Earth pressure balance (EPB) shield tunneling machine has been widely used in underground construction. To avoid the catastrophic accidents caused by earth pressure imbalance, the earth pressure on excavation face must be controlled balance to that in chamber. To solve this problem better, a multi‐variable data‐driven optimal control method for shield machine based on dual‐heuristic programming (DHP) is proposed. The DHP controller is constructed with action network, model network, and critic network based on back‐propagation neural networks (BPNNs). Following Bellman's principle of optimality, a cost function of DHP controller for the chamber's earth pressure is presented, which simplifies a multi‐level optimization to a single‐level optimization. To minimize the cost function, the action network utilizes the critic network's error to achieve multi‐variable optimization, and the optimal control parameters for the tunneling process are obtained at last. The simulation results show that the method can effectively control the earth pressure balance. Even in case of disturbance, the system has strong anti‐interference ability and the control process is also quicker and steadier.

中文翻译：

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基于双启发式动态规划的盾构掘进机土压力平衡最优控制

土压平衡（EPB）盾构隧道掘进机已广泛用于地下建筑。为避免因土压力不平衡引起的灾难性事故，必须控制开挖面的土压力与室内的土压力平衡。为了更好地解决该问题，提出了一种基于双启发式编程（DHP）的盾构机多变量数据驱动最优控制方法。DHP控制器由基于反向传播神经网络（BPNN）的动作网络，模型网络和评论网络构成。遵循Bellman的最优原理，提出了DHP控制器对箱体土压力的成本函数，从而将多级优化简化为单级优化。为了最大程度地降低成本功能，行动网络利用了批评者网络的 误差以实现多变量优化，最后获得隧道过程的最优控制参数。仿真结果表明，该方法可以有效地控制土压力平衡。即使在出现干扰的情况下，该系统也具有强大的抗干扰能力，并且控制过程也更快，更稳定。