Abstract
In this paper, a novel forward adaptive neural MIMO NARX model is used for modelling and identifying the forward kinematics of an industrial 3-DOF robot arm system. The nonlinear features of the forward kinematics of the industrial robot arm drive are thoroughly modelled based on the forward adaptive neural NARX model-based identification process using experimental input-output training data. This paper proposes a novel use of a back propagation (BP) algorithm to generate the forward neural MIMO NARX (FNMN) model for the forward kinematics of the industrial 3-DOF robot arm. The results show that the proposed adaptive neural NARX model trained by a Back Propagation learning algorithm yields outstanding performance and perfect accuracy.
Highlights
The robot control problems can be divided into two main areas: kinematics control and dynamic control
This paper proposes the novel use of an adaptive neural NARX model to generate the Forward Neural MIMO NARX (FNMN) model for the nonlinear forward kinematics of a 3‐DOF industrial robot arm system
This paper investigates the potentiality of various simple adaptive Neural MIMO NARX models in order to exploit them in modelling, identification and control
Summary
The robot control problems can be divided into two main areas: kinematics control (the coordination of the links of a kinematics chain to produce the desired motion of the robot) and dynamic control (the driving of the actuator of a mechanism to follow the commanded positional velocities). Karlik et al [4] developed an improved approach to the solution of forward kinematics problems for robot arms In their approach, a structured artificial neural network (ANN) was proposed to control the motion of a robot arm. A Back Propagation (BP) learning algorithm is used to process the experimental input‐output data that is measured from the forward kinematics of the industrial robot arm system to optimize all of the nonlinear and dynamic features of this system. The BP algorithm optimally generates the appropriate neural weightings so as to perfectly characterize the dynamic features of the forward kinematics of the industrial robot arm These good results are due to the proposed FNMN model, which combines the extraordinary approximating capability of the neural system with the powerful predictive and adaptive potentiality of the nonlinear ARX structure that is implied in the proposed FNMN model.
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