Abstract: The liquid supply system in fully mechanized working face has the problems of insufficient liquid supply capacity, large pressure fluctuation and poor system operation stability. In order to solve the above problems, an immune particle swarm optimization fuzzy neural network PID (IPSO-FNN-PID) algorithm is proposed. The IPSO-FNN-PID controller is designed to stabilize the pressure of the liquid supply system. In the IPSO-FNN-PID algorithm, a particle swarm optimization (PSO) algorithm and an immune algorithm (IA) are introduced into a fuzzy neural network (FNN) PID controller. The immune particle swarm optimization (IPSO) algorithm is used to solve the problem that the FNN algorithm is easy to fall into local optimization. The IA is added to the PSO algorithm to improve the convergence of the PSO algorithm. Therefore, the output of the optimal PID parameters is realized. In order to verify the effectiveness of the IPSO-FNN-PID controller, traditional PID controller, Fuzzy-PID controller and FNN-PID controller are selected to compare. The simulation results show that the IPSO-FNN-PID controller has the best control effect on the emulsion pump. The rise time, peak time and regulation time of the other three controllers are longer than the IPSO-FNN-PID controller. The maximum overshoot is greater than the IPSO-FNN-PID controller. After adding the disturbance signal, the IPSO-FNN-PID controller has good adaptability and robustness, and it takes only 1.2 s to restore to a stable state. When traditional PID and Fuzzy-PID controllers are used to control the emulsion pump, the oscillation is obvious and the overshoot is large, which are 41.2% and 22.3% respectively. When the FNN-PID controller is used to control the emulsion pump, the oscillation is significantly weakened, the overshoot is reduced to 17.6%, and the adjustment time is reduced to 2.68 s. When the IPSO-FNN-PID controller is used to control the emulsion pump, there is almost no oscillation. The overshoot is only 5.22%, the adjustment time is shortened to 2.61 s. And the stability is stronger when encountering interference signals. When the disturbance signal is received, the load disturbance has little effect on the IPSO-FNN-PID controller, the convergence is rapid, and the robustness is greatly improved. The results show that the IPSO-FNN-PID controller has good anti-disturbance and disturbance compensation capability, and can meet the pressure stabilization control requirements of the liquid supply system.