Model predictive engine air-ratio control using online sequential extreme learning machine

Air-ratio is an important engine parameter that relates closely to engine emissions, power, and brake-specific fuel consumption. Model predictive controller (MPC) is a well-known technique for air-ratio control. This paper utilizes an advanced modelling technique, called online sequential extreme learning machine (OSELM), to develop an online sequential extreme learning machine MPC (OEMPC) for air-ratio regulation according to various engine loads. The proposed OEMPC was implemented on a real engine to verify its effectiveness. Its control performance is also compared with the latest MPC for engine air-ratio control, namely diagonal recurrent neural network MPC, and conventional proportional–integral–derivative (PID) controller. Experimental results show the superiority of the proposed OEMPC over the other two controllers, which can more effectively regulate the air-ratio to specific target values under external disturbance. Therefore, the proposed OEMPC is a promising scheme to replace conventional PID controller for engine air-ratio control.