【作者】 郑健；

【导师】 李树荣；

【作者基本信息】 中国石油大学 ， 控制理论与控制工程， 2008， 硕士

【摘要】 严格来说,实际控制系统都是非线性的,并具有不确定性,如参数摄动、未建模动态或外部干扰等。因此,系统的期望性能可能会被破坏。此外,时滞的存在使非线性系统的控制分析和设计问题变得更加复杂和困难。所以,研究带有不确定性的非线性时滞系统具有重要的理论和实践意义。对于非线性时滞系统的稳定性分析和控制器设计,一种有效的方法是基于Lyapunov-Krasovskii泛函法。本论文为补偿未知时滞项的影响,用Lyapunov-Krasovskii泛函构造候选Lyapunov函数。针对一类带有不确定性的状态滞后的非线性系统,设计了能够保证闭环系统稳定的鲁棒自适应控制器。在控制器设计过程中应用Lyapunov稳定性理论来证明闭环系统的稳定性。针对一类带有未知时滞项、未知参数和未知非线性函数的严格反馈非线性系统,通过结合反步设计方法和鲁棒控制策略,设计了一种鲁棒自适应控制器。通过引入一种合适的偶函数,解决了控制器的奇异性问题。证明了所提出的设计方法能保证闭环系统的所有信号是全局一致最终有界的,并且跟踪误差信号将收敛于原点的一个小邻域内。仿真实例说明了控制器的有效性。针对一类带有未知非线性函数的严格反馈非线性时滞系统,设计了一种自适应神经网络控制器。本论文选择径向基函数神经网络逼近未知的非线性函数。所提出的控制方案能保证闭环系统的所有信号是全局一致最终有界的。证明了跟踪误差信号将收敛于一个小紧集内。仿真实例验证了所提出方法的有效性。针对同一个二阶系统,在相同条件下对鲁棒自适应控制器和基于径向基函数神经网络的自适应控制器进行比较。仿真实例表明基于径向基函数神经网络设计的自适应控制器具有较好的控制特性。更多还原

【Abstract】 Strictly speaking, practical control systems are nonlinear, and always have uncertainties such as parameters perturbation, unmodeled dynamics or external disturbances, etc. So the system specified performance may be damaged. In addition, the existence of time delays may make the control analysis and design of nonlinear systems more complex and difficult. Therefore, the study of nonlinear time-delay systems with uncertainties has an important theoretical and practical significance.For the stability analysis and controller design of nonlinear time-delay systems, an effective method is based on the Lyapunov-Krasovskii functionals. In this thesis, Lyapunov-Krasovskii functionals were used to construct the Lyapunov function candidates such that the uncertainties from unknown time delays were compensated for. Robust adaptive controllers were designed for a class of uncertain nonlinear systems with delays in the state, which can guarantee the stability of the closed-loop systems. To prove the stability of the closed-loop systems, Lyapunov’s stability theory was applied in the controller design.By combining backstepping design procedure with robust control strategy, a robust adaptive controller was proposed for a class of strict-feedback nonlinear systems with both unknown time delays and uncertainties from unknown parameters and nonlinear functions. Controller singularity problems were avoided by introducing an appropriate even function. It was proven that the proposed design procedure was able to guarantee global uniform ultimate boundedness of all the signals in the closed-loop systems. The tracking error was shown to converge to a small neighborhood of the origin. A simulation example was provided to illustrate the validity of the proposed controller.An adaptive neural network controller was presented for a class of strict-feedback nonlinear time-delay systems with unknown nonlinear functions. A radial basis function neural network was chosen to approximate the unknown nonlinear functions in this thesis. The developed control scheme was able to guarantee global uniform ultimate boundedness of all the signals in the closed-loop systems. The tracking error was proven to converge to a small compact set. A simulation example was provided to illustrate the effectiveness of the proposed approach.For the same second-order system, a robust adaptive controller was compared with an adaptive controller based on radial basis function neural network under the same conditions. Simulation results were provided to show the proposed neural network adaptive controller has well control behavior.更多还原