3 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 3 / 3
Yayın Kinematic analysis of robotic bevel-gear trains: An application of network model approach(Springer Netherlands, 1998-04) Uyguroğlu, Mustafa Kemal; Tokad, YılmazThe network model approach for rigid and multi-rigid body systems developed recently [1,2] can also be used conveniently in formulating system equations or equations of motion of three-dimensional mechanical systems of interconnected rigid bodies. In this article, this method is further elaborated for establishing only the kinematics of spatial robotic bevel-gear trains. However the dynamic analysis of such systems using the same method is also possible and will be taken up in a future publication.Yayın Weakly non-linear waves in a tapered elastic tube filled with an inviscid fluid(Pergamon-Elsevier Science Ltd, 2005-07) Bakırtaş, İlkay; Demiray, HilmiIn the present work, treating the artery as a tapered, thin walled, long and circularly conical prestressed elastic tube and using the longwave approximation, we have studied the propagation of weakly non-linear waves in such a fluid-filled elastic tube by employing the reductive perturbation method. By considering the blood as an incompressible inviscid fluid, the evolution equation is obtained as the Korteweg-de Vries equation with a variable coefficient. It is shown that this type of equation admits a solitary wave-type solution with variable wave speed. It is observed that, the wave speed decreases with distance for positive tapering while it increases for negative tapering. It is further observed that, the progressive wave profile for expanding tubes (a > 0) becomes more steepened whereas for narrowing tubes (a < 0) it becomes more flattened.Yayın End-effector trajectory control in a two-link flexible manipulator through reference joint angle values modification by neural networks(Sage Publications, 2006-02) Öke, Gülay; İstefanopulos, YorgoThe basic difficulty in the control of flexible link manipulators stems from the fact that the link deflections cannot be controlled directly. Since the number of control inputs, applied by the actuators, is less than the total number of variables to be controlled, control approaches aiming at the suppression of deflections and vibrations are generally insufficient. Another possible approach is to determine new joint trajectories to minimize the error of the end-effector in the operational space. In this paper, a neural network is designed to compute incremental changes for the reference values of the joint angles to achieve successful tip tracking in the operational space. Tip position errors in the x- and y-directions are utihzed as inputs to the neural network. The cost function, which is minimized in training the neural network, is also chosen as the sum of squares of the tip position error in both directions. Joint angle control is provided by a PD controller. Simulations are carried out to evaluate the performance of the neural-network-based trajectory tracking method, and the results are depicted in both joint and operational spaces.
