受限非线性参数化系统的自适应迭代学习控制

发布时间：2017-12-28 19:32

本文关键词：受限非线性参数化系统的自适应迭代学习控制　出处：《北京交通大学》2016年博士论文　论文类型：学位论文

【摘要】：本文以非线性连续时间系统为研究对象,着重研究针对一些受限问题的迭代学习控制方法,特别考虑了具有输入饱和与未知时变时滞的非线性参数化系统的自适应迭代学习控制,同时也考虑了具有非参数不确定性和输入饱和的多入多出·系统的迭代学习控制问题。论文的主要研究内容和创新点可以总结如下：一、研究了一类实际中广泛存在的具有输入饱和项与未知时变时滞项的非线性参数化系统的自适应迭代学习控制问题。首先,针对只具有输入饱和项的系统,设计了半饱和的迭代学习控制器和全饱和的自适应参数更新律；其次,针对同时具有输入饱和项与未知时变时滞项的系统,利用参数分离技术,将系统状态方程中局部Lipschitz连续函数的未知因素、时变参数以及未知时变时滞等不确定性项分离出来合并成一个未知时变参数,在此基础上,设计了半饱和的控制器和全饱和的参数更新律；最后,通过构造加权Lyapunov-Krasovskii-Like复合能量函数,证明了上述两种情况下,闭环信号的有界性和跟踪误差沿迭代轴的渐进收敛性。理论分析和仿真数例都验证了所提出的控制器以及参数更新律能很好的处理由输入饱和项与时滞项带来的非线性。二、研究了一类在重复环境下运行的同类多智能体系统的一致性问题。首先,针对具有输入饱和的多智能体系统,每个智能体的动态方程由含有输入饱和项的非线性参数化方程来描述,设计了分布式自适应迭代学习控制策略,并严谨的证明了所提出的控制策略能保证跟随者沿迭代轴渐进跟踪虚拟领导者；其次,针对同时具有输入饱和项和未知时变时滞项的多智能体系统,设计了分布式自适应迭代学习控制器,并通过构造加权Lyapunov-Krasovskii-Like复合能量函数,证明了当迭代次数趋于无穷时,跟随者能完全跟踪虚拟领导者。三、针对一类具有非参数不确定性与输入饱和的非线性多入多出系统,在重置条件下,设计了迭代学习控制器,并证明了所提出的控制器能很好的处理由非参数不确定性和输入饱和所引起的非线性,同时能放宽对相同初始条件的限制。
[Abstract]:Based on the nonlinear continuous time system as the research object, focuses on the study of some problems for constrained iterative learning control method, especially considering the adaptive iteration of nonlinear systems with input saturation and unknown time-varying delay learning control, is also considered with parameter uncertainties and input multiple input saturation more system the iterative learning control problem. The main contents and innovations of the paper can be summarized as follows: first, the adaptive iterative learning control problem of a class of nonlinear parameterized systems with input saturation term and unknown time-varying delay terms is studied. First of all, for only with input saturation system, the design of iterative learning controller and the half saturation saturation adaptive parameter update law; secondly, aiming at the system with input saturation and unknown time-varying delay, using the parameter separation technique, the unknown factors, continuous function of local Lipschitz system in the state equation variable parameters and unknown time-varying delay uncertainties such as a separate merged into an unknown time-varying parameter, on this basis, design the controller semi saturated and fully saturated parameter update law; finally, by constructing a weighted Lyapunov-Krasovskii-Like composite energy function, prove that the above two kinds of circumstances, the closed-loop signals are bounded the tracking error and the gradual convergence of the iterative axis. Theoretical analysis and simulation examples verify that the proposed controller and parameter updating law can well handle the nonlinearity caused by input saturation term and delay term. Two, the consistency of a class of similar multi-agent systems running in a repeated environment is studied. First of all, based on multi-agent systems with input saturation, dynamic equation of each agent is described by the equation of nonlinear parameters with input saturation, design a distributed adaptive iterative learning control strategy, and rigorous proved that the proposed control strategy can ensure the follower shaft along the iterative asymptotic tracking of the virtual leader; secondly at the same time, the multi agent system with input saturation and unknown time-varying delay, the design of the distributed adaptive iterative learning controller, and by constructing a weighted Lyapunov-Krasovskii-Like composite energy function, prove that when the number of iterations tends to infinity, the follower can track the virtual leader. Three, for a class of non parametric uncertainty and input saturation nonlinear MIMO system, the reset condition, the design of iterative learning controller, and it is proved that the proposed controller can non line caused by the very good processing by non parametric uncertainty and input saturation, and can relax on the same initial conditions.
【学位授予单位】：北京交通大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：TP13

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