Abstract
In the realm of modern transportation, autonomous electric vehicles (AEVs) are pivotal for realizing intelligent, electrified mobility. Their progress relies heavily on smart Internet of Things (IoT) devices, crucial for enhancing operational capabilities and ensuring security against cyber threats. This study proposes two key strategies to enhance AEV performance. Firstly, addressing the challenge of steering angle adjustment in AEVs due to road fluctuations and vision system dynamics is paramount. The thesis introduces a fast model predictive controller (MPC) based on discrete-time Laguerre function (DTLF) to mitigate the computational burden of traditional MPC. Augmenting this, a modern dandelion optimizer (DO) strategy fine-tunes the hybrid DTLF-MPC, yielding optimal parameters and favorable outcomes. Diverse scenarios illustrate the effectiveness of this approach in combating vision system uncertainty and road fluctuations, with superior damping performance and shorter settling time compared to alternative algorithms. Secondly, a novel IoT architectural paradigm integrating MPC and neural network (NN) frameworks is proposed. This architecture aims to fortify AEVs against the disruptive impact of erroneous data intrusions resulting from cyber breaches. Through various test scenarios, the effectiveness of this IoT architecture with MPC and NN is emphasized in enhancing AEV performance. Results affirm the capability of the proposed approach to effectively mitigate cyberattacks and data loss, thereby improving production processes and decision-making. In summary, this study presents innovative strategies to enhance AEV performance, addressing challenges related to steering angle adjustment and cybersecurity. The proposed approaches, encompassing advanced control methodologies and IoT architectures, exhibit promising results in bolstering AEV capabilities and resilience against emerging threats.

References
[1] S. Zhang, C. Markos, and J. J. Q. Yu, “Autonomous vehicle intelligent system: Joint ride-sharing and parcel delivery strategy,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 10, pp. 18466–18477, 2022.
[2] M. Khalid et al., “Autonomous transportation in emergency healthcare services: Framework, challenges, and future work,” IEEE Internet Things M., vol. 4, no. 1, pp. 28–33, 2021.
[3] H. Marzbani, H. Khayyam, C. N. To, D. V. Quoc, and R. N. Jazar, “Autonomous vehicles: Autodriver algorithm and vehicle dynamics,” IEEE Trans. Veh. Technol., vol. 68, no. 4, pp. 3201–3211, 2019.
[4] H. Nam, W. Choi, and C. Ahn, “Model predictive control for evasive steering of an autonomous vehicle,” Int. J. Automot. Technol., vol. 20, no. 5, pp. 1033–1042, 2019.
[5] A. Hernandez-Sanchez, A. Poznyak, and I. Chairez, “Robust proportional–integral control of submersible autonomous robotized vehicles by backstepping-averaged sub-gradient sliding mode control,” Ocean Eng., vol. 263, no. 1, pp.11–21, 2022.
[6] A. Hernandez-Sanchez, I. Chairez, A. Poznyak, and O. Andrianova, “Dynamic motion backstepping control of underwater autonomous vehicle based on averaged sub-gradient integral sliding mode method,” J. Intell. Robot. Syst., vol. 103, no. 3, pp. 127–134, 2021.
[7] A. Miglani and N. Kumar, “Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges,” Veh. Commun., vol. 20, no. 3, pp. 176–184, 2019.
[8] A. Aksjonov, P. Nedoma, V. Vodovozov, E. Petlenkov, and M. Herrmann, “Detection and evaluation of driver distraction using machine learning and fuzzy logic,” IEEE Trans. Intell. Transp. Syst., vol. 20, no. 6, pp. 2048–2059, 2019.
[9] L. Nie, J. Guan, C. Lu, H. Zheng, and Z. Yin, “Longitudinal speed control of autonomous vehicle based on a self‐adaptive PID of radial basis function neural network,” IET Intell. Transp. Syst., vol. 12, no. 6, pp. 485–494, 2018.
[10] X. Han, X. Zhang, Y. Du, and G. Cheng, “Design of autonomous vehicle controller based on BP-PID,” IOP Conf. Ser. Earth Environ. Sci., vol. 234, no.2, pp. 120–132, 2019.
[11] H. Nurhadi, E. Apriliani, T. Herlambang, and D. Adzkiya, “Sliding mode control design for autonomous surface vehicle motion under the influence of environmental factor,” Int. J. Electr. Comput. Eng. (IJECE), vol. 10, no. 5, pp. 147–159, 2020.
[12] R. Rout and B. Subudhi, “Inverse optimal self-tuning PID control design for an autonomous underwater vehicle,” Int. J. Syst. Sci., vol. 48, no. 2, pp. 367–375, 2017.
