In order to solve the problems of high identification limitation and low identification rate in nonlinear system identification with fixed structure and parameters, a Subsystem-based Structural Adaptive Filtering (SSAF) method for nonlinear system identification was proposed with introducing structural adaptation into the optimization of identification. Multiple subsystems with linear-nonlinear hybrid structure were cascaded to form the model for this method. The linear part is a 1-order or 2-order Infinite Impulse Response (IIR) digital filter with uncertain parameters, and the nonlinear part is a static nonlinear function. In the initial stage, the parameters of the subsystems were randomly generated, and the generated subsystems were connected randomly according to the set connection rules, and the effectiveness of the nonlinear system was guaranteed by the connection mechanism with no feedback branches. An Adaptive Multiple-Elites-guided Composite Differential Evolution with a shift mechanism(AMECoDEs) algorithm was used for loop optimization of the adaptive model until the optimal structure and parameters were found, that is, the global optimal. The simulation results show that AMECoDEs performs well on nonlinear test functions and real data sets with high identification rate and good convergence rate. Compared with the Focused Time Lagged Recurrent Neural Network (FTLRNN), the number of parameters used in SSAF is reduced to 1/10, and the accuracy of fitness is improved by 7%, which proves the effectiveness of the proposed method.

Aiming at the path planning problem of manipulator with complex static background and multiple constraints, a new multi-population particle swarm optimization algorithm based on elite population and monocular vision was proposed. Firstly, the image difference algorithm was used to eliminate the background, then the contour surrounding method was used to find out the target area, and the model pose estimation method was used to locate the target position. Secondly, a multi-population particle swarm optimization based on elite population was proposed to obtain the optimal angles of the manipulator according to the target position. In this algorithm, the elite population and the sub-populations were combined to form the multi-population particle swarm, and the pre-selection and interaction mechanisms were used to make the algorithm jump out of local optimums. The simulation results show that compared with the real coordinates, the coordinates error of the object position obtained by background elimination method is small; compared with those of the state-of-the-art evolutionary algorithms, the average fitness values of the paths and the Mean Square Errors (MSE) obtained by the proposed algorithm are the smallest for the objects in different positions.