Performance Comparison Between Predictive Functional Control and PID Algorithms for Automobile Cruise Control System

Zainuddin, Mohamed Al-Sideque; Abdullah, Mohammad; Ahmad, Sultan; Tofrowaih, K.A.

doi:10.15282/ijame.19.1.2022.09.0728

Cited by 3 publications

(5 citation statements)

References 23 publications

(32 reference statements)

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“…In general, the inputs to the system will be the pedal pressure from the throttle and brake, while the output of interest will be velocity. In a traditional cruise control system, the https://doi.org/10.31436/iiumej.v24i1.2341 braking effect is not considered, thus the standard kinetic vehicle longitudinal model can be used straight away to derive the control law of PFC as presented in [18]. However, the ACC system needs braking input as well to keep the safe distancing with a lead vehicle.…”

Section: Vehicle Longitudinal Dynamics Modellingmentioning

confidence: 99%

“…However, the ACC system needs braking input as well to keep the safe distancing with a lead vehicle. If a full kinetic modelling is used to derive the control law as in [18], the constraint implementation will be quite complicated especially if it is designed for full range dynamic operation where the nonlinearities will be very high due to the changing gear ratio and coefficient of friction between road and tire [2]. Thus, a hierarchal control strategy is often proposed in the literature [5,6] as shown in Fig.…”

Section: Vehicle Longitudinal Dynamics Modellingmentioning

confidence: 99%

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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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Section: Vehicle Longitudinal Dynamics Modellingmentioning

confidence: 99%

Section: Vehicle Longitudinal Dynamics Modellingmentioning

confidence: 99%

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

Zainuddin¹,

Abdullah²,

Ahmad³

et al. 2023

IIUMEJ

Self Cite

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“…Indeed, many efforts are proposed to reduce the computation load of MPC either by modifying the algorithm or the control structure [16]. Several authors have demonstrated by utilizing the Laguerre function in the control law, where a large control horizon can be represented using a smaller horizon to reduce the computation load [9]. Nevertheless, an additional tuning parameter is needed.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, several works have proposed using Predictive Functional Control (PFC), a simplified MPC subvariant, to design an ACC system [9][10]. PFC uses a similar concept to MPC, except that it minimizes a single target error with direct inversion of equality between target trajectory and output prediction at a specific coincidence horizon instead of using a quadratic cost function.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the controller only needs minimum computation effort, yet due to the simplicity of the objective, one can argue that the control input is suboptimal. However, for the single input single output application, the performance of PFC is acceptable considering its low computation time and often giving a better response than the traditional PID controller [9]. Nevertheless, there is a lack of clarity over which method should be used with the PFC, either the centralized or hierarchical structures, where both approaches have their advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%

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Performance Comparison Between Centralized and Hierarchical Predictive Functional Control for Adaptive Cruise Control Application

Abdullah,

Zainuddin,

Ahmad

et al. 2023

Int. J. Automot. Mech. Eng.

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This paper presents a performance comparison between two different control structures of Predictive Functional Control (PFC), namely centralized and hierarchical architectures for an Adaptive Cruise Control (ACC) system. Centralized PFC utilizes a linearized model of the vehicle longitudinal dynamics to compute the control law, while hierarchical PFC uses a simple kinematic model with inverse vehicle longitudinal dynamics. Based on the simulation results, both control structures produced a satisfactory performance. However, in the presence of disturbances such as road slopes and wind gusts, the centralized PFC becomes more sensitive and conservative than the hierarchical PFC. The main reason is that the linearization model used in centralized PFC is only effective around the selected nominal operating points. Since the decision-making process depends on the internal model performance, centralized PFC cannot compensate for this effect. Besides, it also takes a longer computation time than hierarchical PFC since more mathematical operations must be solved than the kinematic model, especially when physical and operational constraints are considered. The standard performance parameters such as Root Mean Squared Error (RMSE), settling time, and rise time are also used for the analysis. These findings can become a solid justification for using the hierarchical PFC structure in designing an ACC system for future work.

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Simulation of Automatic Speed Control in Motor Vehicles (Cruise Control) Using Simulink PID Controller

Desmira,

Hamid,

Darmawan

et al. 2024

AMM

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An adaptive control system is an advanced method for controlling the speed of a moving motorized vehicle. Using this intelligent control system, the driver can easily control the speed of the car according to his wishes or the prevailing situation. The adaptive control system consists of a sensor attached to a moving vehicle which then registers the speed of the car and provides input to the processing unit. The controller is designed according to the force exerted by the car to drive a certain distance in a certain time. This time, the control uses a PID controller. This method is followed for various tunings of Kp, Ki, and Kd values for the P, PI, PID, and IPD structures for a cruise control system using MATLAB. The PID used in this experiment is intended to control the speed to make it more stable and optimal.

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Performance Comparison Between Predictive Functional Control and PID Algorithms for Automobile Cruise Control System

Cited by 3 publications

References 23 publications

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

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

Performance Comparison Between Centralized and Hierarchical Predictive Functional Control for Adaptive Cruise Control Application

Simulation of Automatic Speed Control in Motor Vehicles (Cruise Control) Using Simulink PID Controller

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