An optimal learning-based controller derived from Hamiltonian function combined with a cellular searching strategy for automotive coldstart emissions

作者：Azad Nasser L^*; Mozaffari Ahmad; Fathi Alireza

来源：International Journal of Machine Learning and Cybernetics, 2017, 8(3): 955-979.

DOI：10.1007/s13042-015-0467-x

• Automotive coldstart
• Hydrocarbon emissions
• Extreme learning machine
• Cellular particle swarm optimization
• Euler-Lagrange equation
• Optimal control

The main objective of this study is to propose a novel algorithmic framework for the implementation of a nonlinear controller for reducing the amount of tailpipe hydrocarbon emissions in automotive engines over the coldstart period. To this aim, the control problem for a given engine is formulated in the form of the standard Bolza problem, and then, the concepts of Euler-Lagrange equation and Hamiltonian function are taken into account to calculate the optimal states, co-states, and control input signals. An extreme learning machine is also linked to an experimentally validated nonlinear state-space representation of the engine during the coldstart to approximate the values of exhaust gas temperature and engine-out hydrocarbon emissions, which are two key variables for the considered control problem. To solve the resulting system of equations, a cellular variant of the particle swarm optimization technique is implemented and the existing nonlinear system of equations is solved heuristically. In addition, some constraints are exerted on the control signals to guarantee the smooth operation of the engine by applying the calculated controlling commands. Finally, the authenticity of the resulting optimal controller is validated against a classical Pontryagin's minimum principle-based control system. Generally, the findings demonstrate the effectiveness of the proposed control methodology to reduce coldstart hydrocarbon emissions in automotive engines.

• 出版日期2017-6