Simulink for Adaptive Control

In control systems, model-free adaptive (MFA) control presents significant challenges. To address this problem, we developed a Simulink-based adaptive control program implementing several advanced techniques including model reference adaptive control (MRAC) and empirical model-based adaptive control. The program utilizes gradient-based adaptation algorithms with real-time parameter tuning through embedded MATLAB functions and S-function blocks. For optimization, we integrated model predictive control (MPC) algorithms with rolling horizon optimization and model-independent adaptive control techniques using recursive least squares (RLS) estimation. The implementation features gain scheduling modules with anti-windup compensation and stability monitoring through Lyapunov-based analysis blocks. These algorithmic approaches enable improved control performance with higher precision through dynamic adjustment of controller parameters. The system incorporates performance monitoring blocks that track objective functions and constraint handling mechanisms for reliability enhancement. Through these implementations, our Simulink program not only solves MFA control problems but also significantly improves overall control system performance, stability, and reliability.