Muhammad Abdullah

A regenerative braking system is an advanced technology applicable to transportation, particularly electric vehicles. The purpose of incorporating regenerative braking is to recover energy during braking and deceleration, which can be stored in the battery. This paper aims to study the operation of the regenerative braking system based on an urban drive cycle. This study selects the US60 and NEDC drive cycles as inputs to evaluate future powertrain systems and vehicle concepts. The output torque is calculated longitudinally based on the vehicle dynamic equation to determine whether the torque is negative or positive. When the torque is negative, regenerative braking applies, and the state of charge (SoC) of the battery increases. The concept of regenerative braking is that this system uses four MOSFETs as switches. As a result, at the 50% level of SoC, the first regeneration improved performance by 12.22%, whereas the second showed a smaller gain of 5.96%. Similarly, at the 80% level of SoC, the first regeneration yielded a 12.55% increase, while the second achieved only a 6.19% improvement. The rise in SoC for both levels demonstrates that energy can be recovered when implementing regenerative braking. Therefore, the results obtained from the MATLAB simulation will be used for future studies in implementing a regenerative braking control strategy. ABSTRAK: Sistem brek jana semula adalah teknologi canggih yang digunakan untuk pengangkutan, terutamanya kenderaan elektrik. Tujuan menggabungkan brek jana semula adalah bagi memulihkan tenaga semasa brek dan nyahpecutan, yang boleh disimpan dalam bateri. Kajian ini bertujuan bagi mengkaji operasi sistem brek jana semula berdasarkan kitaran pacuan bandar. Dalam kajian ini, kitaran pemacu US60 dan NEDC dipilih sebagai input bagi menilai sistem powertrain dan konsep kenderaan masa hadapan. Tork keluaran dikira berdasarkan persamaan dinamik membujur kenderaan bagi menentukan tork negatif atau positif. Apabila tork negatif, brek jana semula terpakai, dan keadaan cas (SoC) bateri meningkat. Konsep brek sistem jana semula ini menggunakan empat MOSFET sebagai suis. Hasilnya, pada tahap 50% SoC, penjanaan semula pertama meningkatkan prestasi sebanyak 12.22%, manakala tahap kedua menunjukkan kenaikan lebih kecil iaitu 5.96%. Begitu juga, pada tahap 80% SoC, penjanaan semula pertama menghasilkan peningkatan 12.55%, manakala yang kedua hanya mencapai peningkatan 6.19%. Peningkatan SoC bagi kedua-dua tahap menunjukkan bahawa tenaga boleh dipulihkan bagi melaksanakan brek jana semula. Oleh itu, dapatan kajian yang diperoleh dari simulasi MATLAB akan digunakan untuk kajian masa hadapan dalam melaksanakan strategi kawalan brek jana semula.

Buck converters are widely employed in power electronics for efficient DC voltage regulation, particularly in applications such as motor drives and embedded systems. However, conventional control methods, such as PID, often exhibit limitations including significant voltage ripple, overshoot, and sluggish dynamic response under varying load conditions. This study introduces a fuzzy logic controller (FLC) integrated into a buck converter system to address these challenges through adaptive and nonlinear control. The research contribution is the design and simulation of an FLC-based voltage regulation strategy that enhances output stability and improves transient performance in DC motor applications. The proposed buck converter operates in continuous conduction mode and consists of an IGBT switch, inductor, diode, and filter capacitor. The FLC employs voltage deviation and its rate of change as input variables and utilizes a 25-rule Mamdani fuzzy inference system to modulate the duty cycle in real time. Simulated in MATLAB Simulink with a dynamic DC motor load, the FLC demonstrates superior control characteristics over the PID controller. Most notably, voltage ripple is reduced by over 65%, leading to improved voltage stability and reduced fluctuations. The FLC also exhibits faster settling behavior and better handling of dynamic load variations, confirming its effectiveness in nonlinear and time-varying systems. Future work will focus on hardware validation, hybrid control integration, and deployment in renewable energy and electric vehicle systems to improve adaptability and real-world performance.

