Control Constraint

Control constraint research focuses on designing controllers that achieve desired system behavior while adhering to limitations on control inputs and system states, ensuring safety and performance. Current efforts concentrate on developing robust algorithms, such as control barrier functions (CBFs) and their variants (e.g., adaptive, high-order CBFs), constrained optimal control formulations, and reinforcement learning methods incorporating constraints, often leveraging techniques like dynamic programming or neural networks. These advancements are crucial for deploying autonomous systems in safety-critical applications, including robotics, aerospace, and process control, by providing formal guarantees of safe and efficient operation.