  In this letter, we investigate the problem of actuator scheduling for
networked control systems. Given a stochastic linear system with a number of
actuators, we consider the case that one actuator is activated at each time.
This problem is combinatorial in nature and NP hard to solve. We propose a
convex relaxation to the actuator scheduling problem, and use its solution as a
reference to design an algorithm for solving the original scheduling problem.
Using dynamic programming arguments, we provide a suboptimality bound of our
proposed algorithm. Furthermore, we show that our framework can be extended to
incorporate multiple actuators scheduling at each time and actuation costs. A
simulation example is provided, which shows that our proposed method
outperforms a random selection approach and a greedy selection approach.
