Differential Particle Swarm Evolution for Robot Control Tuning
Document Type
Conference Proceeding
Publication Date
2014
Publication Title
American Control Conference
Abstract
We present a differential particle swarm evolution (DPSE) algorithm which combines the basic idea of velocity and position update rules from particle swarm optimization (PSO) and the concept of differential mutation from differential evolution (DE) in a new way. With the goal of optimizing within a limited number of function evaluations, the algorithm is tested and compared with the standard PSO and DE methods on 14 benchmark problems to illustrate that DPSE has the potential to achieve a faster convergence and a better solution. Simulation results show that, on the average, DPSE outperforms DE by 39.20% and PSO by 14.92% on the 14 benchmark problems. To show the feasibility of the proposed strategy on a real-world optimization problem, an application of DPSE to optimize the parameters of active disturbance rejection control (ADRC) in PUMA-560 robot is presented.
Repository Citation
Zheng, Q.; Simon, Daniel J.; Richter, Hanz; and Gao, Zhiqiang, "Differential Particle Swarm Evolution for Robot Control Tuning" (2014). Electrical and Computer Engineering Faculty Publications. 346.
https://engagedscholarship.csuohio.edu/enece_facpub/346
Original Citation
Q. Zheng, D. Simon, H. Richter and Z. Gao, "Differential particle swarm evolution for robot control tuning," in 2014 American Control Conference, 2014, pp. 5276-5281.
DOI
10.1109/ACC.2014.6858721
