A new computationally efficient algorithm for optimal sensors and actuators placement for large-scale systems

Abstract

Sensor and actuator placement is an important step in control of large-scale systems. Decisions in this context, directly affect the control performance. Therefore a suitable actuator and sensor placement is a prerequisite for the success of any control strategies. The methods developed for optimal placement of the sensors and actuators are known to be computationally expensive in particular for large-scale systems. To remedy this, in this paper a new algorithm which is called Restricted Genetic Algorithm (RGA) is introduced. The RGA is a new genetic-based algorithm which is developed specifically for sensors and actuators placement. This innovative framework not only solves the problem of optimal sensors and actuators placement, but also reduces the computational burden significantly. The method is interesting in particular for applications in control of large-scale systems for which the state-of-art placement methods are not efficient. The effectiveness of presented technique is illustrated by numerical examples.