[13] L. González, E. Martí, I. Calvo, A. Ruiz, and J. Pérez, “Towards risk estimation in automated vehicles using fuzzy logic,” in International Conference on Computer Safety, Reliability, and Security, Cham: Springer, 2018, pp. 278–289.
[14] X. Xiang, C. Yu, and Q. Zhang, “Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties,” Comput. Oper. Res., vol. 84, pp. 165–177, 2017.
[15] T. Mac Thi, C. Copot, R. De Keyser, T. D. Tran, and T. Vu, “MIMO fuzzy control for autonomous mobile robot,” Journal of Automation and Control Engineering, vol. 4, no. 1, pp. 65–70, 2016.
[16] I. Sung, B. Choi, and P. Nielsen, “On the training of a neural network for online path planning with offline path planning algorithms,” Int. J. Inf. Manage., vol. 57, no.3, pp. 132–141, 2021.
[17] Z. Peng and J. Wang, “Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks,” IEEE Trans. Syst. Man Cybern. Syst., vol. 48, no. 4, pp. 535–544, 2018.
[18] X. Du, K. K. K. Htet, and K. K. Tan, “Development of a genetic-algorithm-based nonlinear model predictive control scheme on velocity and steering of autonomous vehicles,” IEEE Trans. Ind. Electron., vol. 63, no. 11, pp. 6970–6977, 2016.
[19] J. Kabzan, L. Hewing, A. Liniger, and M. N. Zeilinger, “Learning-based model predictive control for autonomous racing,” IEEE Robot. Autom. Lett., vol. 4, no. 4, pp. 3363–3370, 2019.
[20] J. Asgari, F. Borelli, H. E. Tseng, and R. Rajamani, “Discussion on: Hybrid parameter-varying model predictive control for autonomous vehicle steering,” Eur. J. Control, vol. 14, no. 5, pp. 432–436, 2008.
[21] Q. Cui, R. Ding, B. Zhou, and X. Wu, “Path-tracking of an autonomous vehicle via model predictive control and nonlinear filtering,” Proc. Inst. Mech. Eng. Pt. D: J. Automobile Eng., vol. 232, no. 9, pp. 1237–1252, 2018.
[22] A. Koga, H. Okuda, Y. Tazaki, T. Suzuki, K. Haraguchi, and Z. Kang, “Realization of different driving characteristics for autonomous vehicle by using model predictive control,” in 2016 IEEE Intelligent Vehicles Symposium (IV), 2016
[23] Y. Zheng, J. Zhou, Y. Xu, Y. Zhang, and Z. Qian, “A distributed model predictive control based load frequency control scheme for multi-area interconnected power system using discrete-time Laguerre functions,” ISA Trans., vol. 68, pp. 127–140, 2017.
[24] L. Wang, Model predictive control system design and implementation using MATLAB®. Springer Science & Business Media, 2009.
[25] K. Dileep, S. Krishnan, and A. Jose, “Vehicular adaptive cruise control using Laguerre functions model predictive control,” Int. J. Eng. Technol. (IJET), vol. 10, no. 6, pp. 1719–1730, 2018.
[26] M. Abdullah, J. A. Rossiter, and R. Haber, “Development of constrained predictive functional control using Laguerre function-based prediction,” IFAC-Papers Online, vol. 50, pp. 10705–10710, 2017.
[27] M. Abdullah and J. A. Rossiter, “Using Laguerre functions to improve the tuning and performance of predictive functional control,” Int. J. Control, vol. 94, no. 1, pp. 202–214, 2021.
[28] S. B. Joseph, E. G. Dada, A. Abidemi, D. O. Oyewola, and B. M. Khammas, “Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems,” Heliyon, vol. 8, no. 5, pp. 93–99, 2022.
[29] G. P. Fotis, L. Ekonomou, T. I. Maris, and P. Liatsis, “Development of an artificial neural network software tool for the assessment of the electromagnetic field radiating by electrostatic discharges,” IET Sci. Meas. Technol., vol. 1, no. 5, pp. 261–269, 2007.
[30] A. F. Winfield, K. Michael, J. Pitt, and V. Evers, “Machine ethics: The design and governance of ethical AI and autonomous systems [scanning the issue],” Proc. IEEE Inst. Electr. Electron. Eng., vol. 107, no. 3, pp. 509–517, 2019.
[31] G. Fotis, V. Vita, and L. Ekonomou, “Machine learning techniques for the prediction of the magnetic and electric field of electrostatic discharges,” Electronics (Basel), vol. 11, no. 12, pp. 18–28, 2022.
[30] R. Sarkar, D. Barman, and N. Chowdhury, “Domain knowledge based genetic algorithms for mobile robot path planning having single and multiple targets,” J. King Saud Univ. - Comput. Inf. Sci., 2020.
[31] Y. Li, Z. Huang, and Y. Xie, “Research status of mobile robot path planning based on genetic algorithm,” J. Phys. Conf. Ser., vol. 1544, no. 1, pp.12–21, 2020.
[32] X. Li, D. Wu, J. He, M. Bashir, and M. Liping, “An improved method of particle swarm optimization for path planning of mobile robot,” J. Control Sci. Eng., vol. 2020, pp. 1–12, 2020.
[33] L. Zhang, Y. Zhang, and Y. Li, “Mobile robot path planning based on improved localized particle swarm optimization,” IEEE Sens. J., vol. 21, no. 5, pp. 6962–6972, 2021.
[34] S. Manne, E. L. Lydia, I. V. Pustokhina, D. A. Pustokhin, V. S. Parvathy, and K. Shankar, “An intelligent energy management and traffic predictive model for autonomous vehicle systems,” Soft Comput., vol. 25, no. 18, pp. 11941–11953, 2021.
[35] F. Mohammadi and R. Rashidzadeh, “An overview of IoT-enabled monitoring and control systems for electric vehicles,” IEEE Instrum. Meas. Mag., vol. 24, no. 3, pp. 91–97, 2021.
[36] A. Weimerskirch, “An overview of automotive cybersecurity: Challenges and solution approaches,” in Proceedings of the 5th International Workshop on Trustworthy Embedded Devices, 2015.
[37] A. Chattopadhyay, U. Mitra, and E. G. Strom, “Secure estimation in V2X networks with injection and packet drop attacks,” in 2018 15th International Symposium on Wireless Communication Systems (ISWCS), 2018.
[38] K. Liu, H. Zhang, Y. Zhang, and C. Sun, “False data-injection attack detection in cyber-physical systems with unknown parameters: A deep reinforcement learning approach,” IEEE Trans. Cybern., vol. 53, no. 11, pp. 7115–7125, 2023.
[39] X. Cai, F. Xiao, and B. Wei, “Resilient Nash equilibrium seeking in multiagent games under false data injection attacks,” IEEE Trans. Syst. Man Cybern. Syst., vol. 53, no. 1, pp. 275–284, 2023.
[40] Z. Ju, H. Zhang, X. Li, X. Chen, J. Han, and M. Yang, “A survey on attack detection and resilience for connected and automated vehicles: From vehicle dynamics and control perspective,” IEEE Trans. Intell. Veh., vol. 7, no. 4, pp. 815–837, 2022.
[41] Y. Wang, N. Masoud, and A. Khojandi, “Real-time sensor anomaly detection and recovery in connected automated vehicle sensors,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 3, pp. 1411–1421, 2021.
[42] K. Salahshoor, M. Mosallaei, and M. Bayat, “Centralized and decentralized process and sensor fault monitoring using data fusion based on adaptive extended Kalman filter algorithm,” Measurement (Lond.), vol. 41, no. 10, pp. 1059–1076, 2008.
[43] M. Sewak and S. Singh, “IoT and distributed machine learning powered optimal state recommender solution,” in 2016 International Conference on Internet of Things and Applications (IOTA), 2016.
[44] F. van Wyk, Y. Wang, A. Khojandi, and N. Masoud, “Real-time sensor anomaly detection and identification in automated vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 3, pp. 1264–1276, 2020.
[45] D. Ding, Q.-L. Han, Y. Xiang, X. Ge, and X.-M. Zhang, “A survey on security control and attack detection for industrial cyber-physical systems,” Neurocomputing, vol. 275, pp. 1674–1683, 2018.
[46] A. Petrillo, A. Pescape, and S. Santini, “A secure adaptive control for cooperative driving of autonomous connected vehicles in the presence of heterogeneous communication delays and cyberattacks,” IEEE Trans. Cybern., vol. 51, no. 3, pp. 1134–1149, 2021.
[47] J. Pasternack and D. Roth, “Judging the veracity of claims and reliability of sources with fact-finders,” in Computational Trust Models and Machine Learning, Chapman and Hall/CRC, 2014, pp. 63–96.
[48] P. S. Chib and P. Singh, “Recent advancements in end-to-end autonomous driving using deep learning: A survey,” IEEE Trans. Intell. Veh., vol. 9, no. 1, pp. 103–118, 2024.
