Considering coil inductance and the spatial distribution of the magnetic field, this paper developed an approximate distributed-parameter model of a hybrid energy harvester (HEH). The analytical solutions were compared with numerical solutions. The effects of load resistances, electromechanical coupling factors, mechanical damping ratio, coil parameters and size scale on performance were investigated. A meso-scale HEH prototype was fabricated, tested and compared with a stand-alone piezoelectric energy harvester (PEH) and a stand-alone electromagnetic energy harvester (EMEH). The peak output power is 2.93% and 142.18% higher than that of the stand-alone PEH and EMEH, respectively. Moreover, its bandwidth is 108%- and 122.7%-times that of the stand-alone PEH and EMEH, respectively. The experimental results agreed well with the theoretical values. It is indicated that the linearized electromagnetic coupling coefficient is more suitable for low-level excitation acceleration. Hybrid energy harvesting contributes to widening the frequency bandwidth and improving energy conversion efficiency. However, only when the piezoelectric coupling effect is weak or medium can the HEH generate more power than the single-mechanism energy harvester. Hybrid energy harvesting can improve output power even at the microelectromechanical systems (MEMS) scale. This study presents a more effective model for the performance evaluation and structure optimization of the HEH.

A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit‐Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter‐identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm.