Title :
A optimal control method with Hopfield neural networks
Author :
Zhang, Shao-bai ; Cheng, Xie-feng
Author_Institution :
Coll. of Comput., Nanjing Univ. of Posts & Telecommun., Nanjing, China
Abstract :
An optimal control method based on continuous-time, continuous-state Hopfield neural network (CTCSHNN) is proposed for multivariable time-varying systems. The equivalence is built theoretically between moving-horizon linear quadratic (LQ) performance index and energy function of CTCSHNN, and the CTCSHNN is constructed to solve the above LQ optimization control problems. Moreover, the rolling optimization strategy is adopted to form closed-loop control structure that includes CTCSHNN. So, the dynamic optimization control for multivariable time-varying systems is realized in infinite-horizon. As an example, a second order time-varying system is simulated. Simulation results show the effectiveness of proposed method.
Keywords :
Hopfield neural nets; closed loop systems; continuous time systems; dynamic programming; equivalence classes; infinite horizon; multivariable control systems; neurocontrollers; optimal control; time-varying systems; closed-loop control structure; continuous-time continuous-state Hopfield neural network; dynamic optimization control; energy function; equivalence; infinite horizon; moving-horizon linear quadratic performance index; multivariable time-varying systems; optimal control; rolling optimization strategy; Cost function; Hopfield neural networks; Neurons; Optimal control; Time varying systems; Hopfield neural network; infinite-horizon; multivariable time-varying systems;
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
DOI :
10.1109/ICNC.2010.5583580
