Improving trajectory tracking of a three axis SCARA robot using neural networks

Abstract

In this paper, a neural-network based robust adaptive controller is proposed to control an industrial robot considering non-linearities, uncertainties and external perturbations. Three-axis SCARA robots is used to test the performance of this controller. The nonlinear system is treated as a partially known system. The known dynamic is used to design a nominal feedback controller based on the well-known feedback linearization method and PD controller. A Variable Structure Controller is added to the PD loop to provide robustness to uncertainties in the model of the system in order to improve accuracy of the trajectory tracking. A Neural Network (NN) based robust adaptive tracking controller is applied to further improves the control action. The outputs of the NNs are used to compensate the effects of the system uncertainties and to improve the tracking performance. Using this scheme, strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, the output tracking error between the plant output and the desired reference output can asymptotically converge to zero as well. This controller exhibited superior performance characteristics where the maximum absolute error for the three-axis SCARA robot is considerably reduced.

SVM-Based Control System for a Robot Manipulator

“…After substituting equation (3) into equation (5) and recognizing the relations (8) in the resulting equation, we obtain the following error equation:…”

“…However, few have been proposed to deal with both dynamic and kinematics uncertainties [3,4]. To deal with these issues, researchers have demonstrated the positive use of neural networks in learning system uncertainties [5] and in helping in the generation of control laws [6,7,8]. In fact, instead of generating the feed-forward signal mathematically, it can be learnt from the feedback signal using neural networks.…”

Modelling and Simulation of Fuzzy-based Coordination of Trajectory Planning and Obstacle Avoiding for RRP-Typed SCARA Robots

“…The Selective Compliance Assembly Robot Arm (SCARA robot) was firstly invented by Hiroshi Makino in 1978 [1]. Since then, SCARA robot has been developed for different Degree of Freedom (DoF) such as 3-DoF [2], [3], 4-DoF [4], 5-DoF [5,6], and 6-DoF [7], [8]. In particular, SCARA robot with 3-DoF has become one of the most applied industrial robots due to its simple and basic kinematic structures.…”

“…Fuzzy logic is an intelligent control tool that simplifies the complexity of nonlinear control through IF-THEN rules. Thanks to this advantage, many fuzzy logic solutions are proposed for controlling 3-DoF SCARA robot such as trajectory tracking control [2], position control [14], path planning control [15], obstacle avoidance [16], [17]. The Matlab simulation results of [2] show that it is possible to apply fuzzy logic for the RRP-typed SCARA controller to reduce the loop trajectory errors.…”

“…Thanks to this advantage, many fuzzy logic solutions are proposed for controlling 3-DoF SCARA robot such as trajectory tracking control [2], position control [14], path planning control [15], obstacle avoidance [16], [17]. The Matlab simulation results of [2] show that it is possible to apply fuzzy logic for the RRP-typed SCARA controller to reduce the loop trajectory errors. From the result in [14], it is proven that the designed fuzzy logic controller help the RRP SCARA robot move smoother for tracking trajectory but without obstacle avoidance.…”