Increasingly faced with the requirements of safety, environmental sustainability, and profitability, chemical process operation is relying extensively on highly automated control systems. Traditionally, control systems utilize point-to-point wired communication links using a small number of sensors and actuators. The operation of chemical processes, therefore, could benefit from the deployment of control systems using hybrid communication networks that take advantage of an efficient integration of the existing, point-to-point communication networks (wire connections from each actuator/sensor to the control system using dedicated local area networks) and additional networked (wired or wireless) actuator/sensor devices. Such an augmentation in sensor information and network-based availability of wired and wireless data is now well underway in the process industries [1,2,3,4] and clearly has the potential to be transformative in the sense of dramatically improving the ability of the single-process and plantwide model-based control systems to optimize process and plant performance (in terms of achieving control objectives that go well beyond the ones that can be achieved with control systems using wired, point-to-point connections) and prevent or deal with adverse and emergency situations more quickly and effectively (fault-tolerance). Hybrid communication networks allow for easy modification of the control strategy by rerouting signals, having redundant systems that can be activated automatically when component failure occurs, and in general, they allow having a high-level supervisory control over the entire process [1,2,3,4]. However, augmenting existing control networks with real-time wired/wireless sensor and actuator networks challenges many of the assumptions in traditional process control methods dealing with dynamical systems linked through ideal channels with flawless, continuous communication. In the context of hybrid communication networks which utilize networked sensors and actuators, key issues that are important for process control include robustness, reliability and interference. These issues need to be carefully handled because integrated wired and wireless communication networks introduce more components in order to substantially improve closed-loop performance and fault-tolerance, and this increases the probability of missing data at any given point in time.
In this work, we introduce a two-tier control architecture for nonlinear process systems with both continuous and asynchronous sensing and/or actuation. This class of systems arises naturally in the context of process control systems based on hybrid communication networks (i.e, point-to-point wired links integrated with networked wired/wireless communication) and utilizing multiple heterogeneous measurements (e.g., temperature and concentration). Assuming that there exists a lower-tier control system which relies on point-to-point communication and continuous measurements to stabilize the closed-loop system, we propose to use Lyapunov-based model predictive control to design an upper-tier networked control system to profit from both the continuous and the asynchronous measurements as well as from additional networked control actuators. The proposed two-tier control system architecture preserves the stability properties of the lower-tier controller while improving the closed-loop performance. The theoretical results are demonstrated using two different chemical process examples.
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