LMIs in Control/Click here to continue/Controller synthesis/H-inf Optimal Quadratically Stabilizing Controllers with Parametric Norm-Bounded Uncertainty

If there exists some $$\mu \geq 0, P>0$$, and $$Z$$ such that the LMI holds, then the system satisfies $$\|y\|_{L_2} \leq \gamma \|u\|_{L_2}.$$ There also exists a controller with $$u(t) = Kx(t).$$

The System


\begin{align} \dot x(t)&=Ax(t)+Bu(t)+Mp(t)+B_2w(t),&&p(t) = \Delta (t)q(t),\\ q(t) &= Nx(t)+D_{12}u(t),&&\Delta \in \mathbf{\Delta} \; := \{\Delta \in \mathbb{R}^{n \times n} : \| \Delta \| \leq 1 \}\\ y(t) &= Cx(t)+D_{22}u(t)\\ \end{align}$$

The Data
The matrices $$ A,B,M,B_2,N,D_{12},C,D_{22} $$.

The Optimization Problem
Minimize $$ \gamma $$ subject to the LMI constraints below.

The LMI:


\begin{align} \text{Find} \; &P>0,\mu \geq 0, \text{ and } Z:\\ \begin{bmatrix} AP+BZ+PA^T+Z^TB^T+B_2B_2^T+\mu MM^T & (CP+D_{22}Z)^T & PN^T+Z^TD_{12}^T \\ CP+D_{22}Z & -\gamma ^2I & 0 \\ NP+D_{12}Z & 0 & -\mu I \end{bmatrix} < 0\\ \end{align}$$

Conclusion:
The controller gains, K, are calculated by $$ K = ZP^{-1} $$.

Implementation
https://github.com/mcavorsi/LMI

Related LMIs
Quadratically Stabilizing Controllers with Parametric Norm-Bounded Uncertainty

Stabilizing State-Feedback Controllers with Structured Norm-Bounded Uncertainty

Optimal State-Feedback Controllers with Structured Norm-Bounded Uncertainty