Modeling and Parameter Identification of a 3D Measurement System Based on Redundant Laser Range Sensors for Industrial Robots.

The low absolute positioning accuracy of industrial robots is one of the bottlenecks preventing industrial robots from precision applications. Kinematic calibration is the main way to improve the absolute positioning accuracy of industrial robots, which greatly relies on three-dimensional (3D) measurement instruments, including laser trackers and pull rope mechanisms. These instruments are costly, and their required intervisibility space is large. In this paper, a precision 3D measurement instrument integrating multiple laser range sensors is designed, which fuses the information of multiple redundant laser range sensors to obtain the coordinates of a 3D position. An identification model of laser beam position and orientation parameters based on redundant distance information and standard spherical constraint is then developed to reduce the requirement for the assembly accuracy of laser range sensors. A hybrid identification algorithm of PSO-LM (particle swarm optimization Levenberg Marquardt) is designed to solve the high-order nonlinear problem of the identification model, where PSO is used for initial value identification, and LM is used for final value identification. Experiments of identification of position and orientation, verifications of the measuring accuracy, and the calibration of industrial robots are conducted, which show the effectiveness of the proposed 3D measurement instrument and identification methods. Moreover, the proposed instrument is small in size and can be used in narrow industrial sites.

Offline and online parameter estimation of nonlinear systems: Application to a solid oxide fuel cell system.

In this paper, an offline tuning strategy and an online parameter estimation method are exploited to calibrate the solid oxide fuel cell mathematical model. Different to existing offline tuning strategy, the developed strategy is designed in order to tune the model under various operation conditions. First, the particle swarm optimization method combined with the gradient-based search method is applied to tune unknown parameters in the state-space model and the steady-state model for each operation condition. Then, the sensitive parameters are expanded to the polynomial equations. Moreover, the reconstructed model including coefficients in the polynomial equations are determined by using the particle swarm optimization method with gradient-based search method for whole operation conditions. To show the slowly time-varying performance of a solid oxide fuel cell, an adaptive optimal learning law based on the optimization technology is proposed to online minimize a cost function with the information of the estimation error. The estimation error is extracted through several low-pass filters and simple algebraic calculation. Finally, the proposed offline tuning strategy and the developed online adaptive estimation method are verified by conducting experiments on a practical solid oxide fuel cell test bench.