基于滑模预测控制的海底采矿车轨迹跟踪算法

海底采矿车多工作于稀软底质，其面临的外部扰动较大，难以快速收敛跟踪误差，精准地跟踪预设轨迹。为此，本文提出了一种海底采矿车的滑模预测控制（sliding model predictive control，SMPC）轨迹跟踪算法。基于海底采矿车的运动学模型，首先设计滑模控制率实现轨迹跟踪误差快速收敛，其次利用少预测时域的线性时变模型预测控制算法（linear time varying model predictive control，LTV-MPC）优化该滑模控制率。而后，通过证明滑模控制率收敛和模型预测控制稳定，保证了闭环控制系统的稳定性。RecurDyn&Simulink联合仿真结果表明，与单一的滑模控制（sliding mode control，SMC）和线性时变模型预测控制算法相比，所提出的SMPC轨迹跟踪算法提高了轨迹跟踪精度，且算法具有较好的实时性。

A Sliding Mode Predictive Control-based Trajectory Tracking Algorithm for a Seabed Mining Vehicle

Seabed mining vehicles mostly work in thin and soft sediments, suffered from large external disturbance, so it is difficult to quickly converge the tracking error and accurately track the preset trajectory. A sliding mode predictive control-based (SMPC) trajectory-tracking algorithm is proposed in this study. First, based on the kinematic model of the mining vehicle, the trajectory-tracking error is quickly offset by a defined rule in the sliding mode control (SMC). Next, the rule is optimized by a linear time-varying model predictive control algorithm (LTV-MPC) with fewer predictive control steps. Here, the stability of the closed-loop control system is achieved. The joint simulation results of RecurDyn & Simulink show that compared with sliding mode control and linear time-varying model predictive control algorithm, the SMPC improves the trajectory-tracking accuracy and has good real-time performance.