AN OPTIMIZED METHOD FOR SOLVING INTERNAL FORCE OF WALKING MOBILE CHASSIS BASED ON RBF NEURAL NETWORK

For the purpose of determining the maximum forces acting on local structural interface of the walking mobile chassis in the structural design, a method using the bi-level optimization model based on the neural network of radial basis function (RBF) is proposed. In first level, the maximum forces acting on local structure of the chassis is, based on a set of given position parameters, optimized by the sequential quadratic programming (SQP) method. The RBF neural network, as a nonlinear mapping relationship between maximum force state in the local structure and position parameters, is trained by using orthogonal design. In second level, the genetic algorithm (GA) is used to optimize the RBF neural network. A high precision RBF neural network is obtained through an iteration procedure, in which the searching volume is reduced using the bisection method. The results show that the calculated results can provide theoretical data for the walking mobile excavator design, and the optimum method is an effective way for nonlinear and multi-variable optimization of the complex mechanism.