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首页> 外文期刊>Journal of Intelligent & Robotic Systems: Theory & Application >A Robust Model Predictive Control Strategy for Trajectory Tracking of Omni-directional Mobile Robots
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A Robust Model Predictive Control Strategy for Trajectory Tracking of Omni-directional Mobile Robots

机译：全方位移动机器人轨迹跟踪的强大模型预测控制策略

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摘要

This paper proposes a robust model predictive control (MPC) strategy for the trajectory tracking control of a four-mecanum-wheeled omni-directional mobile robot (FM-OMR) under various constraints. The method proposed in this paper can solve various constraints while implementing trajectory tracking of the FM-OMR. Firstly, a kinematics model with constraint relationship of the FM-OMR is established. On the basis of the kinematics model, the kinematics trajectory tracking error model of the FM-OMR is further formulated. Then, it is transformed into a constrained quadratic programming(QP) problem by the method of MPC. In addition, aiming at the speed deficiencies of conventional neural networks in QP solving, a delayed neural network (DNN) is applied to solve the optimal solution of the QP problem, and compared with the Lagrange programming neural network (LPNN) to show the rapidity of the DNN. Finally, two simulation cases considering bounded random disturbance are provided to verify the robustness and effectiveness of the proposed method. Theoretical analysis and simulation results show that the control strategy is effective and feasible.
机译：本文提出了一种鲁棒模型预测控制（MPC）策略，用于各种约束下四麦盼室轮形全向移动机器人（FM-OMR）的轨迹跟踪控制。本文提出的方法可以在实现FM-OMR的轨迹跟踪的同时解决各种约束。首先，建立了具有FM-OMR的约束关系的运动学模型。在运动学模型的基础上，进一步制定了FM-OMR的运动学轨迹跟踪误差模型。然后，通过MPC的方法将其转换为约束的二次编程（QP）问题。此外，针对QP求解中传统神经网络的速度缺陷，应用延迟的神经网络（DNN）来解决QP问题的最佳解决方案，并与拉格朗日编程神经网络（LPNN）进行比较以显示快速度DNN。最后，提供了考虑有界随机干扰的两个模拟案例，以验证所提出的方法的鲁棒性和有效性。理论分析和仿真结果表明，控制策略是有效可行的。

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