Neuro-Fuzzy Modelling and Stable PD Controller for Angular Position in Steering Systems

Ramos-Fernández, Julio C.; López-Moralès, Virgilio; Márquez-Vera, Marco Antonio; Pérez, Juan Manual Xicotencatl; Suárez-Cansino, Joel

doi:10.1007/s12239-021-0129-9

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…This process involves identifying and defining appropriate membership functions to represent the relationships between input and output variables [40]. The ANFIS is based on the Takagi-Sugeno fuzzy system method [41], where the final fuzzy inference system is optimized by training an ANN.…”

Section: Anfis Controllermentioning

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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Section: Anfis Controllermentioning

confidence: 99%

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

Kern,

Marrero,

Urrea

2023

Processes

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“…In general, the fuzzy control logic has two main approaches: (1) Mamdani [45] and (2) Takagi-Sugeno [46]. The basis of ANFIS as an adaptive network-based fuzzy system is the Takagi-Sugeno fuzzy system method [37,47]. Its inference system corresponds to a set of fuzzy IF-THEN rules that have a learning ability to approximate non-linear functions.…”

Section: Anfismentioning

confidence: 99%

An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System

et al. 2022

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In recent decades, one of the scientists’ main concerns has been to improve the accuracy of satellite attitude, regardless of the expense. The obvious result is that a large number of control strategies have been used to address this problem. In this study, an adaptive neuro-fuzzy integrated system (ANFIS) for satellite attitude estimation and control was developed. The controller was trained with the data provided by an optimal controller. Furthermore, a pulse modulator was used to generate the right ON/OFF commands of the thruster actuator. To evaluate the performance of the proposed controller in closed-loop simulation, an ANFIS observer was also used to estimate the attitude and angular velocities of the satellite using magnetometer, sun sensor, and data gyro data. However, a new ANFIS system was proposed that can jointly control and estimate the system attitude. The performance of the proposed controller was compared to the optimal PID controller in a Monte Carlo simulation with different initial conditions, disturbance, and noise. The results show that the proposed controller can surpass the optimal PID controller in several aspects including time and smoothness. In addition, the ANFIS estimator was examined and the results demonstrate the high ability of this designated observer. Consequently, evaluating the performance of PID and the proposed controller revealed that the proposed controller consumed less control effort for satellite attitude estimation under noise and uncertainty.

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“…In existing designs of active steering controllers for vehicles, control algorithms like PID controllers [10][11][12][13], H∞ controllers [14,15], sliding mode controllers [16], and neural-Processes 2023, 11, 2677 2 of 25 network-based controllers [17,18] have all been studied. Ahn [10] adopted a centering control approach, designed a PI controller, and conducted active steering control experiments on a small-scale roller rig using DIRWs driven by surface permanent magnet synchronous motors (SPMSMs).…”

Section: Introductionmentioning

confidence: 99%

“…Ape-X DDPG first computes the priority of samples based on their Temporal Difference (TD) error and then employs Prioritized Weighted Exponential Normalization Sampling (PWENS) as the sampling probability, ensuring a more balanced sampling mechanism. In line with the DDPG algorithm, the TD error, y i , for any sample i can be calculated as shown in Equation (18).…”

Section: Introduction Of Prioritized Experience Replaymentioning

confidence: 99%

Active Steering Controller for Driven Independently Rotating Wheelset Vehicles Based on Deep Reinforcement Learning

Lu,

Wei,

Wang

2023

Processes

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This paper proposes an active steering controller for Driven Independently Rotating Wheelset (DIRW) vehicles based on deep reinforcement learning (DRL). For the two-axle railway vehicles equipped with Independently Rotating Wheelsets (IRWs), each wheel connected to a wheel-side motor, the Ape-X DDPG controller, an enhanced version of the Deep Deterministic Policy Gradient (DDPG) algorithm, is adopted. Incorporating Distributed Prioritized Experience Replay (DPER), Ape-X DDPG trains neural network function approximators to obtain a data-driven DIRW active steering controller. This controller is utilized to control the input torque of each wheel, aiming to improve the steering capability of IRWs. Simulation results indicate that compared to the existing model-based H∞ control algorithm and data-driven DDPG control algorithm, the Ape-X DDPG active steering controller demonstrates better curving steering performance and centering ability in straight tracks across different running conditions and significantly reduces wheel–rail wear. To validate the proposed algorithm’s efficacy in real vehicles, a 1:5 scale model of the DIRW vehicle and its digital twin dynamic model were designed and manufactured. The proposed control algorithm was deployed on the scale vehicle and subjected to active steering control experiments on a scaled track. The experimental results reveal that under the active steering control of the Ape-X DDPG controller, the steering performance of the DIRW scale model on both straight and curved tracks is significantly enhanced.

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Neuro-Fuzzy Modelling and Stable PD Controller for Angular Position in Steering Systems

Cited by 7 publications

References 16 publications

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

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

An Adaptive Neuro-Fuzzy Model for Attitude Estimation and Control of a 3 DOF System

Active Steering Controller for Driven Independently Rotating Wheelset Vehicles Based on Deep Reinforcement Learning

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