[49] T. Alladi, B. Gera, A. Agrawal, V. Chamola, and F. R. Yu, “DeepADV: A deep neural network framework for anomaly detection in VANETs,” IEEE Trans. Veh. Technol., vol. 70, no. 11, pp. 12013–12023, 2021.
[50] S. Almutlaq, A. Derhab, M. M. Hassan, and K. Kaur, “Two-stage intrusion detection system in intelligent transportation systems using rule extraction methods from deep neural networks,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 12, pp. 15687–15701, 2023.
[51] J. Guo, L. Li, J. Wang, and K. Li, “Cyber-physical system-based path tracking control of autonomous vehicles under cyber-attacks,” IEEE Trans. Industr. Inform., pp. 1–11, 2022.
[52] A. Giannaros et al., “Autonomous vehicles: Sophisticated attacks, safety issues, challenges, open topics, blockchain, and future directions,” J. Cybersecur. Priv., vol. 3, no. 3, pp. 493–543, 2023.
[53] K. Zervoudakis and S. Tsafarakis, “A mayfly optimization algorithm,” Comput. Ind. Eng., vol. 145, 2020.
[54] J. Kosecka, R. Blasi, C. J. Taylor, and J. Malik, “Vision-based lateral control of vehicles,” in Proceedings of Conference on Intelligent Transportation Systems, 2002.
[55] A. Goli, E. B. Tirkolaee, and N. S. Aydin, “Fuzzy integrated cell formation and production scheduling considering automated guided vehicles and human factors,” IEEE Trans. Fuzzy Syst., vol. 29, no. 12, pp. 3686–3695, 2021.
[56] M. Elsisi, “Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm,” Int. J. Intell. Syst., vol. 35, no. 11, pp. 1857–1878, 2020.
[57] M. B. Fakhrzad and F. Goodarzian, “A new multi-objective mathematical model for a Citrus supply chain network design: Metaheuristic algorithms.” Journal of Optimization in Industrial Engineering, vol. 14, pp. 127-144, 2021.
[58] M. Elsisi, K. Mahmoud, M. Lehtonen, and M. M. F. Darwish, “An improved neural network algorithm to efficiently track various trajectories of robot manipulator arms,” IEEE Access, vol. 9, pp. 11911–11920, 2021.
[59] M. Mokhtarzadeh, R. Tavakkoli-Moghaddam, C. Triki, and Y. Rahimi, “A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities,” Eng. Appl. Artif. Intell., vol. 98, 2021.
[60] S. Singh, B. V. Akhil, S. Chowdhury, and S. K. Lahiri, “A detailed insight into the optimization of plate and frame heat exchanger design by comparing old and new generation metaheuristics algorithms,” J. Indian Chem. Soc., vol. 99, no. 2, pp. 123-131, 2022.
[61] M. J. Blondin, “Optimization algorithms in control systems,” in SpringerBriefs in Optimization, Cham: Springer International Publishing, 2021, pp. 1–9.
[62] K. Zervoudakis and S. Tsafarakis, “A mayfly optimization algorithm,” Comput. Ind. Eng., vol. 145, p. 106559, Jul. 2020.
[63] X.-S. Yang, “Firefly Algorithms,” in Nature-Inspired Optimization Algorithms, Elsevier, 2014, pp. 111–127.
[64] K. F. Man, K. S. Tang, and S. Kwong, “Genetic algorithms: Concepts and applications,” IEEE Trans. Ind. Electron., vol. 43, no. 5, pp. 519–534, 1996.
[65] J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942–1948.
[66] T. Bhattacharyya, B. Chatterjee, P. K. Singh, J. H. Yoon, Z. W. Geem, and R. Sarkar, “Mayfly in Harmony: A New Hybrid Meta-Heuristic Feature Selection Algorithm,” IEEE Access, vol. 8, pp. 195929–195945, Oct. 2020.
[67] A. Sadollah, H. Sayyaadi, and A. Yadav, “A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm,” Appl. Soft Comput., vol. 71, pp. 747–782, 2018.
[68] S. Zhao, T. Zhang, S. Ma, and M. Chen, “Dandelion Optimizer: A nature-inspired metaheuristic algorithm for engineering applications,” Eng. Appl. Artif. Intell., vol. 114, 2022.
[69] J. Joo, M. C. Park, D. S. Han, and V. Pejovic, “Deep learning-based channel prediction in realistic vehicular communications,” IEEE Access, vol. 7, pp. 27846–27858, 2019.
[70] R. Changalvala and H. Malik, “LiDAR data integrity verification for autonomous vehicle,” IEEE Access, vol. 7, pp. 138018–138031, 2019.
