School of Mechanical Engineering and Automation, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China.
School of Cyber Science and Technology, Beihang University, Beijing, 100191, China.
Neural Netw. 2023 Mar;160:175-191. doi: 10.1016/j.neunet.2023.01.005. Epub 2023 Jan 13.
Under the persistent excitation (PE) condition, the real dynamics of the nonlinear system can be obtained through the deterministic learning-based radial basis function neural network (RBFNN) control. However, in this scheme, the learning speed and accuracy are limited by the tradeoff between the PE levels and the approximation capabilities of the neural network (NN). Inspired by the frequency domain phase compensation of linear time-invariant (LTI) systems, this paper presents an adaptive phase compensator employing the pure time delay to improve the performance of the deterministic learning-based adaptive feedforward control with the reference input known a priori. When the adaptive phase compensation is applied to the hidden layer of the RBFNN, the nonlinear approximation capability of the RBFNN is effectively improved such that both the learning performance (learning speed and accuracy) and the control performance of the deterministic learning-based control scheme are improved. Theoretical analysis is conducted to prove the stability of the proposed learning control scheme for a class of systems which are affine in the control. Simulation studies demonstrate the effectiveness of the proposed phase compensation method.
在持续激励 (PE) 条件下，可以通过基于确定性学习的径向基函数神经网络 (RBFNN) 控制获得非线性系统的真实动态。然而，在该方案中，学习速度和准确性受到 PE 水平和神经网络 (NN) 逼近能力之间的权衡限制。受线性时不变 (LTI) 系统频域相位补偿的启发，本文提出了一种自适应相位补偿器，采用纯时间延迟来提高具有先验已知参考输入的基于确定性学习的自适应前馈控制的性能。当自适应相位补偿应用于 RBFNN 的隐藏层时，RBFNN 的非线性逼近能力得到有效提高，从而提高了基于确定性学习的控制方案的学习性能（学习速度和准确性）和控制性能。理论分析证明了所提出的针对控制仿射系统的学习控制方案的稳定性。仿真研究验证了所提出的相位补偿方法的有效性。
