Auto‐Tuning Weights Model Predictive Control for Three‐State Switching Cell Boost Converters

ABSTRACT This work presents an improved robust model predictive control (MPC) for DC‐DC boost converters operating under polytopic uncertainty. The proposed controller guarantees constraint satisfaction for voltage, current, and duty cycle bounds while ensuring asymptotic stability through a common quadratic Lyapunov function. To circumvent the computational complexity of online optimization, the control law is synthesized offline via semidefinite programming (SDP) using linear matrix inequalities (LMIs), yielding a static state‐feedback gain. An adaptive weight tuning algorithm is developed to automatically adjust cost function matrices in response to time‐varying operating conditions, thereby enhancing closed‐loop performance and disturbance rejection. Numerical simulations validate the theoretical results and demonstrate superior tracking performance and constraint satisfaction compared to existing methods, establishing the efficacy of the proposed approach for high‐performance power electronic systems.