Abstract
This paper presents an inverse kinematic controller using neural networks for trajectory controlling of a delta robot in real-time. The developed control scheme is purely data-driven and does not require prior knowledge of the delta robot kinematics. Moreover, it can adapt to the changes in the kinematics of the robot. For developing the controller, the kinematic model of the delta robot is estimated by using neural networks. Then, the trained neural networks are configured as a controller in the system. The parameters of the neural networks are updated while the robot follows a path to adaptively compensate for modeling uncertainties and external disturbances of the control system. One of the main contributions of this paper is to show that updating the parameters of neural networks offers a smaller tracking error in inverse kinematic control of a delta robot with consideration of joint backlash. Different simulations and experiments are conducted to verify the proposed controller. The results show that in the presence of external disturbance, the error in trajectory tracking is bounded, and the negative effect of joint backlash in trajectory tracking is reduced. The developed method provides a new approach to the inverse kinematic control of a delta robot.
Highlights
The study of robot kinematics and dynamics is crucial for robot design, control, and simulation; kinematic and dynamic modeling can be computationally expensive and time consuming [1,2]
Radial Function Basis (RFB) and Multilayer Perceptron (MLP) are the two main neural networks used in robot model estimation
This paper proposes inverse kinematic control for a delta robot using a neural network algorithm in real-time
Summary
The study of robot kinematics and dynamics is crucial for robot design, control, and simulation; kinematic and dynamic modeling can be computationally expensive and time consuming [1,2]. The kinematic model of a robot can be derived analytically based on the physics and structure of the robot. Recent studies on neural networks and deep learning have proven that robotic control can benefit from this method, and neural networks can replace the complicated mathematical modeling of some systems [2,4,5,6]. They can identify non-linearity between input and output data, which makes them good candidates for estimating the mathematical modeling of a system [7]. Yang et al [8]
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