Input Constraint

Input constraint research focuses on designing control systems that operate safely and effectively within predefined limits on their actuators or inputs. Current efforts concentrate on developing algorithms, such as Control Barrier Functions (CBFs) and their neural network variants (NCBFs), and incorporating them into model predictive control (MPC) frameworks to guarantee safety and optimize performance under these constraints, often for high-dimensional systems and multi-agent scenarios. This work is crucial for deploying autonomous systems in real-world environments, where physical limitations and safety are paramount, improving the reliability and robustness of applications ranging from robotics to aerospace.