Controllability


Controllability is an important property of a control system, and the controllability property plays a crucial role in many control problems, such as stabilization of unstable systems by feedback, or optimal control.
Controllability and observability are dual aspects of the same problem.
Roughly, the concept of controllability denotes the ability to move a system around in its entire configuration space using only certain admissible manipulations. The exact definition varies slightly within the framework or the type of models applied.
The following are examples of variations of controllability notions which have been introduced in the systems and control literature:
The state of a deterministic system, which is the set of values of all the system's state variables, completely describes the system at any given time. In particular, no information on the past of a system is needed to help in predicting the future, if the states at the present time are known and all current and future values of the control variables are known.
Complete state controllability describes the ability of an external input to move the internal state of a system from any initial state to any other final state in a finite time interval.
Controllability does not mean that a reached state can be maintained, merely that any state can be reached.

Continuous linear systems

Consider the continuous linear system
There exists a control from state at time to state at time if and only if is in the column space of
where is the state-transition matrix, and is the Controllability Gramian.
In fact, if is a solution to then a control given by would make the desired transfer.
Note that the matrix defined as above has the following properties:
The Controllability Gramian involves the integration of the state-transition matrix of the system. A simpler condition for controllability is a rank condition analogous to the Kalman rank condition for time-invariant systems.
Consider a continuous-time linear system smoothly varying in an interval of :
The state-transition matrix is also smooth. Introduce the n x m matrix-valued function and define
Consider the matrix of matrix-valued functions obtained by listing all the columns of the, :
If there exists a and a nonnegative integer k such that, then is controllable.
If is also analytically varying in an interval, then is controllable on every nontrivial subinterval of if and only if there exists a and a nonnegative integer k such that .
The above methods can still be complex to check, since it involves the computation of the state-transition matrix. Another equivalent condition is defined as follow. Let, and for each, define
In this case, each is obtained directly from the data The system is controllable if there exists a and a nonnegative integer such that .

Example

Consider a system varying analytically in and matrices
,
Then
and since this matrix has rank 3, the system is controllable on every nontrivial interval of .

Continuous linear time-invariant (LTI) systems

Consider the continuous linear time-invariant system
where
The controllability matrix is given by
The system is controllable if the controllability matrix has full row rank.

Discrete linear time-invariant (LTI) systems

For a discrete-time linear state-space system the state equation is
where is an matrix and is a matrix. The test for controllability is that the matrix
has full row rank. That is, if the system is controllable, will have columns that are linearly independent; if columns of are linearly independent, each of the states is reachable by giving the system proper inputs through the variable.

Derivation

Given the state at an initial time, arbitrarily denoted as k=0, the state equation gives then and so on with repeated back-substitutions of the state variable, eventually yielding
or equivalently
Imposing any desired value of the state vector on the left side, this can always be solved for the stacked vector of control vectors if and only if the matrix of matrices at the beginning of the right side has full row rank.

Example

For example, consider the case when and . Thus, and are vectors. If has rank 2, and so and are linearly independent and span the entire plane. If the rank is 1, then and are collinear and do not span the plane.
Assume that the initial state is zero.
At time :
At time :
At time all of the reachable states are on the line formed by the vector.
At time all of the reachable states are linear combinations of and.
If the system is controllable then these two vectors can span the entire plane and can be done so for time.
The assumption made that the initial state is zero is merely for convenience.
Clearly if all states can be reached from the origin then any state can be reached from another state.
This example holds for all positive, but the case of is easier to visualize.

Analogy for example of ''n'' = 2

Consider an analogy to the previous example system.
You are sitting in your car on an infinite, flat plane and facing north.
The goal is to reach any point in the plane by driving a distance in a straight line, come to a full stop, turn, and driving another distance, again, in a straight line.
If your car has no steering then you can only drive straight, which means you can only drive on a line.
The lack of steering case would be analogous to when the rank of is 1.
Now, if your car did have steering then you could easily drive to any point in the plane and this would be the analogous case to when the rank of is 2.
If you change this example to then the analogy would be flying in space to reach any position in 3D space.
You are allowed to:
Although the 3-dimensional case is harder to visualize, the concept of controllability is still analogous.

Nonlinear systems

Nonlinear systems in the control-affine form
are locally accessible about if the accessibility distribution spans space, when equals the rank of and R is given by:
Here, is the repeated Lie bracket operation defined by
The controllability matrix for linear systems in the previous section can in fact be derived from this equation.

Null Controllability

If a discrete control system is null-controllable, it means that there exists a controllable so that for some initial state. In other words, it is equivalent to the condition that there exists a matrix such that is nilpotent.
This can be easily shown by controllable-uncontrollable decomposition.

Output controllability

Output controllability is the related notion for the output of the system ; the output controllability describes the ability of an external input to move the output from any initial condition to any final condition in a finite time interval. It is not necessary that there is any relationship between state controllability and output controllability. In particular:
For a linear continuous-time system, like the example above, described by matrices,,, and, the output controllability matrix
has full row rank if and only if the system is output controllable.

Controllability under input constraints

In systems with limited control authority, it is often no longer possible to move any initial state to any final state inside the controllable subspace. This phenomenon is caused by constraints on the input that could be inherent to the system or imposed on the system for other reasons. The controllability of systems with input and state constraints is studied in the context of reachability and viability theory.

Controllability in the behavioral framework

In the so-called behavioral system theoretic approach due to Willems, models considered do not directly define an input-output structure. In this framework systems are described by admissible trajectories of a collection of variables, some of which might be interpreted as inputs or outputs.
A system is then defined to be controllable in this setting, if any past part of a behavior can be concatenated with any future trajectory of the behavior in such a way that the concatenation is contained in the behavior, i.e. is part of the admissible system behavior.

Stabilizability

A slightly weaker notion than controllability is that of stabilizability. A system is said to be stabilizable when all uncontrollable state variables can be made to have stable dynamics. Thus, even though some of the state variables cannot be controlled all the state variables will still remain bounded during the system's behavior.

Reachable set

Let T ∈ Т and x ∈ X. The reachable set from x in time T is defined as :
, where xz denotes that there exists a state transition from x to z in time T.
For autonomous systems the reachable set is given by :
where R is the controllability matrix.
In terms of the reachable set, the system is controllable if and only if.
Proof
We have the following equalities:
Considering that the system is controllable, the columns of R should be linearly independent. So:
A related set to the reachable set is the controllable set, defined by:
The relation between reachability and controllability is presented by Sontag :
An n-dimensional discrete linear system is controllable if and only if:
A continuous-time linear system is controllable if and only if:
if and only if for all e>0.
Example
Let the system be an n dimensional discrete-time-invariant system from the formula:
It follows that the future state is in ⇔ it is in the image of the linear map:
which maps,
When and we identify R with a n by nm matrix whose columns are the columns of in that order. If the system is controllable the rank of is n. If this is truth, the image of the linear map R is all of X. Based on that, we have: