Intelligent Explicit Model Predictive Control of Planar Motors for High-Precision Position Tracking Applications

To address the challenges of achieving high-precision motion control under physical constraints in motion systems, an intelligent explicit model predictive control (IEMPC) method for planar motors is proposed in this article. Unlike conventional explicit model predictive control (EMPC) approaches that suffer from excessive memory requirements, limited model flexibility, and dependence on commercial solvers, the proposed method integrates intelligent algorithms with an error state feedback solution space to construct a novel EMPC framework. First, a prediction model of error state is established using an augmented state-space model. Based on this model, a multi-objective optimal position control problem under control input constraints is formulated by incorporating a defined cost function. An offline solving strategy is then developed, in which the error state space is partitioned into multiple convex elliptical regions and explicit error state feedback control laws are derived for each subregion. To solve the constrained multi-objective optimal position control problem, an improved nondominated sorting genetic algorithm II (NSGA-II) is designed, generating a set of Pareto-optimal parameters for the control laws. These parameters are subsequently mapped to corresponding elliptical subregions to determine the control law in each subregion. Finally, the effectiveness and superiority of the proposed method are verified through systematic comparative experiments.