IEEE Trans Cybern. 2016 Dec;46(12):3439-3452. doi: 10.1109/TCYB.2015.2509863.
The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position. The key aspect of the derivation of the controller is the definition of an output function that depends on the position and velocity errors. The internal and external dynamics are rigorously analyzed, thereby proving the uniform ultimate boundedness of the error trajectories. By using real-time experiments, the new scheme is compared with other control methodologies, therein demonstrating the improved performance of the proposed adaptive algorithm.
本文旨在为 Furuta 摆（一个两自由度欠驱动系统）介绍一种新颖的基于自适应神经网络的控制方案。还提供了输入和输出权值的自适应律。所提出的控制器能够保证在摆保持垂直位置的同时跟踪臂的参考信号。控制器的推导的关键方面是定义一个依赖于位置和速度误差的输出函数。严格分析了内部和外部动力学，从而证明了误差轨迹的一致有界性。通过实时实验，将新方案与其他控制方法进行了比较，证明了所提出的自适应算法的性能得到了提高。
