Active speed adjustment path tracking of vehicles based on time-varying local linear MPC

In application scenarios such as mining areas, ports, and industrial parks, autonomous vehicles are required to frequently negotiate sharp turns, U-shaped curves, and continuous high-curvature paths. Under such conditions, path tracking control systems face a fundamental challenge in simultaneously ensuring tracking accuracy and driving efficiency, especially when vehicle steering capability and real-time computational resources are limited. Existing path tracking methods, particularly those based on conservative constant-speed strategies, often sacrifice driving efficiency to maintain accuracy, while methods relying on nonlinear model predictive control (NMPC) frequently suffer from excessive computational burdens in real-time applications. To address this problem, this paper proposes an active speed adjustment path tracking control method based on time-varying local (TVL) linear model predictive control (LMPC), specifically designed for large-curvature path scenarios. The proposed method introduces a time-varying local coordinate system, in which the vehicle dynamics model is reformulated and linearized under the small-angle assumption. This transformation enables the construction of a linear predictive model that remains valid over the prediction horizon, even when the vehicle traverses paths such as right-angle turns and U-shaped curves. On this basis, a multi-reference-point strategy is incorporated into the LMPC framework, allowing the controller to explicitly account for the geometric characteristics of high-curvature paths and thereby improve tracking accuracy compared to conventional single-reference-point LMPC approaches. To resolve the inherent conflict between tracking accuracy and driving efficiency, a rule-based active speed adjustment strategy is further introduced. The strategy dynamically adjusts the vehicle speed based on the current vehicle state, path curvature, and traction limits, ensuring that tracking accuracy is maintained by regulating the speed. The effectiveness of the proposed TVL-LMPC-based active speed adjustment method is validated through comprehensive MATLAB–CarSim co-simulations and Hardware-in-the-Loop (HIL) testing. Simulation results demonstrate that the proposed method can stably complete path tracking tasks under both low-speed and high-speed conditions on large-curvature paths, with a maximum displacement error not exceeding 0.1271 m. In contrast, the constant-speed TVL-LMPC, active speed-adjusted global LMPC, and active speed-adjusted NMPC exhibit error divergence when negotiating sharp turns, highlighting the superior suitability of the proposed approach in complex path scenarios. HIL tests are further conducted to evaluate the performance of the proposed method under conditions closer to practical deployment. To assess robustness against sensing uncertainty, random positioning errors of ±1 cm are introduced in accordance with the performance of typical positioning systems. Under these disturbance conditions, the maximum displacement error remains within 0.1336 m, indicating that the proposed control system maintains stable and accurate tracking performance in the presence of localization disturbances. In terms of real-time performance, under identical simulation conditions, the proposed method reduces the maximum computation time and average computation time by at least 27.95% and 25.61%, respectively, compared to active speed-adjusted NMPC, confirming its suitability for real-time implementation. Overall, the proposed TVL-LMPC-based active speed adjustment path tracking control method effectively improves tracking accuracy and real-time performance on large-curvature paths while enhancing driving efficiency. The method is particularly well-suited for autonomous vehicle applications in mining areas, ports, and industrial parks, where complex path geometries and operational efficiency are both critical.