Gain scheduled linear quadratic tracking system tuned optimally by covariance matrix adaption evolutionary strategy for automotive engine coldstart control

In this paper, a gain scheduled linear quadratic tracking system (LQTS) tuned optimally by an evolutionary strategy (ES) is devised to reduce the total tailpipe hydrocarbon (HC) emissions of an automotive engine over the coldstart period. As the engine’s behavior during coldstart operations is nonlinear, the system dynamics is clearly analyzed and represented by a number of separate linear models generated based on a coldstart model verified by experimental data. An independent LQTS is then implemented for each of these linear models. In this way, several control laws are created, and the corresponding gains are calculated for each of the independent control laws. ES is then used to tune the adjustable parameters of LQTSs to calculate the control inputs, namely air/fuel ratio (AFR) and spark timing (Δ), such that the resulting exhaust gas temperature (T _exh) and engine-out HC emissions (HC _raw) be close to a set of optimum profiles. This enables the controller reduce the cumulative tailpipe hydrocarbon emissions (HC _cum) to the highest possible extent. To demonstrate the acceptable performance of the proposed controller, an optimal controller derived from the Pontryagin’s minimum principle (PMP) is also taken into account. Based on the results of the conducted comparative study, it is shown that the proposed control technique has a very good performance, and also, can be easily used for real-time applications, as it consumes a remarkably trivial computational time for calculating the controlling commands.     

Transfer demand prediction for timed transfer coordination in public transport operational control

Timed transfer coordination in public transit reduces passenger transfer time by providing seamless interconnected transfers. The problem arises when a Receiving Vehicle (RV) arrives to the transfer stop before a Feeding Vehicle (FV) carrying transferring passengers. Timed transfer coordination in operational control dynamically decides whether a RV is held at the transfer stop to allow transfers, or departs as scheduled. While transfer demand is essential for implementing timed transfer coordination, this variable is generally not available in public transit because of the lack of passenger transfer plans. The problem of acquiring this variable in real‐time has also received limited attention in the related literature. This paper proposes a new method to dynamically predict the transfer demand. We anticipate the transferring probability from each individual passenger by examining historical travel itineraries. Three different types of models (simple analytical, statistical, and computation intelligence model) are developed to forecast the number of transferring passengers. Numerical experiments using observed Automatic Vehicle Location and Automatic Fare Collection data from South East Queensland, Australia show the accuracy and applicability of the proposed models in timed transfer coordination. Copyright © 2017 John Wiley & Sons, Ltd.     

Efficiency in Asian railways: a comparison between data envelopment analysis approaches

Performance analysis has become a vital part of the management practices in the logistics infrastructure. Although there are numerous applications using Data Envelopment Analysis (DEA) models to estimate efficiency in ports and airports, research on railway efficiency remains scarce. Most of the efficiency studies of railways assume that inputs and outputs are known with absolute precision. Here, we compare Stochastic-DEA and Fuzzy-DEA models to assess, respectively, how the underlying randomness and fuzziness impact efficiency levels in railway operations in six different Asian countries: Japan, Thailand, Vietnam, Malaysia, Myanmar, and Indonesia. Findings reveal that conclusions with respect to the ranking of these railways may vary substantially depending upon the type of model chosen, although efficiency scores are similar to some extent when compared within the ambits of Stochastic-DEA and Fuzzy-DEA models. Additionally, modeling choices on fuzziness, rather than randomness, appear to be the most critical source for variations in efficiency rankings.     

Robust state feedback stabilization of articulated steer vehicles

In this work, a full-state feedback controller is designed to prevent the oscillatory instability or snaking behaviour of an articulated steer vehicle. To design the controller, first, a linearized model of the vehicle is developed and analyzed to identify the most important uncertain tire parameters with regard to the snaking mode. By using this linearized model, the equations of motion are represented in the form of a polytopic system, which depends affinely on the most important uncertain tire parameters. Then, by solving some linear matrix inequalities, both the Lyapunov and state feedback matrices for the robust stabilization of the vehicle are found. The performance of the resulting controller is evaluated by conducting several simulations based on the linearized model. To verify the results from the linearized model analysis, some simulations are also done by a virtual prototype of the vehicle in ADAMS. The results based on the linearized model are reasonably consistent with those from the simulations in ADAMS. They show that the controller can effectively stabilize the vehicle during the snaking mode in different driving conditions.