Industrial robotic systems with fuzzy logic controller and neural network

Sang-Bae Lee
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引用次数: 3

Abstract

Generally, when we control the robot, we should calculate exact inverse kinematics. However, inverse kinematics calculation is complex and it takes much time for the manipulator to control in real time. Therefore, the calculation of inverse kinematics can result in a significant control delay in real time. We present a method in which inverse kinematics can be calculated through fuzzy logic mapping, based on an exact solution through fuzzy reasoning instead of inverse kinematics calculation. Also, the result provides sufficient precision and transient tracking error can be controlled based on a fuzzy adaptive scheme. We also demonstrate that neural networks can be used effectively for the control of a nonlinear dynamic system with uncertain or unknown dynamics models and applied to the control robot. The advantage of using the neural approach over the conventional inverse kinematics algorithms is that neural networks can avoid time consuming calculations. We represent a good control efficiency through simulation of a 2-DOF manipulator by fuzzy logic controller, and demonstrate the effectiveness of the proposed learning scheme using feedforward neural networks, too.
基于模糊逻辑控制器和神经网络的工业机器人系统
一般在控制机器人时,都要计算精确的逆运动学。然而,机械臂的逆运动学计算复杂,实时控制耗时长。因此,逆运动学的计算在实时中会导致很大的控制延迟。本文提出了一种基于模糊推理的精确解而不是基于逆运动学计算的模糊逻辑映射计算逆运动学的方法。结果表明,基于模糊自适应方案可以有效地控制瞬态跟踪误差。我们还证明了神经网络可以有效地用于具有不确定或未知动力学模型的非线性动态系统的控制,并应用于控制机器人。与传统的逆运动学算法相比,使用神经网络的优点是神经网络可以避免耗时的计算。通过模糊逻辑控制器对二自由度机械臂的仿真,验证了所提出的学习方案在前馈神经网络中的有效性。
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