Dynamic Car–Following Model Calibration Using SPSA and ISRES Algorithms

Markou, Ioulia; Papathanasopoulou, Vasileia; Antoniou, Constantinos

doi:10.3311/pptr.8616

Cited by 8 publications

(4 citation statements)

References 27 publications

(37 reference statements)

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“…Due to this feature, it is ensured to make strong predictions even when the size of the optimization problem is increased. While similar algorithms do many operations in each iteration, SPSA comes to the fore with its ability to solve optimization problems that would take days or hours in a shorter time since it only makes two predictions (Markou et al , 2019). Similar to gradient-based approaches such as gradient descent, SPSA is an iterative optimization algorithm.…”

Section: Control System Deep Neural Network and Simultaneous Stochast...mentioning

confidence: 99%

Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA

Köse

Oktay²

2023

AEAT

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Purpose The purpose of this paper is to optimize the simultaneous flight performance of a hexarotor unmanned aerial vehicle (UAV) by using simultaneous perturbation stochastic approximation (i.e. SPSA), deep neural network and proportional integral derivative (i.e. PID) according to varying arm length (i.e. morphing). Design/methodology/approach In this paper, proper PID gain coefficients and morphing ratio were obtained using the stochastic optimization method, also known as SPSA to maximize flight efficiency. Because it is difficult to establish an analytical connection between the morphing ratio and hexarotor moments of inertia, the deep neural network was used to obtain the moments of inertia according to the morphing ratio. By using SPSA and deep neural network, the best performance indexes were obtained and both longitudinal and lateral flight simulations were performed with the obtained data. Findings With SPSA, the best PID coefficients and morphing ratio are obtained for both longitudinal and lateral flight. Because the hexarotor solid body model changes according to the morphing ratio, the moment of inertia values used in the simulations also change. According to the morphing ratio, the moment of inertia values was obtained with the deep neural network over a created data set. Research limitations/implications It takes a long time to obtain the morphing ratio suitable for the hexarotor model and the PID gain coefficients suitable for this morphing ratio. However, this situation can be overcome with the proposed SPSA. In addition, it takes a long time to obtain the appropriate moments of inertia according to the morphing ratio. However, in this case, it was overcome using the deep neural network. Practical implications Determining the morphing ratio and PID gain coefficients using the optimization method, as well as determining the moments of inertia using the deep neural network, is very useful as it can increase the efficiency of hexarotor flight and flight efficiently with different arm lengths. With the proposed method, the hexarotor design performance criteria (i.e. rise time, settling time and overshoot) values were significantly improved compared to similar studies. Social implications Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability. Originality/value The hexarotor flight efficiency is improved with the proposed SPSA and deep neural network approaches. In addition, the desired flight parameters can be obtained more quickly and reliably with the proposed approaches. The design performance criteria were also improved, enabling the hexarotor UAV to follow the given trajectory in the best way and providing convenience for end users. SPSA was preferred because it converged faster than other methods. While other methods perform 2n operations per iteration, SPSA only performs two operations. To obtain the moment of inertia, many physical parameter values of the UAV are required in the existing methods. In the proposed method, by creating a date set, only arm length and moment of inertia were estimated without the need to obtain physical parameters with the deep neural network structure.

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Section: Control System Deep Neural Network and Simultaneous Stochast...mentioning

confidence: 99%

Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA

Köse

Oktay²

2023

AEAT

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“…Here we described the modeling of an autonomous car as represented in Figure 1 and for designing an optimal AVS the following parameters are taken into consideration: the velocity updation equation, actual and measured velocity, estimated distance, error rate as well as cost function (Markou et al, 2019).…”

Section: Avs Designmentioning

confidence: 99%

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Ravikumar

Kavitha

2021

Journal of Field Robotics

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Numerous developments in technology toward autonomous vehicle systems (AVSs) have been performed for so many years all over the world. As our day-to-day life is becoming progressively dependent on automation vehicle system and control devices, the craze on automation advancements is expected to move closer through scientific technologies like artificial intelligence and robotics. From another point of view, the cyber threat to the AVS causes drastic accidents and traffic congestion by varying the speed differences among the vehicles. To overcome such shortcomings, this paper presented a convolutional neural network-oppositional-based Henry gas solubility optimization (CNN-OHGS) algorithm for an autonomous vehicle control system to enhance the robustness of the vehicle. At the same time, the attackers attempt to embed the faulty or defective data into the sensor readings of the autonomous vehicle to interrupt the optimal distances among the automated vehicles. Therefore to minimize such issues, our proposed framework employs the CNN-OHGS algorithm to reduce the distance variations among the vehicles thus ensuring the safety and optimal distance variation. Finally, the experimental analysis is conducted and the performance evaluation for various attacks, FID evaluation, and remorse function, and distance deviation for all sensor signal attacks are evaluated.The comparative analysis is made and we can clearly state that the proposed work has outperformed other existing approaches.

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“…Different approaches can be applied to the calibration and validations [64]. In this study, the concept of the model was validated by comparison evaluation of the Markov car-following model (6) with the Gazis-Herman-Rothery (GHR) model (7) and the S-curve model (8) solved in a simulation environment.…”

Section: A New Car-following Modelmentioning

confidence: 99%

Intelligent Total Transportation Management System for Future Smart Cities

Nguyen

Rohács

et al. 2020

Applied Sciences

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Smart mobility and transportation, in general, are significant elements of smart cities, which account for more than 25% of the total energy consumption related to smart cities. Smart transportation has seven essential sections: leisure, private, public, business, freight, product distribution, and special transport. From the management point of view, transportation can be classified as passive or non-cooperating, semi-active or simple cooperating, active or cooperating, contract-based, and priority transportation. This approach can be applied to public transport and even to passengers of public transport. The transportation system can be widely observed, analyzed, and managed using an extensive distribution network of sensors and actuators integrated into an Internet of Things (IoT) system. The paper briefly discusses the benefits that the IoT can offer for smart city transportation management. It deals with the use of a hierarchical approach to total transportation management, namely, defines the concept, methodology, and required sub-model developments, which describes the total system optimization problems; gives the possible system and methodology of the total transportation management; and demonstrates the required sub-model developments by examples of car-following models, formation motion, obstacle avoidances, and the total management system implementation. It also introduces a preliminary evaluation of the proposed concept relative to the existing systems.

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Dynamic Car–Following Model Calibration Using SPSA and ISRES Algorithms

Abstract: Calibration plays a fundamental role in successful applications

Cited by 8 publications

References 27 publications

Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA

Simultaneous design of morphing hexarotor and autopilot system by using deep neural network and SPSA

CNN‐OHGS: CNN‐oppositional‐based Henry gas solubility optimization model for autonomous vehicle control system

Intelligent Total Transportation Management System for Future Smart Cities

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