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Finite Time Model Predictive Control for Mobile Manipulators With Floating-Base
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AI
DOI：10.1109/TASE.2025.3599904.png)
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Abstract
En 中文
This article focuses on the trajectory tracking problem of mobile manipulators (MMs). Firstly, we construct a position and orientation model predictive tracking control (POMPTC) scheme for mobile manipulators. The proposed POMPTC scheme can simultaneously minimize the tracking error, joint velocity, and joint acceleration. Moreover, it can achieve synchronous control for the position and orientation of the end-effector. Secondly, a finite-time convergent neural dynamics (FTCND) model is constructed to find the optimal solution of the POMPTC scheme. Then, based on the proposed POMPTC scheme, a non-singular fast terminal sliding model (NFTSM) control method is presented, which considers the disturbances caused by the floating-base on the manipulator at the dynamic level. It can achieve finite-time tracking performance and improve the anti-disturbances ability. Finally, simulation and experiments show that the proposed control method has the advantages of strong robustness, fast convergence, and high control accuracy. Note to Practitioners—In actual applications, mobile manipulators need to operate on rugged terrains, over wavy seas, or in windy air, which can cause the base to tilt in uncertain directions or experience forces from different directions at any time. The manipulators need to consider these interference factors in a timely manner to adapt to changing environments. This can be seen as the manipulators being mounted on a floating-base. There have been a large number of studies on mobile manipulators, but there is still a lack of research on the control of mobile manipulators under high-frequency and large disturbances. In the face of different levels of disturbance, the manipulators need corresponding time to calculate and adjust their pose. However, for high-frequency and large disturbances, existing methods struggle to achieve stable control of mobile manipulators in a short time. Therefore, this article proposes a new control scheme to address the aforementioned issues. It should be noted that this method requires acquiring pose information of both the floating base and the manipulator’s end-effector. Consequently, sensors must provide the manipulator’s joint angles, angular velocities, accelerations, and torque information. Additionally, due to its substantial computational demands, the method requires a host computer with a CPU frequency greater than 3.5 GHz and memory exceeding 1 GB to ensure smooth system operation.
Keywords:
Mobile manipulators
model predictive control
neural dynamics
Journal
IF:
6.4
Papers:
4.9K
Citations:
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