斜拉桥地震响应线性二次型迭代学习控制(LQILC)研究
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摘要
最优控制方法是利用极值原理、最优滤波或动态规划等最优化方法来求解结构振动最优控制输入的一种设计方法。最优控制规律均是建立在系统理想数学模型基础上,而实际结构控制中往往采用降阶模型且存在多种约束条件,因此基于最优控制理论设计的控制器大都只能实现次最优控制。迭代学习控制理论的产生与发展,为结构振动主动控制提供了新的方法,但迭代学习控制的应用又受到其控制效果与其收敛性的制约。本文基于线性二次型最优控制与迭代学习控制相结合的思想,提出二次型最优迭代学习混合控制方法(LQILC),以二次型性能指标为控制目标,提高迭代的收敛速度;在性能指标的基础上进行迭代学习,改善了二次型最优控制的控制效果。以Emerson Memorial斜拉桥Benchmark模型为研究对象,采用二次型迭代学习控制策略(LQILC)对该桥的地震响应进行有效的控制,并得出Benchmark指标评价其对该桥的控制效果。
Optimal control is a method to solve the optimal vibration control input by using maximum principle,optimal filtering or dynamic programming optimization. Controller designed based on optimal control theory can only achieve sub-optimal control,because optimal control is built on the ideal mathematical model and the actual structure is often a reduced order model with a variety of constraints. The emergence and development of the iterative learning control theory provides a new method for structure vibration control. But its control effect and convergence restrict its application. With respective advantages of iterative learning control and quadratic optimal control,we combine them and obtain a new control strategy,which is named linear quadratic iterative learning control( LQILC). The new mixed control strategy enhances the stability and robustness of the iterative learning control system,improves the speed of convergence and the control effect of the quadratic optimal control. Using the linear quadratic iterative learning control to control the Emerson Memorial Bridge against earthquake and calculate the benchmark performance indicators. The result show that the new control strategies are able to effectively control the Emerson Memorial Bridge against earthquake and the control effect is improved.
Optimal control is a method to solve the optimal vibration control input by using maximum principle,optimal filtering or dynamic programming optimization. Controller designed based on optimal control theory can only achieve sub-optimal control,because optimal control is built on the ideal mathematical model and the actual structure is often a reduced order model with a variety of constraints. The emergence and development of the iterative learning control theory provides a new method for structure vibration control. But its control effect and convergence restrict its application. With respective advantages of iterative learning control and quadratic optimal control,we combine them and obtain a new control strategy,which is named linear quadratic iterative learning control( LQILC). The new mixed control strategy enhances the stability and robustness of the iterative learning control system,improves the speed of convergence and the control effect of the quadratic optimal control. Using the linear quadratic iterative learning control to control the Emerson Memorial Bridge against earthquake and calculate the benchmark performance indicators. The result show that the new control strategies are able to effectively control the Emerson Memorial Bridge against earthquake and the control effect is improved.
引文
[1]潘雪,叶永强,王建宏.分数线性相位超前迭代学习控制[J].控制理论与应用,2013,30(7):869-874.PAN Xue,YE Yongqiangy,WANG Jianhong.Fractional linear phase-lead iterative learning control[J].Control Theory&Applications,2013,30(7):869-874.(in Chinese)
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[10]Caicedo J M,Dyke S J,Moon S J,et al.PhaseⅡbenchmark control problem for seismic response of cable-stayed bridges,2002(http://wusceel.cive.wustl.edu/quake)
[11]Dyke S J,Turan G,Caicedo J M,et al.Benchmark control problem for seismic response of cable-stayed bridge,2000(http://wusceel.cive.wustl.edu/quake)
[2]Uchiyama M.Formation of high speed motion pattern of mechanical arm by trial[J].Trans of the Society of Instrumentation and Control Engineer,1978,(19):706-712.
[3]Arimoto S,Kawamura S,Miyazaki K.Bettering operation of robots by learning[J].Journal of Robotic Systems,1984,1(2):123-140.
[4]Arimoto S,Kawamura S,Miyazaki F,et al.Learning control theory for dynamic systems[C]∥Proceedings of the 24th IEEE Conference on Decision and Control,Ft.Lauderdale,Florida,1985,1375-1380.
[5]Heinzinger G,Fenwick D,Paden B,et al.Robust learning control[C]∥Proceedings of the 28th IEEE Conference on Decisionand Control,Tampa,1989,436-440.
[6]Arimoto S,Naniwa T,Suzuki H.Robustness of P-type learning control with a forgetting factor for robot motions[C]∥In:Proceedings of the 29th IEEE Conference on Decision and Control,Honoluu Hawaii,1990,2640-2645.
[7]Togai M,Yamano O.Analysis and design of an optimal learning control scheme for industrial robots:A discrete system approach[C]∥Proceedings of the 24th IEEE Conference on Decision and Control,Fort Landerdale,FL,1985,1399-1404.
[8]Furuta K,Yamakita M.The design of a learning control system for multivariable systems[C]∥Proceedings of the 1987 IEEE International Symposium on Intelligent Control,Philadelphia,Pennsylvania,USA,1987:371-376.
[9]Amann N,Owens D H,Rogers E.Iterative learning control using optimal feedback and feedforward actions[J].International Journal of Control,1996,65(2),277-293.
[10]Caicedo J M,Dyke S J,Moon S J,et al.PhaseⅡbenchmark control problem for seismic response of cable-stayed bridges,2002(http://wusceel.cive.wustl.edu/quake)
[11]Dyke S J,Turan G,Caicedo J M,et al.Benchmark control problem for seismic response of cable-stayed bridge,2000(http://wusceel.cive.wustl.edu/quake)
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