  A common aspect of today's cyber-physical systems is that multiple
optimization-based control tasks may execute in a shared processor. Such
control tasks make use of online optimization and thus have large execution
times; hence, their sampling periods must be large as well to satisfy real-time
schedulability condition. However, larger sampling periods may cause worse
control performance. The goal of our work is to develop a robust to early
termination optimization approach that can be used to effectively solve onboard
optimization problems involved in controlling the system despite the presence
of unpredictable, variable, and limited computing capacity. The significance of
the developed approach is that the optimization iterations can be stopped at
any time instant with a guaranteed feasible solution; as a result,
optimization-based control tasks can be implemented with a small sampling
period (and consequently with a minimum degradation in the control
performance).
