IEEE Trans Neural Netw Learn Syst. 2018 Jan;29(1):208-217. doi: 10.1109/TNNLS.2017.2768820.
In this paper, the problem of path following for underactuated snake robots is investigated by using approximate dynamic programming and neural networks (NNs). The lateral undulatory gait of a snake robot is stabilized in a virtual holonomic constraint manifold through a partial feedback linearizing control law. Based on a dynamic compensator and Line-of-Sight guidance law, the path-following problem is transformed to a regulation problem of a nonlinear system with uncertainties. Subsequently, it is solved by an infinite horizon optimal control scheme using a single critic NN. A novel fluctuating learning algorithm is derived to approximate the associated cost function online and relax the initial stabilizing control requirement. The approximate optimal control input is derived by solving a modified Hamilton-Jacobi-Bellman equation. The conventional persistence of excitation condition is relaxed by using experience replay technique. The proposed control scheme ensures that all states of the snake robot are uniformly ultimate bounded which is analyzed by using the Lyapunov approach, and the tracking error asymptotically converges to a residual set. Simulation results are presented to verify the effectiveness of the proposed method.
本文采用近似动态规划和神经网络（NNs）研究了欠驱动蛇形机器人的路径跟踪问题。通过部分反馈线性化控制律，在虚拟完整约束流形中稳定蛇形机器人的横向波动步态。基于动态补偿器和视线制导律，将路径跟踪问题转化为具有不确定性的非线性系统的调节问题。随后，使用单个批评者 NN 通过无限 horizon 最优控制方案解决。导出了一种新颖的波动学习算法，用于在线近似相关成本函数，并放宽初始稳定控制要求。通过求解修正的 Hamilton-Jacobi-Bellman 方程推导出近似最优控制输入。通过使用经验回放技术放宽传统的持续激励条件。所提出的控制方案确保蛇形机器人的所有状态都是一致的有界的，这是通过 Lyapunov 方法进行分析的，并且跟踪误差渐近收敛于残差集。仿真结果验证了所提出方法的有效性。
