The Control and Sensing Technologies Research Line advances the theoretical and technological foundations of safe, resilient, and energy-efficient industrial systems. The line integrates control theory, system modeling, sensing technologies, signal processing, estimation, and networked automation to design intelligent systems capable of operating reliably under uncertainty, communication constraints, and dynamic conditions. In particular, it promotes a tight interaction between experimental sensing platforms and control-oriented modeling, where sensing data informs control strategies and control requirements guide the design of sensing architectures. 

The research emphasizes physics-informed modeling, optical fiber sensors, wireless sensing networks, energy-aware control design, and advanced estimation techniques, explicitly linking sensing infrastructure with advanced control theory. Through this integration, sensing systems provide high-fidelity, distributed measurements that enable real-time estimation and feedback control, while control-theoretic tools shape how information is processed and utilized. By combining fundamental control theory with distributed optical fiber sensing and wireless networks, the line seeks to develop a new class of robust and efficient systems for the monitoring and control of large-scale industrial processes and infrastructures. 

Research:   

• Energy-Based and Port-Hamiltonian Control: developing physically consistent models and energy-aware control strategies that ensure stability, robustness, and resilience in complex industrial systems. 

• Distributed Optical Fiber Sensing: developing novel distributed interrogation approaches and advanced signal processing methods to enhance the performance of fiber sensing technologies and expand the range of measurable environmental and structural variables. 

• Point Optical Fiber Sensors: investigating approaches based on arrays of discrete fiber sensors, particularly fiber Bragg gratings (FBGs), together with dedicated interrogation and signal processing techniques for industrial monitoring and instrumentation applications. 

• Identification and Estimation under Uncertainty: integrating adaptive modeling, system identification, and machine learning techniques to enhance real-time prediction, fault detection, and performance optimization in dynamic environments.  

• Networked and Distributed Control Systems: addressing communication delays, packet loss, and cyber-physical constraints in geographically distributed processes, enabling reliable large-scale automation. 

• Wireless Channel Characterization: statistical modeling and measurement-based characterization of microwave channels, including the analysis of path loss, shadowing, multipath fading, and scattering across different frequency bands and environments such as indoor, outdoor, urban, and rural scenarios.  

• Advanced and Predictive Process Control: leveraging model predictive control (MPC), real-time analytics, and AI-enhanced optimization to reduce energy consumption, improve safety margins, and increase operational efficiency.  

• Control of Distributed Parameter Systems: designing spatially resolved control strategies for systems governed by partial differential equations, with applications in energy systems, chemical processes, and large-scale industrial infrastructures. 

Industrial and Technological Impact: The research line contributes to the transformation of industrial processes by enabling safer operations, improved energy efficiency, and increased system resilience. Through energy-aware control design, advanced sensing integration, and predictive automation strategies, the line supports critical sectors such as energy, mining, manufacturing, and large-scale processing industries. 

By combining rigorous mathematical modeling with scalable technological deployments in sensing, the line enhances reliability in safety-critical environments and strengthens Chile’s capacity to develop high-value industrial technologies with global competitiveness.