TY - JOUR
T1 - Differential Particle Swarm Evolution for Robot Control Tuning
AU - Zheng, Q.
AU - Simon, Daniel J.
AU - Richter, Hanz
AU - Gao, Zhiqiang
N1 - 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.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - 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.
AB - 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.
KW - Control applications
KW - Evolutionary computing
KW - Optimization
UR - https://engagedscholarship.csuohio.edu/enece_facpub/346
UR - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6858721abstractAccess=nouserType=inst
U2 - 10.1109/ACC.2014.6858721
DO - 10.1109/ACC.2014.6858721
M3 - Article
JO - American Control Conference
JF - American Control Conference
ER -