A New Discrete-time Adaptive ILC for Nonlinear Systems with Time-varying Parametric Uncertainties
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摘要: 利用离散时间轴与迭代轴之间的相似性, 提出了一种新的离散时间自适应迭代学习控制 (AILC) 方法来处理带有时变参数不确定性的非线性系统. 与自适应控制相类似, 所提出的 AILC 是基于投影算法的, 因此学习增益可以沿学习轴迭代地调节. 在随机初始状态和参考轨迹迭代变化的条件下, 所提出的 AILC 仍可沿迭代学习轴渐近地实现有限时间区间上的逐点收敛性.
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关键词:
- 迭代学习控制(ILC) /
- 自适应控制 /
- 时变参数 /
- 非相同初始条件 /
- 非相同参考轨迹
Abstract: Using the analogy between the discrete time axis and the iterative learning axis, a new discrete-time adaptive iterative learning control (AILC) approach is developed to address a class of nonlinear systems with time-varying parametric uncertainties. Analogous to adaptive control, the new AILC can incorporate a projection algorithm, thus the learning gain can be tuned iteratively along the learning axis. When the initial states are random and the reference trajectory is iteration-varying, the new AILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis.
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