Due to the strong nonlinearity of the working process of compression-ignition aero piston engine, only using Model Predictive Control (MPC) algorithm to realize the torque control of compression ignition aero-piston engine would lead to the unsatisfactory accuracy of torque prediction based on state space model. The above problems could be solved by the compound predictive control of engine torque based on Radial Basis Function (RBF) neural-network and MPC. Firstly, the engine operation data obtained by the MAP control method were used as the neural network training sample-set on the self-built engine joint simulation platform. Secondly, the Simulated Annealing (SA) mechanism introduced into particle swarm optimization (PSO) algorithm was to train the RBF neural network torque prediction model. Finally, through joint simulation, the control algorithm was continuously modified to verify the superiority of SA + PSO algorithm in training engine torque prediction model on RBF neural network. Also it could effectively improve the problem that PSO algorithm was easy to fall into local optimization. The effectiveness of torque compound predictive control was verified by the engine bench experiment.