LMIs in Control/Stability Analysis/Stabilizability LMI
Stabilizability LMI
Stabilizability is a essentially a weaker version of the controllability condition. A system is stabilizable if all unstable modes of the system are controllable. This implies that if the system is controllable, it will also be stabilizable. The LMI condition for stabilizability of pair is shown below.
The System edit
where , , at any .
The Data edit
The matrices necessary for this LMI are and . There is no restriction on the stability of A.
The LMI: Stabilizability LMI edit
is stabilizable if and only if there exists such that
- ,
where the stabilizing controller is given by
- .
Conclusion: edit
If we are able to find such that the above LMI holds it means the matrix pair is stabilizable. In words, a system pair is stabilizable if for any initial state an appropriate input can be found so that the state asymptotically approaches the origin. Stabilizability is a weaker condition than controllability in that we only need to approach as whereas controllability requires that the state must reach the origin in a finite time.
Implementation edit
This implementation requires Yalmip and Sedumi.
https://github.com/eoskowro/LMI/blob/master/Stabilizability_LMI.m
Related LMIs edit
WIP: Will be linked once they have been created.
Controllability LMI
Schur Stability LMI
Observability LMI
Detectability LMI
Controllability Grammian LMI
Observability Grammian LMI
External Links edit
A list of references documenting and validating the LMI.
- LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
- LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
- LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
- LMIs in Control Systems: Analysis, Design and Applications - by Guang-Ren Duan and Hai-Hua Yu, CRC Press, Taylor & amp; Francis Group, 2013, Section 6.1.1 and Table 6.1 pp. 166–170, 192.
- A Course in Robust Control Theory: a Convex Approach, - by Geir E. Dullerud and Fernando G. Paganini, Springer, 2011, Section 2.2.3, pp. 71-73.