Abstract
In this chapter, the optimal control problems of modern wireless networks and automotive engines are studied by using ADP methods. In the first part, a novel learning control architecture is proposed based on adaptive critic designs/ADP, with only a single module instead of two or three modules. The choice of utility function for the present self-learning control scheme makes the present learning process much more efficient than existing learning control methods. The call admission controller can perform learning in real time as well as in off-line environments and the controller improves its performance as it gains more experience. In the second part, an ADP-based learning algorithm is designed according to certain criteria and calibrated for vehicle operation over the entire operating regime. The algorithm is optimized for the engine in terms of performance, fuel economy, and tailpipe emissions through a significant effort in the research and development and calibration process. After the controller has learned to provide optimal control signals under various operating conditions off-line or on-line, it is applied to perform the task of engine control in real time. The performance of the controller can be further refined and improved through continuous learning in real-time vehicle operations.
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Zhang, H., Liu, D., Luo, Y., Wang, D. (2013). Other Applications of ADP. In: Adaptive Dynamic Programming for Control. Communications and Control Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-4757-2_10
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DOI: https://doi.org/10.1007/978-1-4471-4757-2_10
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