^n</math>. Also assume that there is a measurable output <math>y \in \mathbb{R}</math> given by
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| : <math>y = h(x).</math>
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| There are several non-approximate approaches for designing an observer. The two observers given below also apply to the case when the system has an input. That is,
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| : <math>\dot{x} = f(x) + B(x) u, </math>
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| : <math>y = h(x), </math> .
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| === Linearizable error dynamics ===
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| One suggested by Krener and Isidori<ref name="KrenerIsidori83">{{citation|doi=10.1016/0167−6911(83)90037−3|title=Linearization by output injection and nonlinear observers|journal=System and Control Letters|last1=Krener|first1=A.J.|year=1983|volume=3|pages=47–52|last2=Isidori|first2=Alberto}}</ref> and Krener and Respondek<ref name="KrenerRespondek85">{{citation|doi=10.1137/0323016|title=Nonlinear observers with linearizable error dynamics|journal=SIAM Journal on Control and Optimization|last1=Krener|first1=A.J.|last2=Respondek|first2=W.|year=1985|volume=23|pages=197–216|issue=2}}</ref> can be applied in a situation when there exists a linearizing transformation (i.e., a [[diffeomorphism]], like the one used in [[feedback linearization]]) <math>z=\Phi(x)</math> such that in new variables the system equations read
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| : <math>\dot{z} = A z+ \phi(y), </math>
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| : <math>y = Cz. </math>
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| The Luenberger observer is then designed as
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| : <math>\dot{\hat{z}} = A \hat{z}+ \phi(y) - L \left(C \hat{z}-y \right) </math>.
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| The observer error for the transformed variable <math>e=\hat{z}-z</math> satisfies the same equation as in classical linear case.
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| : <math> \dot{e} = (A - LC) e</math>.
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| As shown by Gauthier, Hammouri, and Othman<ref name="GauthierHammouriOthman92">{{citation|title=A simple observer for nonlinear systems applications to bioreactors |journal=IEEE Transactions on AC|last1=Gauthier|first1=J.P.|last2=Hammouri|first2=H.|last3=Othman|first3=S.|year=1992|doi=10.1109/9.256352|volume=37|issue=6|pages=875}}</ref>
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| and Hammouri and Kinnaert,<ref name="HammouriKinnaert96">{{citation|title=A New Procedure for Time-Varying Linearization up to Output Injection|journal=System and Control Letters|last1=Hammouri|first1=H.|last2=Kinnaert|year=1996|doi=10.1016/0167−6911(96)00022−9|first2=M.|volume=28|issue=3|pages=151}}</ref> if there exists transformation <math>z=\Phi(x)</math> such that the system can be transformed into the form
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| : <math>\dot{z} = A(u(t)) z+ \phi(y,u(t) ), </math>
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| : <math>y = Cz, </math>
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| then the observer is designed as
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| : <math>\dot{\hat{z}} = A(u(t)) \hat{z}+ \phi(y,u(t) ) - L(t) \left(C \hat{z}-y \right) </math>,
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| where <math>L(t)</math> is a time-varying observer gain.
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| === Sliding mode observer ===
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| As discussed for the linear case above, the peaking phenomenon present in Luenberger observers justifies the use of a [[Sliding mode control#Sliding mode observer|sliding mode observer]]. The sliding mode observer uses non-linear high-gain feedback to drive estimated states to a [[hypersurface]] where there is no difference between the estimated output and the measured output. The non-linear gain used in the observer is typically implemented with a scaled switching function, like the [[Sign function|signum]] (i.e., sgn) of the estimated – measured output error. Hence, due to this high-gain feedback, the vector field of the observer has a crease in it so that observer trajectories ''slide along'' a curve where the estimated output matches the measured output exactly. So, if the system is [[observable]] from its output, the observer states will all be driven to the actual system states. Additionally, by using the sign of the error to drive the sliding mode observer, the observer trajectories become insensitive to many forms of noise. Hence, some sliding mode observers have attractive properties similar to the [[Kalman filter]] but with simpler implementation.<ref name="UtkinGS99" /><ref name="Drakunov83" />
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| As suggested by Drakunov,<ref name="Drakunov92">{{citation|title=Sliding-Mode Observers Based on Equivalent Control Method|journal=Proceedings of the 31st IEEE Conference on Decision and Control (CDC), |last=Drakunov|first=S.V.|year=1992|issue=Tucson, Arizona, December 16–18|pages=2368–2370|url=http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=371368&isnumber=8509|isbn=0−7803−0872−7|doi=10.1109/CDC.1992.371368}}</ref> a [[Sliding mode control#Sliding mode observer|sliding mode observer]] can also be designed for a class of non-linear systems. Such an observer can be written in terms of original variable estimate <math>\hat{x}</math> and has the form
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| : <math> \dot{\hat{x}} = \left [ \frac{\partial H(\hat{x})}{\partial x}
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| \right]^{-1} M(\hat{x}) \, \operatorname{sgn}( V(t) - H(\hat{x}) )</math>
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| where:
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| * The <math>\operatorname{sgn}(\mathord{\cdot})</math> vector extends the scalar [[sign function|signum function]] to <math>n</math> dimensions. That is,
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| :: <math>\operatorname{sgn}(z) = \begin{bmatrix}
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| \operatorname{sgn}(z_1)\\
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| \operatorname{sgn}(z_2)\\
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| \vdots\\
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| \operatorname{sgn}(z_i)\\
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| \vdots\\
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| \operatorname{sgn}(z_n)
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| \end{bmatrix}</math>
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| : for the vector <math>z \in \mathbb{R}^n</math>.
