Synthetic Robust Model Predictive Control With Input Mapping for Constrained Visual Servoing
Synthetic Robust Model Predictive Control With Input Mapping for Constrained Visual Servoing
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DOI：
10.1109/tie.2022.3212411
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发表时间：
2023-09
影响因子：
7.7
通讯作者：
Shaoying He;Yunwen Xu;Yaonan Guan;Dewei Li;Y. Xi
中科院分区：
文献类型：
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作者：
Shaoying He;Yunwen Xu;Yaonan Guan;Dewei Li;Y. Xi
针对带约束的基于图像的视觉伺服（IBVS）问题，提出了一种带输入映射的综合鲁棒模型预测控制（RMPC）方法，该方法通过离线设计鲁棒控制律和在线线性补偿过去数据来构造新的控制律。该方法克服了RMPC的保守性，减少了在线计算量。输入映射方法不像大多数自适应控制方法那样需要慢时变模型或时不变模型，适用于IBVS。其线性组合系数可通过求解二次规划问题在线优化。证明了该方法的稳定性，并证明了其收敛速度比传统的RMPC更快。一个实时实验的六自由度机械手与眼在手的结构设计，以评估所提出的方法。结果表明，该方法不仅具有处理约束和奇异性问题的能力，而且比几种经典的鲁棒控制方法具有更快的收敛速度，计算效率比在线RMPC提高了一个数量级。
This article proposes a synthetic robust model predictive control method (RMPC) with input mapping for the image-based visual servoing (IBVS) problem with constraints, where the novel control law is constructed by the robust control law designed offline and the online linear compensation of the past data. This proposed method can overcome the conservatism of RMPC and reduce the online computational burden. The input mapping method is suitable for the IBVS with no requirement of the slow time-varying model or time-invariant model as most adaptive control methods need. Its linear combination coefficients can be online optimized by solving a quadratic programming problem. The stability of the visual servoing system under our proposed method is proven, and its convergence speed is demonstrated to be faster than the traditional RMPC. A real-time experiment on a six-degree-of-freedom manipulator with eye-in-hand construction is designed to evaluate the proposed method. The results indicate that besides the ability to handle the constraint and the singularity problem, our proposed method provides a faster convergence rate than several classic robust control methods, and improves the computational efficiency by an order of magnitude compared with the online RMPC.