An adaptive neuro-fuzzy with nonlinear PID controller design for electric vehicles

Hasan, Mustafa Wassef; Mohammed, Ammar Sami; Noaman, Saja Faeq

doi:10.1016/j.ifacsc.2023.100238

Cited by 3 publications

(2 citation statements)

References 69 publications

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“…According to Equation (24), the Rouse criterion table shows that the elements of the first column are all positive and the coefficients of the characteristic equations of the system are all positive, so the system is determined to be stable.…”

Section: Transfer Function Of a Free-fall Hook Systemmentioning

confidence: 99%

“…However, the free-fall hook system needs to have the ability to complete fast and flexible braking under large inertia load conditions, and it is necessary to take into account the characteristics of the free-fall hook working conditions as well as the characteristics of the system. Therefore, this paper uses the free-fall hook system proportional pressure-reducing valve, clutch, and other key components of the model to clarify if the nonlinear characteristics of the free-fall hook system, combined with the fuzzy PID control method that does not depend on the model accuracy, are suitable for solving the nonlinear, strong coupling timevarying, hysteresis and other problems of the characteristics of the problem [23][24][25][26], and the rapidity of the feedforward control. An adaptive control strategy for flexible and fast braking of the free-fall hook system under a large inertia load is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process

Gao,

Song,

Yang

et al. 2024

Processes

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Due to the large inertia and strong impact accompanying the free-falling hook process of crawler cranes, it is difficult to meet the demand for flexible and smooth braking control under different weight load conditions. Therefore, this paper takes the free-fall hook system as the research object and combines system operation characteristics and control theory to carry out research on flexible braking control of the free-fall hook system. Firstly, a joint simulation platform of MATLAB (version 2018b) and AMESim (version 2019.1) software is built to theoretically analyze the key components of the free-fall hook system (proportional pressure-reducing valve, winch reducer, and wet clutch). Secondly, a mathematical model of the braking process is established, and the pressure control demand is clarified to analyze the reasons for the existence of dead zones and hysteresis loops in the system. Meanwhile, it is found that the dead zones and hysteresis loops existing in the pressure output of the pressure-reducing valve are the main factors of flexibility with load braking. Then, in this paper, a closed-loop control strategy is formulated based on the automatic adaptation of the braking gear in combination with the fuzzy PID pressure. Finally, the effectiveness of the control strategy proposed in this paper is verified with simulation and experimental testing using the pressure hysteresis loop of the free-fall hook process and the load-braking acceleration as the judging criteria. The results show that the system pressure hysteresis loop is reduced by 50%–60% and the maximum braking acceleration is reduced by 24%–30% under the conditions of 6.44 tonnes and 10.44 tonnes, which improves the accuracy of pressure control and achieves flexible and smooth braking with loads for different tonnages of free-fall hooks.

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Section: Transfer Function Of a Free-fall Hook Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process

Gao,

Song,

Yang

et al. 2024

Processes

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Stabilizing Electric Vehicle Systems Using Proximal Policy-Based Self-structuring Control

Zhuang,

Wang,

Cheng

et al. 2024

Int.J Automot. Technol.

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An active disturbance rejection control (ADRC) has been developed for stabilizing electric vehicle (EV) systems without the need for model identification. The proximal policy optimization (PPO) algorithm, along with actor and critic neural networks, has been used to fine-tune the adjustable parameters of the ADRC controller to achieve optimal performance in a specific case study. The architecture of PPO implements separate neural networks and ameliorates the PPO adaptability to handle continuous action spaces. By maximizing a reward function based on system output, the PPO agent optimally tunes the gains to reduce undesired speed fluctuations of EVs and improve system stability. Performance evaluation under the new European driving cycle and federal test procedure has been conducted to examine the feasibility of the suggested controller. The disturbance rejection capability of the ADRC controller designed by the PPO algorithm has been tested and compared with prevalent control methodologies. Moreover, real-time examinations of the dynamic behavior of EV systems have been made to identify the capability of the suggested controller in real-world hardware. The results show that the suggested controller outperforms other designed controllers in terms of transient behavior and numerical performance metrics.

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Energy saving and speed control in autonomous electric vehicle using enhanced manta ray foraging algorithm optimized intelligent systems

Manavaalan,

Gobikannan,

Elango

et al. 2024

Journal of Power Sources

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An adaptive neuro-fuzzy with nonlinear PID controller design for electric vehicles

Cited by 3 publications

References 69 publications

Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process

Research on Flexible Braking Control of a Crawler Crane during the Free-Fall Hook Process

Stabilizing Electric Vehicle Systems Using Proximal Policy-Based Self-structuring Control

Energy saving and speed control in autonomous electric vehicle using enhanced manta ray foraging algorithm optimized intelligent systems

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