LAUSR

With the rapid development of mobile computing, mission-critical Internet of Things (IoT) systems have become popular. Typical mission-critical IoT systems may contain complicated unknown and unstable elements and it is of particular importance to identify and stabilize them as unstable systems may experience catastrophic consequences. We consider the identification and optimal control for a mission-critical IoT system over multiple-input–multiple-output (MIMO) fading channels. First, we focus on the optimal control of the mission-critical IoT system, assuming that the system dynamics are known, and propose a novel stochastic-approximation-based algorithm to learn the optimal control solution for the IoT controller in an online manner. Second, we extend the optimal control framework to deal with the unknown mission-critical IoT system and propose a novel normalized-stochastic-gradient-descent-based algorithm to simultaneously identify and control the system in an online manner. Using the Lyapunov stability analysis, we theoretically show the asymptotic optimality of the proposed learning algorithms. Numerical results are analyzed for our proposed scheme and for several state-of-the-art learning schemes in terms of the computational complexity, convergence, and stability performance. Specifically, the proposed scheme can be implemented more than 50% faster than the state-of-the-art learning schemes. Moreover, the system identification performance of the proposed scheme can achieve a normalized system identification mean square error (MSE) of around 0.01 in 100 iterations. This is a substantial improvement compared to the baseline algorithms, where the normalized system identification MSE diverges.