Neural Networks Based Optimal Tracking Control of a Delta Robot With Unknown Dynamics
Neural Networks Based Optimal Tracking Control of a Delta Robot With Unknown Dynamics
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DOI：
10.1007/s12555-022-0745-9
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
A. Gholami;Jian-Qiao Sun;R. Ehsani
中科院分区：
文献类型：
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作者：
A. Gholami;Jian-Qiao Sun;R. Ehsani
针对未知一般非线性系统，提出了一种基于神经网络的数据驱动最优跟踪控制方案。首先，建立了一种新的神经网络结构来重构形式为f（x（t））+g（x（t））u（t）的未知系统动力学。设计了两个并行网络来逼近函数sf（x）和g（x）。然后，所获得的数据驱动模型被用来建立最优跟踪控制。所开发的控制包括两部分，前馈控制和最优反馈控制。最优反馈控制是通过神经网络逼近Hamilton-Jacobi-Bellman方程的解来实现的。与其他研究不同的是，Hamilton-Jacobi-Bellman解决方案是通过估计值函数导数使用神经网络。最后，所提出的控制方案进行了测试的三角洲机器人。两个轨迹跟踪的例子来验证所提出的最优控制方法的有效性。
This paper proposes a data-driven optimal tracking control scheme for unknown general nonlinear systems using neural networks. First, a new neural networks structure is established to reconstruct the unknown system dynamics of the formẋ(t) =f(x(t)) +g(x(t))u(t). Two networks in parallel are designed to approximate the functionsf(x) andg(x). Then the obtained data-driven models are used to build the optimal tracking control. The developed control consists of two parts, the feed-forward control and the optimal feedback control. The optimal feedback control is developed by approximating the solution of the Hamilton-Jacobi-Bellman equation with neural networks. Unlike other studies, the Hamilton-Jacobi-Bellman solution is found by estimating the value function derivative using neural networks. Finally, the proposed control scheme is tested on a delta robot. Two trajectory tracking examples are provided to verify the effectiveness of the proposed optimal control approach.