To tackle the ever-growing demands on high-quality and cost-effective industrial production, recent developments in embedded sensing, wireless communication, and cloud computing offer great opportunities. Resource-aware reliable wireless communication and real-time control are needed to leverage these technologies. The thesis develops a new design framework for such wireless process control systems.   

In the first part, an energy-aware multi-hop network scheduler for remote estimation and control is developed. Multiple sensors transmit their data to a remote estimator or controller through a shared multi-hop network. We develop scheduling algorithms determining which links of the network that should be activated and when to convey sensor data. For remote estimation, an optimization problem minimizing a linear combination of the averaged estimation error and network energy is formulated. We solve the problem by splitting it into tree planning and sensor selection subproblems, and show that an optimal periodic schedule can be obtained. The setting is then extended to an optimal control formulation, where an optimal solution minimizes the combination of the averaged linear quadratic Gaussian control cost and network energy consumption. Algorithms to reconfigure schedules and routes when network link outages are present are also introduced. The applicability of the proposed scheduler is demonstrated in numerical examples.  

In the second part, event-triggered sensing, actuation, and control reconfiguration algorithms are developed. We derive stability conditions under event-triggered actuation for PID, cascade, decoupling, and delay-compensating control systems. Sensors sample and transmit their measurements periodically, while control commands are updated only when a certain event threshold is crossed. A tuning method for the threshold is proposed. We show that the approach yields setpoint tracking and disturbance rejection. Event-triggered sensing together with control reconfiguration is then considered for feedforward and cascade control, illustrating how wireless sensing can efficiently attenuate disturbances. Numerical examples demonstrate how the methods reduce information exchange without closed-loop performance degradation.