This Technical Committee addresses all control problems involving system models that are subject to large size uncertainties and seeks solutions where model uncertainty is compensated for using adaptation and learning rules. It facilitates migrating intelligence into adaptive and learning systems.


Technical Committee 1.2 develops methods for designing and analyzing adaptive and learning systems, including:

• Adaptive Estimators and Optimizers

• Adaptive Predictors and Filters

• Adaptive Observers for Linear and Nonlinear Systems

• Adaptive Observers for Finite- and Infinite-Dimensional Systems

• Sampled-data and Networked Adaptive Observers

• Adaptive Controllers for Linear and Nonlinear Systems

• Adaptive Controllers for Finite- and Infinite-Dimensional Systems

• Robust and Nonlinear Adaptive Controllers

• Output-Feedback Adaptive Controllers

• Self-Tuning and Stochastic Adaptive Controllers

• Gain-scheduling Adaptive Systems

• Multiple-Model and Switched Adaptive Systems

• Fault Detection and Fault-Tolerant Control Systems

• Intelligent and Knowledge-based Adaptive Systems

• Iterative learning and repetitive control Systems

• Reinforcement Learning Systems

• Agent-based Control Systems


Technical Committee 1.2 applies the developed methods in a wide range of engineering areas, including:

• Aeronautics and Aerospace

• Transportation and Automotive Systems

• Power and Energy systems

• Networked and Communication systems

• Speech and Audio processing

• Sonar and Radar

• Medical and Biomedical Engineering

• Robotics and Mechatronic Systems

• Process Engineering and Industrial Manufacturing

• Mining and Minerals Applications

• Components and instruments