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| * The vector <math>H(x)</math> has components that are the output function <math>h(x)</math> and its repeated Lie derivatives. In particular,
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| :: <math>H(x) \triangleq
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| \begin{bmatrix}
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| h_1(x)\\
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| h_2(x)\\
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| h_3(x)\\
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| \vdots\\
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| h_n(x)
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| \end{bmatrix}
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| \triangleq
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| \begin{bmatrix}
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| h(x)\\
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| L_{f}h(x)\\
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| L_{f}^2 h(x)\\
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| \vdots\\
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| L_{f}^{n-1}h(x)
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| \end{bmatrix}</math>
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| : where <math>L^i_f h</math> is the ''i''<sup>th</sup> [[Lie derivative]] of output function <math>h</math> along the vector field <math>f</math> (i.e., along <math>x</math> trajectories of the non-linear system). In the special case where the system has no input or has a [[feedback linearization|relative degree]] of ''n'', <math>H(x(t))</math> is a collection of the output <math>y(t)=h(x(t))</math> and its <math>n-1</math> derivatives. Because the inverse of the [[linearization|Jacobian linearization]] of <math>H(x)</math> must exist for this observer to be well defined, the transformation <math>H(x)</math> is guaranteed to be a local [[diffeomorphism]].
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| * The [[diagonal matrix]] <math>M(\hat{x})</math> of gains is such that
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| :: <math>M(\hat{x}) \triangleq | |
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| \operatorname{diag}( m_1(\hat{x}), m_2(\hat{x}), \ldots, m_n(\hat{x}) )
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| =
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| \begin{bmatrix}
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| m_1(\hat{x}) & & & & & \\
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| & m_2(\hat{x}) & & & & \\
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| & & \ddots & & & \\
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| & & & m_i(\hat{x}) & &\\
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| & & & & \ddots &\\
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| & & & & & m_n(\hat{x})
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| \end{bmatrix}</math>
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| : where, for each <math>i \in \{1,2,\dots,n\}</math>, element <math>m_i(\hat{x}) > 0</math> and suitably large to ensure reachability of the sliding mode.
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| * The observer vector <math>V(t)</math> is such that
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| :: <math>V(t)
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| \triangleq
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| \begin{bmatrix}v_{1}(t)\\
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| v_2(t)\\
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| v_3(t)\\
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| \vdots\\
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| v_i(t)\\
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| \vdots\\
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| v_{n}(t)
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| \end{bmatrix}
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| \triangleq
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| \begin{bmatrix}
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| y(t)\\
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| \{ m_1(\hat{x}) \operatorname{sgn}( v_1(t) - h_1(\hat{x}(t)) ) \}_{\text{eq}}\\
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| \{ m_2(\hat{x}) \operatorname{sgn}( v_2(t) - h_2(\hat{x}(t)) ) \}_{\text{eq}}\\
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| \vdots\\
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| \{ m_{i-1}(\hat{x}) \operatorname{sgn}( v_{i-1}(t) - h_{i-1}(\hat{x}(t)) ) \}_{\text{eq}}\\
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| \vdots\\
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| \{ m_{n-1}(\hat{x}) \operatorname{sgn}( v_{n-1}(t) - h_{n-1}(\hat{x}(t)) ) \}_{\text{eq}}
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| \end{bmatrix}
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| </math>
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| : where <math>\operatorname{sgn}(\mathord{\cdot})</math> here is the normal [[signum]] function defined for scalars, and <math>\{ \ldots \}_{\text{eq}}</math> denotes an "equivalent value operator" of a discontinuous function in sliding mode.
