Iterative learning control (ILC) can synthesize the feedforward control signal for the trajectory tracking control of a repetitive task, even when the system has strong nonlinear dynamics. This makes ILC be one of the most popular methods for trajectory tracking control. Restriction on a repetitive task, however, limits its application to multiple trajectories. This article proposes a neural-network-based ILC (NN-ILC) to deal with nonrepetitive tasks very effectively. A position-based ILC is designed to compensate the tracking error, based on which the multiple outputs of the ILC (ILC outputs) for multiple tasks are expressed as a function of the reference position, velocity, and acceleration. The proposed NN-ILC divides the ILC outputs of multiple tasks into two parts: the linear and nonlinear portions. The first part is expressed by a linear function, which is the linear portion of the function of the ILC outputs. The second part is expressed by a nonlinear function, which is estimated by complementary neural networks including a general neural network and a switching neural network. Finally, the two parts are combined and the ILC outputs of multiple tasks are expressed as a neural-network-based function. Two advantages of the proposed NN-ILC are emphasized. First, the ILC outputs of multiple tasks are compressed into a function by the proposed method, and thus, the memories can be saved. Second, in terms of generalizability, the neural-network-based function of the ILC outputs can easily predict position compensation for multiple tasks without extra iterative learning processes. Experimental results on a robot arm show that the proposed NN-ILC method can easily realize the ILC of multiple tasks. It can save memory comparing with the method of storing the data of multiple tasks and can predict the ILC output of any task, which can accelerate the iterative learning process.

中文翻译：

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基于神经网络的多任务迭代学习控制

迭代学习控制 (ILC) 可以合成前馈控制信号，用于重复性任务的轨迹跟踪控制，即使系统具有很强的非线性动力学。这使得 ILC 成为最流行的轨迹跟踪控制方法之一。然而，对重复性任务的限制将其应用限制在多个轨迹上。本文提出了一种基于神经网络的 ILC（NN-ILC）来非常有效地处理非重复性任务。基于位置的 ILC 旨在补偿跟踪误差，在此基础上将多个任务的 ILC 输出（ILC 输出）表示为参考位置、速度和加速度的函数。提出的 NN-ILC 将多个任务的 ILC 输出分为两部分：线性部分和非线性部分。第一部分由线性函数表示，它是 ILC 输出函数的线性部分。第二部分用非线性函数表示，由包括通用神经网络和切换神经网络在内的互补神经网络估计。最后，将两部分结合起来，将多个任务的 ILC 输出表示为基于神经网络的函数。强调了所提出的 NN-ILC 的两个优点。首先，通过所提出的方法将多个任务的 ILC 输出压缩成一个函数，从而可以节省内存。其次，就通用性而言，ILC 输出的基于神经网络的函数可以轻松预测多个任务的位置补偿，而无需额外的迭代学习过程。在机器人手臂上的实验结果表明，所提出的 NN-ILC 方法可以轻松实现多任务的 ILC。与存储多个任务的数据的方法相比，它可以节省内存，并且可以预测任何任务的ILC输出，从而可以加速迭代学习过程。