Multi-objective optimization design of parallel manipulators using a neural network and principal component analysis

Abstract

In this work, a multi-objective optimization design method is proposed based on principal component analysis (PCA) and a neural network to obtain a mechanism's optimal comprehensive performance. First, multi-objective optimization mathematical modeling, including design parameters, objective functions, and constraint functions, is established. Second, the sample data are obtained through the design of the experiment (DOE) and are then standardized to eliminate the adverse effects of a non-uniform dimension of objective functions. Third, the first k principal components are established for p performance indices (k<p) using the variance-based PCA method, and then the factor analysis method is employed to define its physical meaning. Fourth, the overall comprehensive performance evaluation index is established by objectively determining weight factors. Finally, the computational cost of the modeling is improved by combining the neural network and a particle swarm optimization (PSO) algorithm. Dimensional synthesis of a Sprint (3RPS) parallel manipulator (PM) is taken as a case study to implement the proposed method, and the optimization results are verified by a comprehensive performance comparison of robots before and after optimization.