论文标题
在计算能力有限的网络物理系统中实施基于优化的控制任务
Implementing Optimization-Based Control Tasks in Cyber-Physical Systems With Limited Computing Capacity
论文作者
论文摘要
当今网络物理系统的一个常见方面是，共享处理器可能会执行多个基于优化的控制任务。这样的控制任务利用在线优化，因此具有较大的执行时间；因此，它们的采样周期也必须很大，以满足实时可调度性条件。但是，较大的采样周期可能会导致控制性能较差。我们工作的目的是开发一种强大的早期终止优化方法，该方法可用于有效解决控制系统中涉及的板载优化问题，尽管存在不可预测，可变和有限的计算能力。开发方法的意义在于，可以随时使用可行的解决方案立即停止优化迭代。结果，可以在较小的采样周期（因此在控制性能中具有最小降解）来实现基于优化的控制任务。
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).
