DARPA ULTRALOG Final Report - Industrial and Manufacturing ...

Manuscript for IEEE Transactions on Automatic Control 8
4. Overall control procedure
There are two representative optimal control approaches in dynamic systems: Dynamic
Programming (DP) and Model Predictive Control (MPC). Though DP gives optimal closed-loop
policy it has inefficiencies in dealing with large-scale systems especially when systems are
working in finite time horizon [20]-[22]. In MPC, for each current state, an optimal open-loop
control policy is designed for finite-time horizon by solving a static mathematical programming
model [23]-[26]. The design process is repeated for the next observed state feedback forming a
closed-loop policy reactive to each current system state. Though MPC does not give absolutely
optimal policy in stochastic environments, the periodic design process alleviates the impacts of
stochasticity and it is easy to adapt to new contexts by explicitly handling objective function or
constraints.
Considering the characteristic of the current problem, we choose MPC framework. Our
networks are large-scale working in finite time horizon and need to adapt to unpredictable stress
environment. Therefore, under MPC framework, we develop an adaptive control mechanism as
depicted in Fig. 2. First, to address adaptivity we model stress environment by quantifying
resource availability through sensors. Second, we build a mathematical programming model with
the resource availability incorporated, which predicts QoS as a function of alternative algorithms.
Third, we provide an auction market as a decentralized coordination mechanism for solving the
programming model. By periodically opening the auction market, the system can achieve
desirable performance adaptive to changing stress environment while assuring scalability
property. We define sensors and build a mathematical programming model in Section 5, and
refine it based on stability analysis in Section 6. The refined programming model is decentralized
in Section 7.