Automation
Ali Mirmohammad Sadeghi; Abdollah Amirkhani; Behrooz Mashadi
Abstract
Recognizing a driver’s braking intensity plays a pivotal role in developing modern driver assistance and energy management systems. Therefore, it is especially important to autonomous and electric vehicles. This paper aims at developing a strategy for recognizing a driver’s braking intensity ...
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Recognizing a driver’s braking intensity plays a pivotal role in developing modern driver assistance and energy management systems. Therefore, it is especially important to autonomous and electric vehicles. This paper aims at developing a strategy for recognizing a driver’s braking intensity based on the pressure produced in the brake master cylinder. In this regard, a model-based, synthetic data generation concept is used to generate the training dataset. This technique involves two closed-loop controlled models: an upper-level longitudinal vehicle dynamics model and a lower-level brake hydraulic dynamic model. The adaptive particularly tunable fuzzy particle swarm optimization algorithm is recruited to solve the optimal K-means clustering. By doing so, the best number of clusters and positions of the centroids can be determined. The obtained results reveal that the brake pressure data for a vehicle traveling the new European driving cycle can be best partitioned into two clusters. A driver’s braking intensity may, therefore, be clustered as moderate or intensive. With the ability to automatically recognize a driver’s pedal feel, the system developed in this research could be implemented in intelligent driver assistance systems as well as in electric vehicles equipped with intelligent, electromechanical brake boosters.
Automation
Pardeep kumar Rohilla; Feras Hakkak; Vineet Kumar
Abstract
Inherent nonlinearities like, deadband, stiction and hysteresis in control valves degenerate plant performance. Valve stiction standouts as a more widely recognized reason for poor execution in control loops. Measurement of valve stiction is essential to maintain scheduling. For industrial scenarios, ...
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Inherent nonlinearities like, deadband, stiction and hysteresis in control valves degenerate plant performance. Valve stiction standouts as a more widely recognized reason for poor execution in control loops. Measurement of valve stiction is essential to maintain scheduling. For industrial scenarios, loss of execution due to nonlinearity in control valves is an imperative issue that should be tackled. Thus, an intelligent technique is required for automated execution, observation and enhancement. The paper shows the creative utilization of an intelligent controller for nonlinearity diagnosis in control valves. This is a Fuzzy Gain Scheduling (FGS) PID smart controller that tunes its gain parameters in real time to manage a control valve’s inherent nonlinearity. The viability of the FGS PID controller is experimentally verified in a laboratory scale plant. An execution comparison between FGS PID and classical PID controllers are undertaken for their set point following and disturbance rejection at different operating points. Experimental results show that the FGS PID controller outperforms the classical PID controller for all explored cases effectively managing stiction based oscillation in the controller output.