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| The idea can be briefly explained as follows. According to the theory of sliding modes, in order to describe the system behavior, once sliding mode starts, the function <math>\operatorname{sgn}( v_{i}(t)\!-\! h_{i}(\hat{x}(t)) )</math> should be replaced by equivalent values (see ''equivalent control'' in the theory of [[sliding mode control|sliding mode]]s). In practice, it switches (chatters) with high frequency with slow component being equal to the equivalent value. Applying appropriate lowpass filter to get rid of the high frequency component on can obtain the value of the equivalent control, which contains more information about the state of the estimated system. The observer described above uses this method several times to obtain the state of the nonlinear system ideally in finite time.
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| The modified observation error can be written in the transformed states <math>e=H(x)-H(\hat{x})</math>. In particular,
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| : <math>\begin{align} | |
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| \dot{e}
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| &=
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| \frac{\operatorname{d}}{\operatorname{d}t} H(x)
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| -
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| \frac{\operatorname{d}}{\operatorname{d}t} H(\hat{x})\\
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| &=
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| \frac{\operatorname{d}}{\operatorname{d}t} H(x)
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| -
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| M(\hat{x}) \, \operatorname{sgn}( V(t) - H(\hat{x}(t)) ),
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| \end{align}</math>
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| and so
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| : <math>
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| \begin{align}
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| \begin{bmatrix}
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| \dot{e}_1\\
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| \dot{e}_2\\
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| \vdots\\
| |
| \dot{e}_i\\
| |
| \vdots\\
| |
| \dot{e}_{n-1}\\
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| \dot{e}_n
| |
| \end{bmatrix}
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| &=
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| \mathord{\overbrace{
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| \begin{bmatrix}
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| \dot{h}_1(x)\\
| |
| \dot{h}_2(x)\\
| |
| \vdots\\
| |
| \dot{h}_i(x)\\
| |
| \vdots\\
| |
| \dot{h}_{n-1}(x)\\
| |
| \dot{h}_n(x)
| |
| \end{bmatrix}
| |
| }^{\tfrac{\operatorname{d}}{\operatorname{d}t} H(x)}}
| |
| -
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| \mathord{\overbrace{
| |
| M(\hat{x}) \, \operatorname{sgn}( V(t) - H(\hat{x}(t)) )
| |
| }^{\tfrac{\operatorname{d}}{\operatorname{d}t} H(\hat{x})}}
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| =
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| \begin{bmatrix}
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| h_2(x)\\
| |
| h_3(x)\\
| |
| \vdots\\
| |
| h_{i+1}(x)\\
| |
| \vdots\\
| |
| h_n(x)\\
| |
| L_f^n h(x)
| |
| \end{bmatrix}
| |
| -
| |
| \begin{bmatrix}
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| m_1 \operatorname{sgn}( v_1(t) - h_1(\hat{x}(t)) )\\
| |
| m_2 \operatorname{sgn}( v_2(t) - h_2(\hat{x}(t)) )\\
| |
| \vdots\\
| |
| m_i \operatorname{sgn}( v_i(t) - h_i(\hat{x}(t)) )\\
| |
| \vdots\\
| |
| m_{n-1} \operatorname{sgn}( v_{n-1}(t) - h_{n-1}(\hat{x}(t)) )\\
| |
| m_n \operatorname{sgn}( v_n(t) - h_n(\hat{x}(t)) )
| |
| \end{bmatrix}\\
| |
| &=
| |
| \begin{bmatrix}
| |
| h_2(x) - m_1(\hat{x}) \operatorname{sgn}( \mathord{\overbrace{ \mathord{\overbrace{v_1(t)}^{v_1(t) = y(t) = h_1(x)}} - h_1(\hat{x}(t)) }^{e_1}} )\\
| |
| h_3(x) - m_2(\hat{x}) \operatorname{sgn}( v_2(t) - h_2(\hat{x}(t)) )\\
| |
| \vdots\\
| |
| h_{i+1}(x) - m_i(\hat{x}) \operatorname{sgn}( v_i(t) - h_i(\hat{x}(t)) )\\
| |
| \vdots\\
| |
| h_n(x) - m_{n-1}(\hat{x}) \operatorname{sgn}( v_{n-1}(t) - h_{n-1}(\hat{x}(t)) )\\
| |
| L_f^n h(x) - m_n(\hat{x}) \operatorname{sgn}( v_n(t) - h_n(\hat{x}(t)) )
| |
| \end{bmatrix}.
| |
| \end{align}
| |
| </math>
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| So:
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| # As long as <math>m_1(\hat{x}) \geq |h_2(x(t))|</math>, the first row of the error dynamics, <math>\dot{e}_1 = h_2(\hat{x}) - m_1(\hat{x}) \operatorname{sgn}( e_1 )</math>, will meet sufficient conditions to enter the <math>e_1 = 0</math> sliding mode in finite time.
