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In this paper, pioneering Adaptive Neuro Fuzzy Inference System (ANFIS) which is trained with well known traditional Proportional, Integral and Derivative (PID) controller data for Half Car (HC) model is proposed to improve the travelling comfort. The travelling performance is generally assessed at the design stage in automobile industries by simulating the vehicle response to various road excitations...
This article applies iterative learning control (ILC) to road simulation test system and simulates the control system to reproduce a stochastic pavement profile. With uniform white noise as input, using actual measured input-output data and dynamic neural network, system nonlinear autoregressive moving average model (NARMA) was established. Regarding road simulator control mission as a perfect tracking...
On account of the random fluctuation of traffic demands or some special events, the signalized intersection system often exhibits severe nonlinear and time-varying behavior and therefore cannot be adequately controlled with some conventional means. A stochastic traffic signal control scheme, based on reinforcement learning, is introduced in the traffic signal control systems due to its powerful adaptability...
Practical large-scale nonlinear control systems require an intensive and time-consuming effort for the fine-tuning of their control parameters in order to achieve a satisfactory performance. In most cases, the fine-tuning process may take years and is performed by experienced personnel. The purpose of this paper is to introduce and analyze a systematic approach for the automatic fine-tuning of the...
Traffic signal control is an effective way to regulate traffic flow to avoid conflict and reduce congestions. This research investigates a real-time traffic signal control system that integrates a traffic flow prediction model and an adaptive control scheme based on dynamic programming with rolling horizon. The proposed approach estimates the parameter of the arriving traffic flow at the intersection,...
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