{"ID":5554348,"CreatedAt":"2026-07-02T02:11:27.934456424Z","UpdatedAt":"2026-07-04T17:38:54.732508317Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01215","arxiv_id":"2607.01215","title":"Computationally Efficient Near-Optimal Control for Current Ripple Reduction and Optimization of Three-Phase Motors via LMIs","abstract":"The optimal control of three-phase permanent-magnet synchronous motors (PMSMs) is challenging due to their nonlinearity and the discrete nature of the control set. Existing approaches either rely on mixed-integer trajectory optimization or require computationally intensive value-iteration procedures. This paper proposes a Linear Matrix Inequality (LMI)-based method for approximating the infinite-horizon value function using a quadratic parameterization and iterated Bellman inequalities, yielding a tractable convex program. The computed function can be obtained efficiently offline and used online as a tail cost in a horizon-one optimal control law. Simulation results show that the proposed approach achieves a favorable trade-off between switching effort and current ripple, with performance comparable to that of finite-control-set MPC but with a significantly lower computational cost.","short_abstract":"The optimal control of three-phase permanent-magnet synchronous motors (PMSMs) is challenging due to their nonlinearity and the discrete nature of the control set. Existing approaches either rely on mixed-integer trajectory optimization or require computationally intensive value-iteration procedures. This paper propose...","url_abs":"https://arxiv.org/abs/2607.01215","url_pdf":"https://arxiv.org/pdf/2607.01215v1","authors":"[\"Huu-Thinh Do\",\"Trung B. Tran\",\"Jing Sun\",\"Ilya Kolmanovsky\"]","published":"2026-07-01T17:53:21Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"eess.SY\"]","methods":"[\"Large Language Model\"]","has_code":false}