Parameter-Free Predictive Control of PMSM Drives: An Incremental Ultra-Local Approach

This paper presents an enhanced model-free predictive control strategy for permanent magnet synchronous motors (PMSMs), based on a novel incremental ultra-local modeling framework. The proposed controller, referred to as the Incremental Ultra-Local Model-Based Predictive Control (IULPC), reformulates the predictive model in terms of temporal input-output differences, enhancing disturbance rejection and reducing sensitivity to modeling drift. Unlike conventional model predictive approaches that require accurate knowledge of motor parameters or additional compensation mechanisms, the proposed method leverages an ultra-local estimation structure to achieve full parameter independence. The incremental formulation further improves robustness by attenuating the impact of slowly varying disturbances and estimator transients, while naturally aligning with digital implementation. A compact real-time estimator is used to capture the aggregated system dynamics in their incremental form. Experimental results under operating conditions representative of electric vehicle propulsion, including rapid acceleration and regenerative braking, parameter mismatches, traction load disturbances, and rapid torque reference changes, demonstrate that the proposed controller consistently achieves accurate and stable performance, validating its effectiveness for real-time PMSM control in electric transportation systems.