We study a networked control architecture where wireless sensors are used to measure and transmit plant outputs to a remote controller. Packet loss probabilities depend upon the time-varying communication channel gains and the transmission powers of the sensors. Within this context, we develop a centralized stochastic nonlinear model predictive controller. It determines the sensor power levels by trading energy expenditure for expected plant state variance. To further preserve sensor energies, the power controller sends coarsely quantized power increment commands only when necessary. Simulations on measured channel data illustrate the performance achieved by the proposed controller.
Nonlinear Model Predictive Control: Towards New Challenging Applications p. 215-224
Lecture Notes in Control and Information Sciences 384/2009