State and Input Constrained Model Reference Adaptive Control With Robustness and Feasibility Analysis

• 成果类型: Article
• 署名作者: Ghosh, Poulomee; Bhasin, Shubhendu
• 署名单位: Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3654318
• 发表日期: 2026
• 关键词: BARRIER LYAPUNOV FUNCTIONS; Predictive control; nonlinear control; systems; STABILITY

摘要

We propose a model reference adaptive controller (MRAC) for uncertain linear time-invariant plants with user-defined state and input constraints in the presence of unmatched bounded disturbances. Unlike popular optimization-based approaches for constrained control, such as model predictive control (MPC) and control barrier function (CBF) that solve a constrained optimization problem at each step using the system model, our approach is optimization-free and adaptive; it combines a saturated adaptive controller with a barrier Lyapunov function-based design to ensure that the plant state and input always stay within prespecified bounds despite the presence of unmatched disturbances. To the best of the authors' knowledge, this is the first result that considers both state and input constraints for control of uncertain systems with disturbances and provides sufficient feasibility conditions to check for the existence of an admissible control policy. Simulation results, including a comparison with a robust MRAC, demonstrate the effectiveness of the proposed algorithm.