Design and Comparison of AFS Controllers with PID, Fuzzy-Logic, and Sliding-Mode Controllers

Song, Jeonghoon

doi:10.1155/2013/401548

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…On the one hand, many previous works have proven that neural network-based disturbance estimators considerably improves the performance of many controllers [75]. On the other hand, Fuzzy control has been suggested as suitable for complex robots, whose models cannot be easily established from a mathematical point of view [76]. Moreover, a Fuzzy-PID controller has been recently proposed to control a spherical robot with excellent performance [47].…”

Section: B Comparison Between the Proposed Methods And Fuzzy-pid Contmentioning

confidence: 99%

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

et al. 2020

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We develop a new robust control scheme for a non-holonomic spherical robot. To this end, the mathematical model of a pendulum driven non-holonomic spherical robot is first presented. Then, a recurrent neural network-based robust nonsingular sliding mode control is proposed for stabilization and tracking control of the system. The designed recurrent neural network is applied to approximate compound disturbances, including external interferences and dynamic uncertainties. Moreover, the controller is designed in a way that avoids the singularity problem in the system. Another advantage of the proposed scheme is its ability for tracking control while there exists control input saturation, which is a serious concern in robotic systems. Based on the Lyapunov theorem, the stability of the closed-loop system has also been confirmed. Lastly, the performance of the proposed control technique for the uncertain system in the presence of an external disturbance, unknown input saturation, and dynamic uncertainties has been investigated. Also, the proposed controller has been compared with a Fuzzy-PID one. Simulation results show the effectiveness and superiority of the developed control technique.

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Section: B Comparison Between the Proposed Methods And Fuzzy-pid Contmentioning

confidence: 99%

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

et al. 2020

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“…Moreover, the application of the control systems to prevent the vehicle's yaw instability and its lateral dynamic improvement is considerably increasing. 1–12 Song 13 to increase the vehicle's manoeuvrability designed a control system based on proportional integral derivative, fuzzy and sliding mode control methods. The controller was developed based on a two-degrees-of-freedom (DOFs) linear dynamic model.…”

Section: Introductionmentioning

confidence: 99%

A new robust combined control system for improving manoeuvrability, lateral stability and rollover prevention of a vehicle

Saeedi

2019

Proceedings of the Institution of Mechanical Engineers, Part K:

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This paper presents a new effective method in order to achieve an appropriate performance for a four-wheeled vehicle during different conditions. The main goal of the study is focused on the handling improvement and lateral stability increment of the vehicle using a robust combined control system. First, in order to increase the vehicle's manoeuvrability, an active steering control system is proposed based on the sliding mode control method and using the simplified dynamic model. The tracking of the desired values of the yaw rate and lateral velocity of the vehicle is the main purpose for using the controller. Also, in order for verifying the performance of the sliding mode controller, the linearization feedback control method is used to design the active steering control system. Moreover, to improve the directional stability of the vehicle, a new active roll control system is proposed. In this control system, the roll angle is considered as the state variable as well as the active anti-roll-bar is utilized as an actuator to generate the roll moment. Then, a 14-degrees-of-freedom nonlinear dynamic model of the vehicle validated using CarSim software is utilized. Afterward, the performance of the designed combined control system is investigated at various velocities. The simulation results confirm that the combined control system has an important effect on vehicle's manoeuvrability improvement and its lateral stability increment, especially during severe transient manoeuvre.

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“…Vehicle handling stability control has always been the research focus in recent years. To maintain lateral stability and reduce the traffic accidents, vehicle stabilization controllers, such as electric stability program (ESP), 1 active steering control, [2][3][4][5] and antilock braking system (ABS), 6,7 have been maturely developed and have gradually become the standard equipment on cars. Among these controllers, the majority of researchers regard yaw rate and lateral velocity response as the most important assessment criteria of the vehicle handling performance, and the main controlling target is to track the desired values.…”

Section: Introductionmentioning

confidence: 99%

A feedback linearization controller combined with a data-driven subspace-based prediction method for vehicle handling stabilization

Zhang

Yang

Feng

2018

Proceedings of the Institution of Mechanical Engineers, Part C:

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Obtaining precise yaw rate and lateral velocity as well as developing a nonlinear controller becomes more and more essential for improving the vehicle handling performance. Different from traditional methods, a data-driven subspace-based prediction approach is introduced by integrating propagator with predictor-based subspace identification method in this paper. Based on an identifiable vehicle model, the prediction process is validated by standard road tests data. To employ this data-driven prediction method in the vehicle handling stabilization and solve the controlling problem of nonlinear lateral dynamic system, a feedback linearization controller based on the new piecewise tire model is elaborately developed. On account of that the one-step prediction output reduces the time delay between actuator and lateral dynamic response, the subspace-based controller can theoretically improve the vehicle handling performance. By road simulation results, the proposed feedback linearization controller combined with a data-driven subspace-based prediction method greatly enhances the handling performance and provides a more effective technique for both vehicle parameter estimation and handling stabilization.

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Design and Comparison of AFS Controllers with PID, Fuzzy-Logic, and Sliding-Mode Controllers

Cited by 12 publications

References 14 publications

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

Recurrent Neural Network-Based Robust Nonsingular Sliding Mode Control With Input Saturation for a Non-Holonomic Spherical Robot

A new robust combined control system for improving manoeuvrability, lateral stability and rollover prevention of a vehicle

A feedback linearization controller combined with a data-driven subspace-based prediction method for vehicle handling stabilization

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