Enhanced model-free predictive control using a Cuckoo search algorithm

Although the Dead-Beat Model Predictive Control (DB-MPC) for induction motor (IM) control has many advantages, there are still that require further study, where DB-MPC is highly dependent on the accuracy of the motor parameters. To improve control robustness, a model-free predictive control method that combines Ultra-Local Model (ULM) and Integrated Sliding Mode Observer (ISMO) with some optimization methods are proposed in this paper. The ULM is used because it does not depend on the uncertain parameters of the all system. Enhanced ISMO design is proposed to reduce the chattering phenomenon. Moreover, the careful calculation of factors related to the design of this strategy affects system performance and ensures system convergence. Furthermore, in this paper, these factors are obtained off-line using a Cuckoo Search Algorithm (CSA). The performance of the proposed control is evaluated under various operating conditions through simulation tests, and the results are compared with the conventional DB-MPC.