论文标题
部分可观测时空混沌系统的无模型预测
A Novel Uncalibrated Visual Servoing Controller Baesd on Model-Free Adaptive Control Method with Neural Network
论文作者
论文摘要
如今，随着机器人武器的应用程序场景的持续扩展，越来越多的场景与机器人武器接触。但是，就机器人臂视觉伺服而言，传统的基于位置的视觉宣誓（PBV）需要大量的校准工作，这对非专业人士而言是具有挑战性的。为了应对这种情况，未校准的基于图像的视觉致暗陶器（UIBV）使人们摆脱了乏味的校准工作。这项工作应用了无模型自适应控制（MFAC）方法，这意味着控制器的参数是实时更新的，从而带来了更好的抑制系统和环境变化的能力。人工智能神经网络应用于手眼关系的控制器和估计器的设计。通过MFAC方法中的系统输入和输出信息的了解，对神经网络进行了更新。受到模型预测控制（MPC）方法中的“预测模型”和“回收者”的启发，并将相似结构引入我们的算法，我们意识到了固定靶标和移动轨迹的未校准的视觉伺服效果。将进行机器人操纵器的模拟实验，以验证所提出的算法。
Nowadays, with the continuous expansion of application scenarios of robotic arms, there are more and more scenarios where nonspecialist come into contact with robotic arms. However, in terms of robotic arm visual servoing, traditional Position-based Visual Servoing (PBVS) requires a lot of calibration work, which is challenging for the nonspecialist to cope with. To cope with this situation, Uncalibrated Image-Based Visual Servoing (UIBVS) frees people from tedious calibration work. This work applied a model-free adaptive control (MFAC) method which means that the parameters of controller are updated in real time, bringing better ability of suppression changes of system and environment. An artificial intelligent neural network is applied in designs of controller and estimator for hand-eye relationship. The neural network is updated with the knowledge of the system input and output information in MFAC method. Inspired by "predictive model" and "receding-horizon" in Model Predictive Control (MPC) method and introducing similar structures into our algorithm, we realizes the uncalibrated visual servoing for both stationary targets and moving trajectories. Simulated experiments with a robotic manipulator will be carried out to validate the proposed algorithm.
