由于盾构掘进姿态对隧道成型和掘进效率影响较大，而实际影响掘进姿态各要因的强耦合和非线性存在复杂难辨性，常规的调参方法稳态效果不佳。为优化盾构挖掘过程中的姿态轨迹纠偏，提出一种基于自抗扰控制和Q学习优化的复合控制方法。首先，将盾构油缸调压分区数学模型化，设计出线性自抗扰控制器；然后，在自抗扰控制框架基础上，利用Q学习算法实现控制器参数的自适应整定；最后，通过仿真模型验证所提方法的有效性，为编写设备控制程序提供技术支撑。相比于传统PID控制和自抗扰控制，所提方法可实现自适应参数调试，提高盾构纠偏姿态的控制性能。
To optimize attitude trajectory correction during
shield excavation, a composite control method based on selfdisturbance
rejection control and Qlearning optimization is
proposed. This is because the shieldtunneling posture
considerably affects tunnel formation and excavation efficiency,strong coupling
and nonlinearity that affect the excavation posture in practice are complex and
difficult to distinguish, and steadystate effect of
conventional parameter adjustment methods is insufficient. The proposed control
method involves the mathematical modeling of the pressure regulation zones of
the shield oil cylinder and designing of a linear selfdisturbance
rejection controller. Based on the selfdisturbance
rejection control framework, the Qlearning algorithm
is used to achieve adaptive tuning of controller parameters. The effectiveness
of the proposed method is validated through model simulations, providing
technical insights for developing device control programs. Compared with the
traditional proportionalintegralderivative and selfdisturbance rejection
controls, the proposed method achieves adaptive parameter debugging and
improves the control performance of the deviation correction attitude of
shield.
