Iterative Learning Control Algorithms Based on Complex Stochastic Distribution Systems
In this paper, a new generalized iterative learning algorithm is.rst proposed based on complex non-Gaussion stochastic control systems. Following designed neural networks are used to approximate the output PDF of the stochastic system in the repetitive processes or the batch processes, the tracking control to PDF is transformed into a parameter adaptive tuning problem in NN basis function. Under this framework, we study a new model free iterative learning control problem and propose a convex optimization algorithm based on a set of designed LMIs and L1 performance index. Such an algorithm has the advantage of the improvement of the closed-loop output PDF tracking performance and robustness. Simulation results are given to demonstrate the effectiveness of the proposed approach.
YI Yang SUN Changyin GUO Lei
Institute of Automation, Southeast University, Nanjing 210096, P.R.China College of Information Engi Institute of Automation, Southeast University, Nanjing 210096, P.R.China Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100083, P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-5
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）