This paper presents a formulation and analysis of a low computation Predictive Functional Control (PFC), which is a simplified version of the more advanced Model Predictive Control (MPC) for an Adaptive Cruise Control (ACC) system by using a representation of first order closed-loop transfer function. In this work, a non-linear mathematical model of vehicle longitudinal dynamics is considered as a control plant. Then, a simple Proportional Integral (PI) controller is employed as an inner loop to identify the first-order relationship between its actual and desired trajectory speed according to the reasonable time constant based on the logical response of pedals pressing. To directly control the whole plant, the PFC is formulated as an outer loop to track the desired speed together with the convergence rate based on a user preference while satisfying constraints related to acceleration and safe distancing. Since PFC is formulated based on the first-order transfer function, the prediction and tuning processes are straightforward and specific to this system. The simulation results confirm that the proposed controller managed to track the desired speed while maintaining a comfortable driving response. Besides, the controller also can retain safe distancing during the car following application, even in the presence of unmeasured disturbance. In summary, this framework can avoid the need to formulate an inverse non-linear model that is typically used when deploying a hierarchical control structure to compute the throttle and brake pedals pressing as it has been replaced with an inner loop PI controller. The performance also is comparable yet more conservative due to the simplification. These findings can become a good reference for designing and improving the ACC controller, as the framework can be easily generalized for any type of vehicle for future work.

Longitudinal dynamics control is one of the essential tasks for an autonomous vehicle, where it deals with speed regulation to ensure smooth and safe operations. To design a good controller, a simple yet reliable mathematical model is needed so that it can be used as a plant and to tune the controller. Although there are many types of mathematical models available in the literature, finding the right one for control application is essential. The model cannot be too complex and can be too simple. Thus, the main objective of this work is to derive a simple yet reliable vehicle longitudinal model so that it can be used as a simulation plant in MATLAB Simulink to test or tune various types of control algorithm's performance. The model consists of three main parts which are the vehicle body dynamics, simplified power train dynamic, and braking dynamic. To validate the reliability of the model, standard urban drive cycles will be used as a reference speed and a hierarchical PID control structure with inverse plant model is used to control the pedal inputs replacing the driver in simulation environment. Results show that the controller managed to track the drive cycle with an acceptable pedal pressing response which is between 40% throttle press and 20% brake press that in line with the normal operation of a vehicle. Although only simulation result is presented, the model can be used as a good starting point for further development and testing of different types of control algorithms for future work.

A recent study demonstrated that the use of feedforward information with conventional Predictive Functional Control (PFC) leads to unexpected inconsistencies, with subsequent negative impacts on tuning and behaviour. A proposal was made to define the coincident point differently and shown to reduce the lag in the closed-loop PFC responses and applied to some systems with benign dynamics. Other recent work has looked at parameterisations of the future input to deal with challenging open-loop dynamics and significantly extended the range of problems for which PFC can be effective. This paper combines the two concepts, and thus proposes an algorithm that has both more effective and simple tuning than original PFC as well as being applicable to a range of challenging dynamics.

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.

A power converter is one of the important components in a hybrid electric vehicle (HEV), where it has a strong nonlinear dynamic due to the variation of load demand from different driving modes, namely acceleration, braking and cruising. To adapt with the nonlinearities, this work proposes the use of direct model reference adaptive control (DMRAC) to regulate its operation in tracking the load and current demand of the HEV. To validate the response, the control performance is benchmarked with the commonly used traditional PI controller. The system model includes a battery with a supercapacitor, and its controller was constructed using the MATLAB Simulink platform. Simulation results show that DMRAC provides better performance as compared to the PI controller in two cases, which are tracking the current and load demands according to the root mean square error (RMSE) analysis. Nevertheless, in the presence of disturbance, it is noted that DMRAC is only effective in tracking the current demand while requiring some time to adapt and surpass the PI controller in tracking the load demand. Based on these findings, it can be justified that the DMRAC has the potential to become a good alternative approach to control the HEV power converters, specifically in the presence of disturbance.

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.

Stack temperature and airflow rate are vital control problems for Proton Exchange Membrane fuel cell (PEMFC). Two separate Model Predictive Controllers (MPC) have been employed to regulate these problems. The controllers utilized Laguerre and exponential weight functions to reduce its numerical instability and computational time. The temperature MPC considered delayed and constrained coolant pump voltage as manipulated input and stack temperature as the desired output. While airflow MPC manipulated compressor motor voltage to maintain the desired level of oxygen excess ratio subjected to starvation, surge, and choke constraints. Results showed that both controllers worked well together. The desired temperature and oxygen excess ratio were maintained subjected to all the constraints, even with the presence of external disturbances. This study highlights that MPC manages to handle both control problems without any conflict. Yet, it also proves that MPC can handle a large time delay process.