Model Optimization Strategy Based on Moving Horizon Estimation for Induction Motor

Getting a desired model for the controlled objective under a complex environment is worth constant exploration as the upper limit of control performance depends on the used model. This paper presents a generalized model optimization method to strengthen the robustness of drive systems and achieve improved performance. The proposed strategy designs a decoupled feedback structure for rotor flux and stator current, and then limited horizon correction methods are formulated by considering the characteristics of the mathematical model. Meanwhile, real-time optimization of the utilized model is realized by solving an unconstrained minimization problem through a tailored iterative solution. Compared to existing methods, the proposed approach has the capability to obviate the mutual influences between different methods, integrate with the model-based method seamlessly, and substantially enhance correction results. The comparative results from simulations and experiments conducted on an embedded control system with an induction motor have substantiated its superiority.