[71] K. Bahirat and B. Prabhakaran, “A study on lidar data forensics,” in 2017 IEEE International Conference on Multimedia and Expo (ICME), 2017.

[72] X. Wang, J. H. Park and H. Li, "Fuzzy Secure Event-Triggered Control for Networked Nonlinear Systems Under DoS and Deception Attacks," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, doi: 10.1109/TSMC.2023.3240401.
[73] L. Zhang, Y. Chen, and M. Li, “Resilient predictive control for cyber–physical systems under denial-of-service attacks,” IEEE Trans. Circuits Syst. II Express Briefs, vol. 69, no. 1, pp. 144–148, 2022.
[74] S. Prasad, “Counteractive control against cyber‐attack uncertainties on frequency regulation in the power system,” IET Cyber-Phys. Syst. Theory Appl., vol. 5, no. 4, pp. 394–408, 2020.
[75] T. Aljohani, A.Almutairi, "Modeling time-varying wide-scale distributed denial of service attacks on electric vehicle charging Stations," Ain Shams Engineering Journal, Volume 15, Issue 7, 2024.
[76] E. Hodo et al., “Threat analysis of IoT networks using artificial neural network intrusion detection system,” in 2016 International Symposium on Networks, Computers and Communications (ISNCC), 2016, pp. 1–6.
[77] R. Y. Choi, A. S. Coyner, J. Kalpathy-Cramer, M. F. Chiang, and J. P. Campbell, “Introduction to machine learning, neural networks, and deep learning,” Transl. Vis. Sci. Technol., vol. 9, no. 2, p. 14, 2020.
[78] J. D. Rojas, O. Arrieta, and R. Vilanova, Industrial PID controller tuning: With a multiobjective framework using MATLAB (R), 1st ed. Cham, Switzerland: Springer Nature, 2022.
[79] R. P. Borase, D. K. Maghade, S. Y. Sondkar, and S. N. Pawar, “A review of PID control, tuning methods and applications,” Int. J. Dyn. Contr., vol. 9, no. 2, pp. 818–827, 2021.
[80] S. W. Sung and I.-B. Lee, “Limitations and countermeasures of PID controllers,” Ind. Eng. Chem. Res., vol. 35, no. 8, pp. 2596–2610, 1996.
[81] A. De Luca, G. Oriolo, and M. Vendittelli, “Control of wheeled mobile robots: An experimental overview,” in Ramsete, Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, pp. 181–226.
[82] Y. Kanayama, Y. Kimura, F. Miyazaki, and T. Noguchi, “A stable tracking control method for an autonomous mobile robot,” in Proceedings., IEEE International Conference on Robotics and Automation, 2002.
[83] D. P. Atherton and S. Majhi, “Limitations of PID controllers,” in Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 1999.
[84] A. Hashim, “Optimal speed control for direct Current motors using Linear Quadratic Regulator,” Journal of Engineering and Computer Science (JECS), vol. 14, no. 2, pp. 48–56, 2019.
[85] “Industrial IoT Plattform Elements for IoT,” Contact-software.com. [Online]. Available: https://www.contact-software.com/de/produkte/industrial-iot-plattform-elements-for-iot/.
[86] S. Chandramohan and M. Senthilkumaran, “Intelligent automatic guided vehicle for smart manufacturing industry,” in Springer Proceedings in Materials, Singapore: Springer Singapore, 2021, pp. 337–343.
[87] R. Yan, S. J. Dunnett, and L. M. Jackson, “Model-based research for aiding decision-making during the design and operation of multi-load automated guided vehicle systems,” Reliab. Eng. Syst. Saf., vol. 219, no. 108264, p. 108264, 2022.
[88] T. M. Aljohani, “Cyberattacks on energy infrastructures as modern war weapons—Part I: Analysis and motives,” IEEE Technol. Soc. Mag., vol. 43, no. 2, Jun. 2024, doi: 10.1109/MTS.2024.3395688.
[89] T. M. Aljohani, “Cyberattacks on energy infrastructures as modern war weapons—Part II: Gaps, standardization, and mitigation measures,” IEEE Technol. Soc. Mag., vol. 43, no. 2, Jun. 2024, doi: 10.1109/MTS.2024.3395697.
[90] A. Khalid, P. Kirisci, Z. H. Khan, Z. Ghrairi, K.-D. Thoben, and J. Pannek, “Security framework for industrial collaborative robotic cyber-physical systems,” Comput. Ind., vol. 97, pp. 132–145, 2018.