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Computational Intelligence in Automotive Applications Episode 2 Part 1

Tham khảo tài liệu 'computational intelligence in automotive applications episode 2 part 1', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Recurrent Neural Networks for AFR Estimation and Control in Spark Ignition Automotive Engines 149 Since the nineties many studies addressed the benefits achievable by replacing the PI controller by advanced control architectures such as linear observers 42 Kalman Filter 4 13 26 and sliding mode 10 27 51 . Nevertheless a significant obstacle to the implementation of such strategies is represented by measuring delay due to the path from injection to O2 sensor location and the response time of the sensor itself. To overcome these barriers new sensors have been developed 36 but their practical applicability in the short term is far from certain due to high cost and impossibility to remove transport delay. Thus developing predictive models from experimental data might significantly contribute to promoting the use of advanced closed loop controllers. 3.1 RNN Potential Recurrent Neural Network whose modeling features are presented in the following section have significant potential to face the issues associated with AFR control. The authors themselves 5 and other contributions e.g. Alippi et al. 1 showed how an inverse controller made of two RNNs simulating both forward and inverse intake manifold dynamics is suitable to perform the feedforward control task. Such architectures could be developed making use of only one highly-informative data-set 6 thus reducing the calibration effort with respect to conventional approaches. Moreover the opportunity of adaptively modifying network parameters allows accounting for other exogenous effects such as change in fuel characteristics construction tolerances and engine wear. Besides their high potential when embedded in the framework of pure neural-network controller RNN AFR estimators are also suitable in virtual sensing applications such as the prediction of AFR in coldstart phases. RNN training during cold-start can be performed on the test-bench off-line by pre-heating the lambda sensor before turning on the engine. Moreover .