Selection of Noisy Sensors and Actuators for Regulation of Linear Systems.

摘要

This research has developed and tested an algorithm which aids the controls engineer in placing sensors and actuators in a linear system to best achieve a set of variance specifications on the outputs and inputs of the system. The term best achieve has been defined to be the sensor and actuator configuration which enables a controller to do either of the following: Meet the input specifications while minimizing a sum of output variances normalized by their specification (i.e. input-constrained solution), or meet the output specifications while minimizing a sum of input variances normalized by their specification (i.e., output-constrained solution). The approach taken to solve this sensor and actuator selection (SAS) problem was to use LQG (Linear Quadratic Gaussian) theory to specify a structure for the controller, and then develop an algorithm (SASLQG) that places sensors and actuators in this controller structure to achieve either the input-constrained or output-constrained solution. The main advantage of this approach is the mathematical ease which LQG theory addresses variance constraints, and the main disadvantage is that there may be other controller structures which do better.