Fuzzy Logic-based Adaptive Cruise Control for Autonomous Model Car

Alomari, Khaled; Mendoza, Ricardo; Sundermann, Stephan; Goehring, Daniel; Rojas, Raúl

doi:10.5220/0010175101210130

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Some studies suggest the use of artificial intelligence in ACC control, showing promising results comparing fuzzy and neural network-based controls as mentioned in the study [10]. When compared to conventional PID control, AI-based control outperforms as presented in the study [11]. Other evidence to show the promising results of using AI-based technique for the case of ACC have also been conducted by the study [12,13], utilizing metaheuristic optimization techniques genetic algorithm (GA) and particle swarm optimization (PSO), respectively.…”

Section: Introductionmentioning

confidence: 76%

The Adaptive Cruise Control for Curved Roads Using Archived Crow Search Algorithm

Prastiyanto,

Apriaskar,

Subiyanto

et al. 2024

IJTDI

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One of the advanced driver assistance systems (ADAS) technologies that can address the issue of high-traffic accidents is adaptive cruise control (ACC). However, a challenge arises due to the lack of control algorithm development in ACC technology that accommodates curved road conditions. This paper proposes a comprehensive solution by introducing ACC for curved roads through the utilization of a multidimensional control system model. This paper aims to implement the crow search algorithm (CSA) into the ACC technology: (1) Our objective is to apply the original crow search algorithm (OCSA) to find the most optimal values for the parameters verr, xerr, vx of ACC, and kp and ki of lateral displacement control; (2) We also implement the archived crow search algorithm (ACSA) into the control system, which is considered to have faster computation time than OCSA. Based on the obtained results, ACSA demonstrates faster computation time. The optimal values for achieving enhanced performance are found to be kp at 0.7492, ki at 0.6506, verr at 0.9716, xerr at 0.9778, and vx at 0.7012. This model was developed using MATLAB and compared to the non-optimized version. The research aims to contribute to ADAS development by addressing the optimization challenges of control algorithms for ACC parameters on curved roads. Ultimately, this solution enhances driver safety by providing more effective control in challenging road conditions.

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Section: Introductionmentioning

confidence: 76%

The Adaptive Cruise Control for Curved Roads Using Archived Crow Search Algorithm

Prastiyanto,

Apriaskar,

Subiyanto

et al. 2024

IJTDI

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“…The proposed methodology is implemented in a case study examining the deterioration of aircraft engines. Fuzzy logic is currently used in a number of industrial and consumer electronics devices that require an effective control system but where optimal control is not necessarily a concern [81].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Fu,

Avdelidis

2023

Sensors

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Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.

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“…Using a common logic rule, the switching strategy is developed based on the relative distance with the lead vehicle. The strategy is quite straightforward and implantable, yet as reported by [4], in the presence of unmeasured disturbance, the control performance will be deteriorated. Indeed, there are several options to overcome these issues such as improving the accuracy of the fuzzy rule by using a different logic function or combining it with other advanced controllers [5].…”

Section: Introductionmentioning

confidence: 98%

Performance Analysis of Predictive Functional Control for Automobile Adaptive Cruise Control System

Zainuddin¹,

Abdullah²,

Ahmad³

et al. 2023

IIUMEJ

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This paper presents the performance analysis of Predictive Functional Control (PFC) for Adaptive Cruise Control (ACC) application. To cope with multiple driving objectives of modern ACC systems such as passenger comfort, safe distancing, and fast time response, an advanced optimal controller such as Model Predictive Control (MPC) is often used. Nevertheless, MPC requires a high computation load due to its complex formulation and may overload the processing power of a microcontroller. Thus, the prime objective of this work is to propose a PFC algorithm as an alternative controller, while providing a formal comparison between MPC and the traditional Proportional Integral (PI) controller. A standard kinematic model for vehicle longitudinal dynamics was modelled and used to derive the control law of PFC. Since the open-loop dynamic of the derived transfer function is not stable, the second objective is to propose a pre-stabilized loop or cascade PFC structure for the system. A complete tuning procedure and analysis were presented. The simulation result shows that although MPC performance is the best for the ACC application with Root Mean Square Error (RMSE) of 1.4873, PFC has shown a promising response with RMSE of 1.5501, which is better compared to the PI controller with RMSE of 1.6219. All the imposed driving constraints such as maximum acceleration, maximum deceleration and safe distance were satisfied in the car following application. Thus, the findings from this work can become a good initial motivation to further explore the capability of the PFC algorithm for future ACC development. ABSTRAK: Kajian ini adalah berkenaan analisis prestasi Kawalan Fungsi Ramalan (PFC) aplikasi Kawalan Mudah Suai (ACC). Bagi memenuhi pelbagai keperluan objektif sistem pemanduan moden ACC seperti keselesaan penumpang, penjarakan selamat dan tindak balas pantas, kawalan optimum terbaru seperti Model Kawalan Ramalan (MPC) sering digunakan. Walau bagaimanapun, MPC memerlukan beban pengiraan tinggi kerana rumusnya yang kompleks dan mungkin mengakibatkan beban berlebihan kuasa pemprosesan mikrokawalan. Oleh itu, matlamat utama kajian ini adalah bagi mencadangkan algoritma PFC yang mempunyai pengiraan mudah sebagai kawalan alternatif, sementara menyediakan perbandingan formal antara MPC dan kawalan tradisional Berkadar Keseluruhan (PI). Oleh kerana model ini tidak stabil, objektif kedua adalah mencadangkan penggunaan struktur PFC berlapis bagi menstabilkan sistem terlebih dahulu sebelum algorithma kawalan digunakan atau dengan menggunakan struktur PFC secara berturut pada sistem. Prosedur lengkap dan terperinci untuk analisis PFC dibentangkan. Dapatan simulasi kajian menunjukkan walaupun prestasi MPC adalah baik bagi aplikasi ACC dengan Ralat Punca Min Kuasa Dua (RMSE) bernilai 1.4873, namun PFC menunjukkan tindak balas baik dengan RMSE bernilai 1.5501 berbanding kawalan PI yang mempunyai RMSE sebanyak 1.6219. Kesemua kekangan seperti pecutan dan nyahpecutan maksima, dan penjarakan selamat bertepatan dengan aplikasi kenderaan ini. Dengan itu, penemuan ini adalah motivasi awal yang baik bagi meneroka lebih jauh keupayaan algoritma PFC bagi membangun ACC pada masa hadapan.

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Fuzzy Logic-based Adaptive Cruise Control for Autonomous Model Car

Cited by 7 publications

References 9 publications

The Adaptive Cruise Control for Curved Roads Using Archived Crow Search Algorithm

The Adaptive Cruise Control for Curved Roads Using Archived Crow Search Algorithm

Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview

Performance Analysis of Predictive Functional Control for Automobile Adaptive Cruise Control System

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