Guo Lei, Lin Hongyu, Jiang Jiale, Song Yuan, Gan Dongming
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
School of Engineering Technology, Purdue University, West Lafayette, IN, USA.
ISA Trans. 2024 Feb;145:479-492. doi: 10.1016/j.isatra.2023.11.032. Epub 2023 Nov 23.
In this paper, balance control of a bicycle robot is studied without either a trail or a mechanical regulator when the robot moves in an approximately rectilinear motion. Based on the principle of moment balance, an input nonaffine nonlinear dynamics model of the bicycle robot is established. A driving velocity condition is proposed to maintain the robot balance. The nonaffine nonlinear system is transformed into an affine nonlinear system by defining the equivalent control. Subsequently, a feedback linearization controller is designed for the equivalent control. We design a combined control algorithm of synchronous policy iteration based on the actor-critic architecture. The actor neural network (NN) is designed based on the feedback linearization control law. Weight tuning laws for the critic and actor NNs are proposed. The system closed-loop stability and convergence of the NN weights are guaranteed based on the Lyapunov analysis. The optimality of the equivalent control policy is guaranteed. To satisfy the driving velocity condition, the values of the steering angle and driving velocity are determined based on the optimal equivalent control. The effectiveness of the proposed algorithm is verified through simulations and real experiments.
本文研究了自行车机器人在近似直线运动时，无拖尾或机械调节器情况下的平衡控制。基于力矩平衡原理，建立了自行车机器人的输入非仿射非线性动力学模型。提出了一个驱动速度条件以维持机器人平衡。通过定义等效控制，将非仿射非线性系统转化为仿射非线性系统。随后，为等效控制设计了反馈线性化控制器。我们基于行为-评判架构设计了一种同步策略迭代的组合控制算法。行为神经网络（NN）基于反馈线性化控制律进行设计。提出了评判和行为神经网络的权重调整律。基于李雅普诺夫分析保证了系统闭环稳定性和神经网络权重的收敛性。保证了等效控制策略的最优性。为满足驱动速度条件，基于最优等效控制确定转向角和驱动速度的值。通过仿真和实际实验验证了所提算法的有效性。
