A Structure-Exploiting Constraint Preprocessing Method for Laguerre-Based Model Predictive Control

초록

This study presents a constraint-preprocessing framework for Laguerre-based model predictive control (LMPC) aimed at improving real-time implementability in constrained control. Although LMPC reduces decision variables through input parameterization, the computational burden can remain high due to numerous linear inequality constraints. To address this, we propose a structure-exploiting algorithm that selects a compact set of dominant constraints prior to optimization. The procedure combines normalization, prediction-based screening, row-wise extrema detection, violation analysis of the unconstrained solution, and warm-start propagation. The retained constraint set is selected to represent the dominant part of the original linear inequality set while reducing computational cost. The approach is demonstrated on a numerical study and high-fidelity CarSim-based vehicle simulations. Results indicate near-optimal tracking with reduced computation compared with conventional MPC and alternative input-parameterization baselines. Under matched computational budgets, the proposed LMPC yields the most favorable performance-computation trade-off among the tested reduced-parameterization methods. The framework is useful when execution time is limited by a fixed compute budget and is applicable beyond automotive to other constrained engineering systems.

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