Optimum dog-leg angle for mass and bearing force optimization of multistage gear reduction

This paper describes a method to minimize bearing forces as well as bearing and housing mass for a multistage gear reduction. This is done by finding the optimum dog-leg angles for the stages while leaving other aspects of the design unaltered. The optimization is demonstrated first for spur gears, and then for helical gears typically used in electric vehicles. A numerical example shows how bearing forces and mass of bearings and housing are reduced considerably by choosing the optimum dog-leg angle.

A new projection neural network for linear and convex quadratic second-order cone programming

A new projection neural network approach is presented for the linear and convex quadratic second-order cone programming. In the method, the optimal conditions of the linear and convex second-order cone programming are equivalent to the cone projection equations. A Lyapunov function is given based on the G-norm distance function. Based on the cone projection function, the descent direction of Lyapunov function is used to design the new projection neural network. For the proposed neural network, we give the Lyapunov stability analysis and prove the global convergence. Finally, some numerical examples and two kinds of grasping force optimization problems are used to test the efficiency of the proposed neural network. The simulation results show that the proposed neural network is efficient for solving some linear and convex quadratic second-order cone programming problems. Especially, the proposed neural network can overcome the oscillating trajectory of the exist projection neural network for some linear second-order cone programming examples and the min-max grasping force optimization problem.