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针对一类输入时滞非线性系统提出了一种新的学习控制算法,即在任意初始状态条件下系统的输入和初态同时进行学习的闭环PD型迭代学习控制,其中输入利用给定超前法。给出了该算法谱半径形式的收敛条件,并利用算子理论证明了系统在任意初始状态条件下经过迭代后,其输出能够完全跟踪期望轨迹。该算法解决了闭环PD型迭代学习控制的初始状态问题,且放宽了收敛条件。仿真结果表明了该算法的有效性。
A new learning control algorithm is proposed for a class of nonlinear systems with input delay. The closed-loop PD-type iterative learning control, in which input and initial state of the system are under the condition of any initial state, . The convergence condition of spectral radius form of the algorithm is given, and the operator theory is used to prove that the output of the system can track the desired trajectory exactly after iteration under any initial condition. The algorithm solves the initial state problem of closed-loop PD-type iterative learning control and relaxes the convergence condition. Simulation results show the effectiveness of the algorithm.