Diesel engine air path control based on neural approximation of nonlinear MPC
| Autor: | Ryuta Moriyasu, Akio Matsunaga, Tomohiko Jimbo, Sayaka Nojiri, Toshihiro Nakamura |
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| Rok vydání: | 2019 |
| Předmět: |
Compressor stall
0209 industrial biotechnology Artificial neural network Computer science Applied Mathematics 020208 electrical & electronic engineering 02 engineering and technology Kalman filter Computer Science Applications Nonlinear system Model predictive control 020901 industrial engineering & automation Control and Systems Engineering Control theory Approximation error Control system 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering |
| Zdroj: | Control Engineering Practice. 91:104114 |
| ISSN: | 0967-0661 |
| Popis: | This paper deals with a control design problem for a diesel engine air path system that has strong nonlinearity and requires multi-input and multi-output control to satisfy requirements and constraints. We focus on a neural network based approximation of nonlinear model predictive control (NMPC) for high-speed computation. Most neural approximation methods are verified only through simulation; further, the influence of approximation on the closed-loop performance has been not sufficiently discussed. In this study, we discuss this influence, and propose a new method to improve stability against degradation due to an approximation error. The control system is assembled using a neural network based controller, obtained by the proposed method, and an unscented Kalman filter. This system is verified both numerically and experimentally; the results demonstrate the capability of the proposed method to track the boost pressure, EGR rate, and pumping loss according to the reference values, and satisfy the constraints of compressor surge and choke. The high computation speed that can be achieved using a standard on-board ECU is also demonstrated using the approximated controller. |
| Databáze: | OpenAIRE |
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