The Iterative Learning Control for Saturated Nonlinear Industrial Control Systems with Delay
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摘要: 基于稳态优化中递阶控制结构,对具有滞后的非平滑饱和非线性工业控制系统施行迭 代学习控制,提出了期望目标轨线δ-可达以及迭代学习算法的ε-收敛的慨念,给出了加权超前 PD-型开环迭代学习算法,对算法的收敛性进行论证.数字仿真证明了算法的有效性,并表明对 工业控制系统的动态品质有显著改进.Abstract: In this paper, based on hierarchical control structure in steady-state optimization, the iterative learning control is applied to non-smooth and saturated nonlinear industrial control systems with delay, the definitions of δ-reachability of desired target trajectory and t-convergence of the iterative learning control algorithm are suggested, the weighted PD-type leading open-loop iterative learning control algorithm is discussed, and the convergence proof of the algorithm is also given. Numerical simulation shows that the algorithm is effective and the dynamic performance of industrial control system is remarkably improved.
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Key words:
- Iterative learning control /
- non-smooth saturation /
- nonlinearity /
- reachability /
- convergence
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