在海洋工程试验水池中，进行模型试验时，造波机产生的波浪从池壁和结构模型上反射后生成反射波，传播到造波机处再次反射产生二次反射波，二次反射波干扰了水池的波浪场，从而降低了试验的精度，这对试验是不利的。为消除二次反射波常采用主动吸收系统，其中，力反馈式主动吸收系统考虑造波板上作用力，以力与造波板冲程之比为吸收传递函数，实时控制系统的输入与输出，使得生成的波逐渐接近理想波浪。为了使得造波板冲程和力保持最优平衡关系，本文基于理论传递函数，设计了IIR数字滤波器进行滤波，实现主动吸收控制。对于设计IIR数字滤波器，粒子群优化算法是一种非常有效的设计方法，该算法设计简单，运算速度快，但是该算法在优化过程中可能会局限于局部最优，而无法得到全局最优解。本文在更新粒子速度时，采用了一种新型的方法——即基于惯性权重改进学习因子，惯性权重与学习因子呈非线性函数关系，对比惯性权重线性递减、非线性递减的改进方法。仿真实验结果表明，该方法收敛速度较快，迭代次数较少，求解效率较高，优化结果最优。 In the ocean engineering test basin, during the model test, the wave generated by the wave-makers generates reflected waves after being reflected from the basin wall and structural model, which propagates to the wave-making plates and reflects again to generate the secondary reflected wave. The secondary reflected wave interferes with the wave field of the basin, thus reducing the accuracy of the test, which is unfavorable to the test. An active absorption system is often used to eliminate the secondary reflection wave. The force-feedback type active absorption system takes the ratio of the force to the stroke of the wave-making paddles as the absorption transfer function, and controls the input and output of the system in real time, so that the generated wave gradually approaches the ideal wave. In order to maintain the optimal balance between the stroke and force of the wave-making paddles, IIR digital filter based on the theoretical transfer function is designed to realize the active absorption control. To design IIR digital filter, particle swarm optimization algorithm is a very efficient design method, the algorithm is simple and has fast calculation speed, but the algorithm in the optimization process could be limited to the local optimum, and can’t get the global optimal solution. In this paper, a new kind of method is used to update the particle velocity based on inertia weight to improve the learning factor. Inertia weight and learning factor have a nonlinear function relation. Compared with the improved method of linear decrease and nonlinear decrease of inertia weight, the simulation results show that this method has fast convergence speed, fewer iteration times, higher solving efficiency and optimal optimization results.