Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new approach for engine calibration and control is proposed. ,  present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine,  developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control.  have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.
['Derong Liu', 'Hossein Javaherian', 'Olesia Kovalenko', 'Ting Huang']
I am writing a research paper on a idea Adaptive Critic Learning Techniques for Engine Torque and Air–Fuel Ratio Control
[{"from": "human", "value": "I am writing a research paper on a idea Adaptive Critic Learning Techniques for Engine Torque and Air\u2013Fuel Ratio Control"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new approach for engine calibration and control is proposed. ,  present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine,  developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control.  have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved."}]