More Realistic Dynamical Modeling of a Humanoid Robotic Arm to Reduce Control Effort with Uncertainty Using Self-Impact Joint Constrained

Background: the main challenge of modeling humanoid robots is establishinga compromise between the simplicity of the model and accuracy of the system.one of the realities of movement which is important about humans is movingtheir hands to keep balance and reduce energy consumption while they arewalking.methods: in this context, the role of elbow joint and the limitation that thejoint exerts in terms of movement on the forearm and arm as self-impactjoint constraint is undeniable. this paper deals with modeling and control ofhumanoid robot’s hand as double-pendulum will consider mentioned constraintwhile normal walking and also in throwing darts.results: the presence of the self-impact joint constraint contributed to about a26% saving in power consumption of robot motors within an impact range of0.6346 to 0.6896 during normal human walking.since this control has a high power, 10 to 30% of the uncertainty was added tothe length and mass parameters. as was observed, this controller routed thedesired curves in the least possible time.conclusion: as mentioned earlier, consideration of this constraint in elbowjoint of the humanoid robot will help in approaching the reality of system incomparison with past models previously designed. as constraint causes additionof severe nonlinear terms to dynamic system equation, the control of systemswith this type of constraint faces a great deal of complexity. for adaptive-neuralcontroller to control of the system of humanoid robot’s hand will be used. also,to display the ability of control system, the uncertainty of length and mass forthis system will be considered. the existence of self-impact joint constraint willcause saving in consumption power of robot engines within the impact range.2019© the authors. published by jrsr. all rights reserved.