具有输出约束的非线性系统自适应输出反馈控制研究

发布时间：2018-06-06 15:51

本文选题：自适应控制 + 动态面控制　；参考：《扬州大学》2017年硕士论文

【摘要】：在许多实际非线性系统中,未建模动态的存在严重降低了闭环系统的性能,甚至会造成系统的不稳定.实现对未建模动态影响的抵消和抑制是提高系统控制性能的关键.此外,为了确保系统的高性能和安全性,实际控制系统通常会对其输出和状态变量进行约束.在实际操作中,如果违反约束条件,将会导致系统性能下降甚至是系统损毁.目前对无约束系统的研究已经非常成熟,但各种约束的存在使得无约束系统控制方法不能很好地应用到有约束系统中,这就使得对有约束系统的深入研究变得非常必要.本文针对几类具有未建模动态和输出约束的非线性系统,分别基于障碍Lyapunov函数(BLF)、积分型障碍Lyapunov函数(iBLF)和非线性映射(NM),提出了四种自适应动态面控制方案.现将论文的主要内容总结如下:第一,基于未建模子系统是指数输入状态实际稳定的假设,针对一类具有状态未建模动态和对称输出约束的非线性系统,通过引入BLF,提出了一种自适应动态面输出反馈控制方案.利用径向基函数神经网络(RBF NNs)逼近未知非线性函数.利用K滤波器将复杂的非线性系统转变为低阶子系统.引入一个可量测的动态信号来抵消未建模动态对系统的影响.通过保证BLF有界,达到限制系统输出的目的.通过理论分析证明了闭环系统的所有信号是半全局一致终结有界的且系统输出满足约束条件.最后,利用一个数值仿真算例进一步验证了所提控制方案的有效性.第二,针对一类具有状态未建模动态和对称输出约束的非线性系统,通过引入iBLF,提出了一种自适应动态面输出反馈控制方案.为了降低K滤波器的阶数和相应向量参数的维数,避免直接利用RBF NNs估计系统未知非线性函数.通过直接对系统的输出进行约束,放宽了确保约束满足的初始条件.为了降低控制器设计的复杂性,采用动态面控制方法进行控制器设计,同时通过构造适当的未知连续函数,在稳定性分析中,有效地处理了由神经网络逼近和未建模动态所产生的误差项.通过理论分析证明了闭环系统的所有信号是半全局一致终结有界的且系统输出满足约束条件.最后,利用两个数值仿真算例进一步验证了所提控制方案的有效性.第三,基于未建模子系统是全局指数稳定的假设,针对一类具有状态未建模动态和非对称输出约束的非线性系统,通过引入NM,提出了一种自适应动态面输出反馈控制方案.基于假设未建模子系统是全局指数稳定的,利用Lyapunov函数描述未建模动态.通过引入K滤波器和NM,将具有输出约束的复杂非线性系统转换为无约束的低阶系统.通过理论分析证明闭环系统的所有信号是半全局一.致终结有界的且系统输出满足约束条件.最后,利用两个数值仿真算例进一步了验证所提控制方案的有效性.第四,将NM方法进一步应用到一类具有输出约束的严格反馈非线性系统中,并同时考虑了输入未建模动态对系统的影响.通过理论分析证明闭环系统的所有信号是半全局一致终结有界的且系统输出满足约束条件.最后,利用两个数值仿真算例进一步了验证所提控制方案的有效性.
[Abstract]:In many practical nonlinear systems, the existence of unmodeled dynamics seriously reduces the performance of the closed loop system and even causes the instability of the system. It is the key to improve the performance of the system to achieve the cancellation and suppression of the unmodeled dynamics. In addition, in order to ensure the high energy and security of the system, the actual control system will usually do it The output and the state variables are constrained. In the actual operation, if the constraints are violated, the system performance will be reduced or even the system damage. At present, the research on the unconstrained system is very mature, but the existence of various constraints makes the unconstrained system control methods not well applied to the constrained system, which makes the system a In this paper, four nonlinear systems with unmodeled dynamics and output constraints are proposed, which are based on the obstacle Lyapunov function (BLF), the integral barrier Lyapunov function (iBLF) and the nonlinear mapping (NM), respectively. The main contents of the paper are summarized. Firstly, based on the assumption that the unbuilt model system is the actual stability of the exponential input state, an adaptive dynamic surface output feedback control scheme is proposed for a class of nonlinear systems with state unmodeled dynamics and symmetric output constraints. A new adaptive dynamic surface output feedback control scheme is proposed by using the radial basis function neural network (RBF NNs) to approximate the unknown nonlinearity. A K filter is used to transform the complex nonlinear system into a lower order subsystem. A measurable dynamic signal is introduced to counteract the influence of the unmodeled dynamics to the system. By guaranteeing the bounds of the BLF, the purpose of limiting the output of the system is achieved. And the system output satisfies the constraints. Finally, a numerical simulation example is used to further verify the effectiveness of the proposed control scheme. Second, for a class of nonlinear systems with state unmodeled dynamics and symmetric output constraints, an adaptive dynamic surface output feedback control scheme is proposed by introducing iBLF. In order to reduce the K filter. The order of the wave device and the dimension of the corresponding vector parameters avoid directly using the RBF NNs to estimate the unknown nonlinear function of the system. By restricting the output of the system directly, the initial conditions are relaxed to ensure the constraint satisfaction. In order to reduce the complexity of the controller design, the dynamic surface control method is used to design the controller, and the structure is constructed at the same time. In the stability analysis, an appropriate unknown continuous function is used to effectively deal with the error term produced by the neural network approximation and the unmodeled dynamics. Through theoretical analysis, it is proved that all the signals of the closed loop system are semi globally uniformly terminated and bounded and the system output satisfies the constraints. Finally, the two numerical simulation examples are further verified. The effectiveness of the proposed control scheme is demonstrated. Third, based on the assumption that the unbuilt model system is a global exponential stability, a adaptive dynamic surface output feedback control scheme is proposed for a class of nonlinear systems with state unmodeled dynamic and asymmetric output constraints. Based on the assumption that the unbuilt model system is a global index, it is assumed that the unbuilt model system is a global index. Third Stable, using the Lyapunov function to describe the unmodeled dynamics. By introducing the K filter and NM, the complex nonlinear systems with output constraints are converted into unconstrained lower order systems. Through theoretical analysis, all the signals of the closed loop system are semi global one. The end bounded and the system output satisfies the constraints. Finally, two uses are used. The numerical simulation example further proves the effectiveness of the proposed control scheme. Fourth, the NM method is further applied to a class of strictly feedback nonlinear systems with output constraints, and the influence of the input unmodeled dynamics on the system is considered. Finally, two numerical examples are used to verify the effectiveness of the proposed control scheme.
【学位授予单位】：扬州大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP273

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