{'fraction_non_alphanumeric': 0.03664495114006515, 'fraction_numerical': 0.0, 'mean_word_length': 5.114427860696518, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hybrid model predictive controller for engine air-ratio control

Air-ratio is an important engine parameter which 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 two advanced techniques, discrete wavelet transformation (DWT) and relevance vector machine (RVM), to develop wavelet relevance vector machine model predictive controller (W-MPC) for air-ratio regulation. To compensate for the modelling error of W-MPC and system disturbances, the W-MPC is proposed to connect in parallel with a proportional-integral (PI) controller so as to form a new hybrid model predictive controller (H-MPC). The proposed H-MPC is implemented on a real engine to evaluate its effectiveness. Its control performance is also compared with the W-MPC without PI controller and the latest MPC for engine air-ratio control in the literature. Experimental results show the superiority of the proposed H-MPC over the other two controllers, which can more effectively regulate the air-ratio to target values under external disturbance. Therefore, the proposed H-MPC is a promising scheme for engine air-ratio control.