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| # Along the <math>e_1 = 0</math> surface, the corresponding <math>v_2(t) = \{m_1(\hat{x}) \operatorname{sgn}( e_1 )\}_{\text{eq}}</math> equivalent control will be equal to <math>h_2(x)</math>, and so <math>v_2(t) - h_2(\hat{x}) = h_2(x) - h_2(\hat{x}) = e_2</math>. Hence, so long as <math>m_2(\hat{x}) \geq |h_3(x(t))|</math>, the second row of the error dynamics, <math>\dot{e}_2 = h_3(\hat{x}) - m_2(\hat{x}) \operatorname{sgn}( e_2 )</math>, will enter the <math>e_2 = 0</math> sliding mode in finite time.
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| # Along the <math>e_i = 0</math> surface, the corresponding <math>v_{i+1}(t) = \{\ldots\}_{\text{eq}}</math> equivalent control will be equal to <math>h_{i+1}(x)</math>. Hence, so long as <math>m_{i+1}(\hat{x}) \geq |h_{i+2}(x(t))|</math>, the <math>(i+1)</math><sup>th</sup> row of the error dynamics, <math>\dot{e}_{i+1} = h_{i+2}(\hat{x}) - m_{i+1}(\hat{x}) \operatorname{sgn}( e_{i+1} )</math>, will enter the <math>e_{i+1} = 0</math> sliding mode in finite time.
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| So, for sufficiently large <math>m_i</math> gains, all observer estimated states reach the actual states in finite time. In fact, increasing <math>m_i</math> allows for convergence in any desired finite time so long as each <math>|h_i(x(0))|</math> function can be bounded with certainty. Hence, the requirement that the map <math>H:\mathbb{R}^n \rightarrow \mathbb{R}^n </math> is a [[diffeomorphism]] (i.e., that its [[linearization|Jacobian linearization]] is invertible) asserts that convergence of the estimated output implies convergence of the estimated state. That is, the requirement is an observability condition.
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| In the case of the sliding mode observer for the system with the input, additional conditions are needed for the observation error to be independent of the input. For example, that
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| : <math> \frac{\partial H(x)}{\partial x} B(x)</math>
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| does not depend on time. The observer is then
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| : <math>
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| \dot{\hat{x}} = \left[ \frac{\partial H(\hat{x})}{\partial x}
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| \right]^{-1} M(\hat{x}) \operatorname{sgn}(V(t) - H(\hat{x}))+B(\hat{x})u.
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| </math>
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| == Bounding observers ==
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| The Bounding <ref>http://www.nt.ntnu.no/users/skoge/prost/proceedings/ecc03/pdfs/437.pdf</ref> or Interval observers <ref>http://www.nt.ntnu.no/users/skoge/prost/proceedings/cdc-2008/data/papers/1446.pdf</ref> constitute a class of observers that provide two estimations
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| of the state simultaneously: one of the estimation provides an upper bound on the real value of the state,
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| whereas the second one provides a lower bound. The real value of the state is then known to be always within these two
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| estimations.
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| These bounds are very important in practical applications,<ref>http://www.iaeng.org/publication/WCE2010/WCE2010_pp656-661.pdf</ref><ref>http://www.sciencedirect.com/science/article/pii/S0959152499000748</ref> as they make possible to know at each time the precision of the estimation.
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| Mathematically, two Luenberger observers can be used, if <math> L </math> is properly selected, using, for example, [[positive systems]] properties:<ref>Ait Rami, M., Tadeo, F., Helmke, U. (2011),
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| "Positive observers for linear positive systems, and their implications",
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| International Journal of Control 84
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| </ref> one for the upper bound <math> \hat{x}_U(k) </math> (that ensures that <math> e(k) = \hat{x}_U(k) - x(k) </math> converges to zero from above when <math> k \rightarrow \infty </math>, in the absence of noise and [[uncertainty]]), and a lower bound <math> \hat{x}_L(k) </math> (that ensures that <math> e(k) = \hat{x}_L(k) - x(k) </math> converges to zero from below). That is, always <math> \hat{x}_U(k) \ge x(k) \ge \hat{x}_L(k) </math>
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| == See also ==
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| * [[Kalman filter]]
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| * [[Extended Kalman filter]]
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| * [[Positive systems]]
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| == References ==
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| ; In-line references
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| {{reflist}}
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| ; General references
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| {{refbegin}}
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| * {{Citation
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| | last = Sontag
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| | first = Eduardo
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| | authorlink = Eduardo D. Sontag
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| | year = 1998
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| | title = Mathematical Control Theory: Deterministic Finite Dimensional Systems. Second Edition
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| | publisher = Springer
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| | isbn = 0-387-98489-5
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| }}
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| {{refend}}
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| [[Category:Control theory]]
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| [[Category:Signal estimation]]
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