Aiming at the formation control of multiple Mecanum-wheeled mobile robots (MWMRs) with physical constraints and model uncertainties, a novel robust control scheme that combines model predictive control (MPC) and extended state observer-based adaptive sliding mode control (ESO-ASMC) is proposed in this paper. First, a linear MPC strategy is proposed to address the motion constraints of MWMRs, which can transform the robot formation model based on leader-follower into a constrained quadratic programming (QP) problem. The QP problem can be solved iteratively online by a delay neural network (DNN) to obtain the optimal control velocity of the follower robot. Then, to address the input saturation constraints, model uncertainties and unknown disturbances in the dynamic model, an improved ESO-ASMC is proposed and compared with the robust adaptive terminal sliding mode control (RATSMC) and the conventional sliding mode control (SMC) to prove the effectiveness. The proposed scheme, considering the optimal control velocity obtained by the kinematics controller as the given desired velocity of the dynamics controller, can implement precise formation control, while solving various physical constraints of the robot, and eliminating the effects of model uncertainties and disturbances. Finally, through a comparative simulation case, the effectiveness and robustness of the proposed method are verified.

针对具有物理约束和模型不确定性的多麦克纳姆轮移动机器人（MWMR）的编队控制，一种结合模型预测控制（MPC）和基于扩展状态观测器的自适应滑模控制（ESO-ASMC）的新型鲁棒控制方案本文提出。首先，提出了一种线性 MPC 策略来解决 MWMR 的运动约束，该策略可以将基于领导者 - 跟随者的机器人编队模型转换为约束二次规划 (QP) 问题。QP问题可以通过延迟神经网络（DNN）在线迭代求解，以获得跟随机器人的最优控制速度。然后，为了解决动态模型中的输入饱和约束、模型不确定性和未知干扰，提出了一种改进的ESO-ASMC，并与鲁棒自适应终端滑模控制（RATSMC）和传统滑模控制（SMC）进行了比较以证明其有效性。该方案将运动学控制器获得的最优控制速度作为给定的动力学控制器的期望速度，可以实现精确的编队控制，同时解决机器人的各种物理约束，消除模型不确定性和扰动的影响。最后，通过一个对比仿真案例，验证了所提方法的有效性和鲁棒性。将运动学控制器获得的最优控制速度作为给定的动力学控制器期望速度，可以实现精确的编队控制，同时解决机器人的各种物理约束，消除模型不确定性和扰动的影响。最后，通过一个对比仿真案例，验证了所提方法的有效性和鲁棒性。将运动学控制器获得的最优控制速度作为给定的动力学控制器期望速度，可以实现精确的编队控制，同时解决机器人的各种物理约束，消除模型不确定性和扰动的影响。最后，通过一个对比仿真案例，验证了所提方法的有效性和鲁棒性。