This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The ...This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.展开更多
研究一类未知异构非线性多智能体的编队控制问题.首先,利用全格式动态线性化(full form dynamic linearization, FFDL)方法将未知非线性智能体转化为含有时变参数的数据模型,并给出时变参数的估计方法;然后,基于该数据模型设计一种分布...研究一类未知异构非线性多智能体的编队控制问题.首先,利用全格式动态线性化(full form dynamic linearization, FFDL)方法将未知非线性智能体转化为含有时变参数的数据模型,并给出时变参数的估计方法;然后,基于该数据模型设计一种分布式无模型自适应多智能体编队控制方案;最后,为验证所提出的无模型自适应编队控制方案的有效性,利用3台NAO机器人开发基于Python的多智能体编队控制实验平台.实验比较结果表明,通过所提出的控制方案可使3台机器人仅利用局部信息就能有效完成编队控制任务,控制性能优于基于PID的编队控制方法.展开更多
基金supported by National Natural Science Foundation of China(Nos.60834001,60974040,61120106009)the Fundamental Research Funds for the Central Universities(No.2011JBM201)
基金supported by National Science Foundation of China(Nos.60974040,60834001,61120106009)the Research Award Foundation for the Excellent Youth Scientists of Shandong Province of China(No.BS2011DX010)the Fundamental Research Funds for the Central Universities(No.2011JBM201)
基金supported by National Natural Science Foundation of China(Nos.61374102,61120106009,60974040)the Singapore NationalResearch Foundation(NRF)through the Singapore-MIT Alliance for Research and Technology(SMART)Center for Future Urban Mobility(FM)
基金Supported by the National Natural Science Foundation of China （60974040, 61120106009）, the Research Award Foundation for the Excellent Youth Scientists of Shandong Province of China （BS2011DX010）, and the High School Science ＆ Technol- ogy Fund Planning Project of Shandong Province of China （J 10LG32）.
文摘This work presents an anticipatory terminal iterative learning control scheme for a class of batch processes, where only the final system output is measurable and the control input is constant in each operations. The proposed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the saturation bound. The tracking error convergence is established with rigorous mathematical analysis. Simulation results are provided to show the effectiveness of the proposed approach.
文摘研究一类未知异构非线性多智能体的编队控制问题.首先,利用全格式动态线性化(full form dynamic linearization, FFDL)方法将未知非线性智能体转化为含有时变参数的数据模型,并给出时变参数的估计方法;然后,基于该数据模型设计一种分布式无模型自适应多智能体编队控制方案;最后,为验证所提出的无模型自适应编队控制方案的有效性,利用3台NAO机器人开发基于Python的多智能体编队控制实验平台.实验比较结果表明,通过所提出的控制方案可使3台机器人仅利用局部信息就能有效完成编队控制任务,控制性能优于基于PID的编队控制方法.
