Summary Model predictive control (MPC) is becoming a popular topic in the control of AC motor and power electronic converters because of its high dynamic performance and multitarget optimization ability. However, steady‐state error still exists in conventional MPC method because of unavoidable errors of model parameters. In addition, time delay in digital control also affects the performance, and even stability of MPC. In this paper, an offset‐free robust model predictive control (OFR‐MPC) method is proposed for the current control of three‐phase induction motor (IM). An incremental model is adopted to improve the parameter robustness and to realize offset‐free prediction. An improved current observer is designed to realize time‐delay compensation in control variable optimization. In addition, the improved current observer is also acting as a filter to solve the problem of noise amplification effect of the incremental model. The proposed method can also be applied to other AC motors and power electronic converters. Simulations and experiments have been implemented to verify the performance of the proposed OFR‐MPC method.

With an emphasis placed on a low-carbon economy, photovoltaic grid-connected inverters are moving toward the center of the stage. In order to address the problems related to the strong parameter dependence of the conventional model’s predictive control in grid-connected inverters, an improved parameter-free predictive current control is proposed. Relying on an extended state observer, an ultralocal model is employed to predict future currents without any parameters. The system can achieve a satisfactory performance in terms of dynamic response and robustness. Additionally, 30 virtual voltage vectors are extended for lower current ripples, which is followed by the use of a triangle candidate strategy to significantly ease the computing burden. In general, the proposed strategy omits parameter dependence, complex tuning work, and large tracking errors. The effectiveness of the proposed model is verified through the experimental results.