Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique

Abstract

This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy.

Fast Terminal Sliding Control of Underactuated Robotic Systems Based on Disturbance Observer with Experimental Validation

“…Theorem 3. Consider the disturbed underactuated system (2) and nonsingular fast terminal sliding surfaces (35) and (36). If the control input is employed as…”

“…In past years, much consideration has been paid for tracking control and stabilization of the underactuated structures. Numerous control methods, for instance, Lyapunov redesign [29], passivity-based control [30], optimal control [31], input-output linearization [32], backstepping control [33], nonlinear state-feedback control [34], anti-swing control [35], artificial neural network [36], fuzzy control [37], feedforward control [38], H ∞ control [39], coupling-based control [40], adaptive predictive control [41] and sliding mode control (SMC) [42] have been proposed to design proper trackers and stabilizers of underactuated dynamical systems. SMC is a well-known control technique for design of the robust stabilizers and trackers of various dynamical systems with uncertainty and external perturbation [43][44][45].…”

A finite-time convergent sliding mode control for rigid underactuated robotic manipulator

“…The dynamic coupling between the passive and the active joints is used in Bergerman, Lee, and Xu (1994). In Elangovan and Woo (2004) an adaptive fuzzy sliding control scheme and in Hasan (2012) artificial neural network technique is proposed to control a passive robotic manipulator. Sliding mode control has been used in Neila and Tarak (2011), Kim, Shin, and Lee (2002), Zhihong, Paplinski, and Wu (2002), Mon (2013) and Muñoz, Gaviria, and Vivas (2007) for controlling a rigid robotic manipulator system.…”