摘要
结构振动控制的发展很多都局限于对于线性结构的研究,但是结构在强震作用下将不可避免地进入塑性阶段,从而表现出非线性行为,因此研究结构非线性振动控制具有重要的理论与实际意义。本文着重研究了结构非线性振动控制的智能控制算法与模型试验,主要研究内容如下:
1.针对设置层间控制器的结构非线性振动控制问题,结合滑模控制和自适应模糊控制算法各自的优点,提出了结构非线性振动的自适应模糊滑模控制算法,并采用模糊系统来消除滑模控制中的抖振现象,根据Lyapunov稳定定理设计出了相应的模糊控制器和自适应律。为了解决全状态观测问题,利用神经网络的非线性逼近能力,基于结构的非线性振动模型设计了动态神经网络观测器。通过在Benchmark结构非线性振动模型上的仿真验证了本文提出的智能算法的有效性,并进行了鲁棒性分析,结果表明:自适应模糊滑模控制可以很好地控制结构非线性振动,且具有比线性控制算法更好的鲁棒性;同时动态神经网络观测器也能够较好地观测结构非线性振动的全部状态,从而能够实现基于自适应模糊滑模控制算法的结构非线性振动输出控制。
2.分析了本文提出的自适应模糊滑模主动控制力的特点,得出其适合用半主动控制算法实现的结论。通过选择合适的半主动控制算法,利用磁流变阻尼器来跟踪和实现此主动控制力。在Benchmark结构非线性振动模型上进行了磁流变半主动控制仿真分析,并对主动控制力、半主动控制力的特点和控制效果以及结构半主动控制系统耗能进行了分析。同时采用遗传算法对磁流变阻尼器的位置进行了优化,根据半主动控制算法的特点,提出了两阶段的优化算法,并给出了20层Benchmark模型中磁流变阻尼器的最优布置。同时针对作动器故障问题,采用动态神经网络辨识出现作动器故障的结构非线性振动模型,并基于此动态神经网络模型提出了结构非线性振动的容错控制算法。
3.针对设置AMD的结构非线性振动控制问题,根据AMD控制的特点,提出了相应的模糊控制算法。为了解决AMD控制高层结构时模糊输入过多的问题,采用基于二次型指标的广义模糊输入,通过仿真验证了本文所提出的控制算法的合理性和有效性。结合仿真结果对AMD控制的目的及本质进行了较深入的分析,从而对AMD控制结构非线性振动提出了一些合理性的建议。最后,为了改善AMD对结构非线性振动控制可能放大层间响应的问题,采用AMD控制和层间阻尼器相结合的联合控制方式得到了很好的控制效果。
4.利用压电材料对考虑几何非线性及模型不确定性的非线性梁进行了振动控制的试验研究,对于梁在大变形时产生的几何非线性进行了理论及试验研究和分析,并通过在梁端附加质量来模拟此结构的质量不确定性,然后对于不同的质量不确定性,通过试验比较了一般的模糊控制与自适应模糊滑模控制的控制效果,试验结果也验证了自适应模糊滑模控制对于非线性及模型不确定性有较好的控制作用。
5.为了解决结构非线性控制试验模型的可重复性问题,提出并实现了利用旋转式磁流变阻尼器模拟结构塑性铰的方法,建立了相应的结构非线性振动的试验模型。通过控制此旋转式磁流变阻尼器的输入来实现不同的非线性行为,在各种不同的非线性行为下利用磁流变阻尼器实现了对于此试验模型的半主动控制,进而在此试验模型上验证了动态神经网络观测器及自适应模糊滑模控制算法的有效性。试验结果也表明本文提出的智能控制算法能够较好地实现对结构非线性振动的控制。
The current development of structural control has often been restricted to linear structures. However due to the inelastic deformation under intense ground shaking, it has theoretical and practical value to study the control of structural nonlinear vibration. The application of intelligent control algorithm in structural nonlinear vibration control is emphasized in this dissertation. The main research works are outlined as following:
1.Aimed at controlling structural nonlinear vibration using interstory controller, an adaptive fuzzy sliding mode (AFSM) control algorithm is proposed to control structural nonlinear vibration, combining the advantage of sliding mode control and adaptive fuzzy control. Furthermore, the chattering phenomenon of sliding mode control is attenuated by an adaptive fuzzy system. The adaptation law is derived from Lyapunov direct method. To obtain all states of the structure, a dynamical neural network (DNN) observer is designed taking the advantage of dynamical neural network to approximate the arbitrary dynamic system. Numerical simulation is conducted on the nonlinear Benchmark structure. Robust analysis is also performed. The results show that AFSM control is suitable and robust for the control of structural nonlinear vibration. The dynamical neural network can also estimate the total states of structural nonlinear vibration, which leads to the output control of structural nonlinear vibration using the intelligent control algorithm.
2.An active control force based on the AFSM control algorithm is analyzed and proved to be suitable for semi-active control device. Magnetorheological (MR) damper is used to trace the active control force using the developed semi-active control algorithm. Simulation of MR damper control using AFSM control is conducted on the nonlinear Benchmark structure. The control force is applied to each floor through an MR damper. The characteristic of active control force, semi-active control force and control effect are analyzed. The energy analysis is also performed. Furthermore, the placement of MR damper is optimized using a genetic algorithm. Based on the characteristic of semi-active control a two-phase optimization progress using genetic algorithm is developed and verified via numerical simulations. The optimal placement of MR damper for the control of structural nonlinear vibration is presented. On the other hand, considering fault tolerance control for structural nonlinear vibration, the dynamical neural network is used to identify the nonlinear structure with faults. Based on this dynamical neural network model, the corresponding fault tolerance controller is proposed to control structural nonlinear vibration and demonstrated by numerical simulations.
3.To utilize active mass damper (AMD) to control the structural nonlinear vibration, a specified fuzzy controller is proposed and analyzed. To overcome the difficulty for excessive inputs to fuzzy controller, the generalized fuzzy input based on linear-quadratic cost is adopted and verified by the numerical simulation. The in-depth analysis of AMD control is performed considering the analysis of simulation results. The reasonable proposals for AMD control of structural nonlinear vibration are presented. Furthermore, the innovative AMD-ID (interstory damper) controller is developed to control structural nonlinear vibration. Numerical results verify the effectiveness of proposed controller.
4.The experimental study of vibration control of a nonlinear beam with piezoelectric material considering geometric nonlinearity and model uncertainty is conducted. The geometric nonlinearity is theoretically and experimentally analyzed for large deflection of beam vibration. Additional masses are added to the tip of beam to realize the mass uncertainty. The general fuzzy control and adaptive fuzzy sliding mode control are compared using experimental data based on mass uncertainty. The experimental results show that adaptive fuzzy sliding mode controller can perform very well with nonlinearity and model uncertainty.
5.To meet the demand on the cost and repeatability of test model of nonlinear structure, the MR rotary brake is proposed and used to mimic the plastic hinge to establish a nonlinear vibration model. The hysteretic loops of different nonlinear behaviors are realized through the control of voltage input to the MR rotary brake. Moreover, the MR damper is incorporated into the test model to implement the semi-active control. The performance of DNN observer and AFSM controller are verified. As a result, the test data demonstrates that the intelligent algorithms proposed by this dissertation are effective for the control of structural nonlinear vibration.
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