针对四Mecanum轮驱动的自动导引车（AGV）的轨迹跟踪控制问题，设计了一种模型预测控制（MPC）和自适应滑模控制（SMC）级联的控制器，来改善控制精度和稳定性，提高控制过程的层次性、针对性和有效性。在运动学层面，建立了AGV轨迹跟踪误差模型，将其转化为二次规划问题，并加入约束条件，配合模型预测控制的滚动优化来在线求解二次规划的最优解，将AGV位姿误差转化为轮子转速的期望输出；在动力学层面，采用滑模控制得到轮子的输出力矩，实现轮子对期望转速的跟踪，引入具有快速准确逼近能力的极限学习机（ELM）神经网络对模型不确定性和未知干扰进行在线观测，并与滑模控制相结合自适应抵消干扰，进一步提高控制器的鲁棒性。在余弦扰动和脉冲干扰下对控制器进行仿真验证，并将结果与PID控制结果进行对比，发现MPC+SMC级联控制器的跟踪效果具有明显优势；与采用径向基函数（RBF）神经网络观测的级联控制器的对比表明，采用ELM观测器的控制器对干扰的鲁棒性更强，在各转速条件下与干扰曲线的拟合度均超过95%，其跟踪误差在多项指标上相比其他方法小1个数量级，最大位置偏差仅为毫米级。轨迹跟踪样机实验结果验证了该控制器的实用性和可行性。

Aiming at the trajectory tracking control problem of four-Mecanum-wheel AGV (Automatic Guided Vehicle), this study designs a controller named MPC+ELM-SMC, which connects model prediction control and adaptive sliding mode control to improve the control accuracy and stability, and to enhance the control process hierarchy, specificity and effectiveness. At the kinematic level, the trajectory tracking error model is established, which is transformed into a quadratic programming problem, and constraints are added to solve the optimal solution of the quadratic programming online with the rolling optimization of the model predictive control, and the AGV pose error is converted into the expected output of the wheel speed. At the dynamic level, sliding mode control is used to obtain the wheel torque output and to realize the wheel’s tracking of the expected speed. The ELM (Extreme Learning Machine) neural network with fast and accurate approximation ability is introduced to carry out online observation of the model uncertainty and unknown interference, and the adaptive interference is offset in combination with sliding mode control to further improve the robustness of the controller. Under the conditions of cosine disturbance and pulse interference, the controller is simulated and verified. Compared with PID control, the MPC+SMC cascade controller has obvious advantages in tracking effect. Moreover, compared with the cascade controller observed by RBF (Radial Basis Function) neural network, the ELM observer is more robust to interference, for instance, the observation effect remains above 95% under various rotational speed conditions, the tracking error of the proposed control method is one order of magnitude smaller than other methods in multiple indicators, with a maximum deviation of only millimeters. Finally, an experimental platform is set up to perform actual trajectory tracking experiment, and the results verify the practicability and feasibility of the proposed controller.