隐节点分布优化的自适应前馈神经网络控制
Adaptive Feedforward Neural Network Control with an Optimized Hidden Node Distribution
摘要：
具有格型分布隐层节点的复合自适应径向基函数神经网络（RBFNN）控制存在3个固有缺点：1）自适应RBFNN的逼近域难以先验确定; 2）只能保证部分激励持续性（PE）条件; 3）通常情况下，RBFNN所需的隐层节点数很大。本文提出了一种自适应前馈RBFNN控制器的隐节点的优化分布，以适当地解决上述缺点。由K-均值算法计算的隐藏节点的分布沿着期望的状态轨迹最优地分布。自适应RBFNN满足PE条件的周期性参考轨迹。所有隐藏节点的权重将收敛到最优值。该方法大大减少了隐藏节点的数量，同时实现了更好的逼近能力。该控制方案在两种特殊情况下具有与经典PID控制相似的合理性，因此可以看作是一种具有更好逼近能力的增强型PID控制方案。对于由数字器件实现的控制器，对于具有未知动力学的机械手，该方法比具有精确动力学的基于模型的方法具有更好的控制性能，仿真结果验证了该方法的有效性。这一结果为自适应神经网络控制和确定性学习理论的协调提供了更深入的见解。
英文摘要：
Composite adaptive radial basis function neural network (RBFNN) control with a lattice distribution of hidden nodes has three inherent demerits: 1) the approximation domain of adaptive RBFNNs is difficult to be determined a priori; 2) only a partial persistence of excitation (PE) condition can be guaranteed; and 3) in general, the required number of hidden nodes of RBFNNs is enormous. This paper proposes an adaptive feedforward RBFNN controller with an optimized distribution of hidden nodes to suitably address the above demerits. The distribution of the hidden nodes calculated by a K-means algorithm is optimally distributed along the desired state trajectory. The adaptive RBFNN satisfies the PE condition for the periodic reference trajectory. The weights of all hidden nodes will converge to the optimal values. This proposed method considerably reduces the number of hidden nodes, while achieving a better approximation ability. The proposed control scheme shares a similar rationality to that of the classical PID control in two special cases, which can thus be seen as an enhanced PID scheme with a better approximation ability. For the controller implemented by digital devices,the proposed method, for a manipulator with unknown dynamics, potentially achieves better control performance than model-based schemes with accurate dynamics.Simulation results demonstrate the effectiveness of the proposed scheme. This result provides a deeper insight into the coordination of the adaptive neural network control and the deterministic learning theory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
