Optimum fuzzy logic controller applied to a hybrid hydraulic vehicle to minimize fuel consumption and emissions

Eckert, Jony Javorski; Barbosa, Társis Prado; Silva, Fabrício Leonardo; Roso, Vinícius Rückert; Silva, Ludmila C. A.; Silva, Leonardo Adolpho Rodrigues da

doi:10.1016/j.eswa.2022.117903

Cited by 27 publications

(9 citation statements)

References 54 publications

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“…Nevertheless, they have not been yet carried out for real vehicles. Therefore, the introduction of optimization algorithms into powertrain controllers like those in the literatures [38], [39] and the verification using an actual test vehicle will be our future tasks.…”

Section: B Comparison Study and Quantitative Performance Analysismentioning

confidence: 99%

Novel Powertrain Vibration Controller With Six Rules-Based Fuzzy Inference for Time-Fluctuated Control Period

Yonezawa,

Kajiwara

2024

IEEE Access

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To ensure comfort and longevity of vehicle components, it is important to suppress lowfrequency transient vibrations in the powertrain. This study proposes a unique technique for active vibration control that incorporates six rules-based fuzzy logic compensation. The technique addresses changes in control periods of an actuator over time. Firstly, a model prediction technique is used with a sampled-data controller (SDC) to address the highest possible phase delay in the control input caused by the fluctuating control period. In addition, fuzzy sets are utilized to define the changing renewal timings of the control input, which are distinct from the regular timings used by the periodically operated SDC. These fuzzy sets are named "Nearly previous timing" and "Nearly upcoming timing". This study employs six inference rules to achieve fuzzy compensation that resembles human intuition. These rules utilize output variables defined by linguistic fuzzy sets, such as "Similar to commands from SDC" and "Very similar to commands from SDC", making the inference process more flexible. Due to the utilization of the fuzzy sets and periodic control signals provided by the SDC, it is possible to reasonably deduce unknown control inputs at various update timings. To evaluate the effectiveness of the control scheme, simulations and experiments are conducted using an actual test setup to investigate its damping performance. The experimental findings indicate that the new active damping technique effectively minimizes transient powertrain vibrations. Furthermore, comparative studies with previous control systems indicate the improved performance and robustness of the proposed approach.

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Section: B Comparison Study and Quantitative Performance Analysismentioning

confidence: 99%

Novel Powertrain Vibration Controller With Six Rules-Based Fuzzy Inference for Time-Fluctuated Control Period

Yonezawa,

Kajiwara

2024

IEEE Access

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“…Yang et al [37] designed and optimized a similar system combining an electric motor and a piston pump, achieving an even better reduction in power consumption, up to 21.17%. Eckert carried out a series of studies on fuzzy logic applications in hybrid vehicle power distribution systems to minimize energy demands and decrease emissions [38,39].…”

Section: Hydraulic Flow Control Valves and Servo Valvesmentioning

confidence: 99%

A Review of Fuzzy Logic Method Development in Hydraulic and Pneumatic Systems

Filo

2023

Energies

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Fuzzy logic has been developed since the 1960s. Research related to fuzzy logic application in hydraulics and pneumatics is mainly aimed at energy demand reduction and improvement in operational characteristics. This article summarizes the recent achievements in hydraulic and pneumatic fuzzy logic system design. First, the main application areas have been identified, including control and fault diagnosis. The control systems were additionally grouped according to the main objects of study, such as pumps, actuators, proportional valves, etc. Then, the results of the recent research were presented, and the main features of the designed fuzzy logic units were summarized for each group. Particular attention was paid to types of membership functions used for fuzzification and defuzzification, numbers of fuzzy sets defined for input and output signals, types of fuzzy operators, the applied inference algorithm and the defuzzification method. Based on the analysis of the listed parameters, conclusions were formulated regarding advantages, main issues and difficulties, as well as recommended directions for further development.

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“…In these systems, fuzzy logic is employed to define inference rules and membership functions, facilitating reasoning about data and decision-making. Expert knowledge is encapsulated in the form of IF-THEN rules, which proves to be better adapted to the analyzed problem [8].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

Kern,

Marrero,

Urrea

2023

Processes

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This research delves into the development and evaluation of two distinct controllers for a 3-DoF robotic arm in the context of Industry 4.0. Two primary control strategies are presented in the study. The first is a Fuzzy Logic Controller that utilizes joint position error and its derivative as inputs, employing a set of 9 control knowledge rules. The second is an Adaptive Neuro-Fuzzy Inference System (ANFIS) Controller, trained to learn the inverse dynamic model of the robot through a structured dataset. The research emphasizes the importance of accurate parameter tuning and data acquisition to achieve optimal control system performance. Extensive experimentation was conducted to evaluate the controllers’ performance in trajectory tracking and their response against external disturbances, such as load variations. The controllers exhibited remarkable precision and proficiency in tracking reference trajectories, with minimal deviations, overshoots, or oscillations. A quantitative analysis using performance indices such as root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE) further confirmed the controllers’ effectiveness. Notably, the ANFIS Controller consistently outperformed the Fuzzy Logic Controller, demonstrating superior precision in trajectory tracking. The study underscored the importance of selecting the right control method and obtaining high-quality training data. Challenges in parameter tuning for Fuzzy Logic Controllers and potential time constraints in training ANFIS were discussed. The findings have significant implications for advancing robotic control systems, particularly in the era of Industry 4.0.

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Optimum fuzzy logic controller applied to a hybrid hydraulic vehicle to minimize fuel consumption and emissions

Cited by 27 publications

References 54 publications

Novel Powertrain Vibration Controller With Six Rules-Based Fuzzy Inference for Time-Fluctuated Control Period

Novel Powertrain Vibration Controller With Six Rules-Based Fuzzy Inference for Time-Fluctuated Control Period

A Review of Fuzzy Logic Method Development in Hydraulic and Pneumatic Systems

Fuzzy Control Strategies Development for a 3-DoF Robotic Manipulator in Trajectory Tracking

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