Optimal Sensor and Actuator Selection Using Balanced Model Reduction

Optimal sensor and actuator selection is a central challenge in high-dimensional estimation control. Nearly all subsequent control decisions are affected by these locations. In this article, we exploit balanced model reduction greedy optimization to efficiently determine selections that optimize observability controllability. particular, locations scalar measures of controllability using matrix QR pivoting on the dominant modes direct adjoint balancing transformations. Pivoting runtime scales linearly with state dimension, making method tractable for systems. The results demonstrated linearized Ginzburg–Landau system, which our algorithm approximates known optimal placements computed costly gradient descent methods.