A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots

Dang, Thai Son; Duong, Dinh Tu; Le, Van Chuong; Banerjee, Santo

doi:10.1007/s40430-020-02767-8

Cited by 47 publications

(25 citation statements)

References 38 publications

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“…Sato et al [28] proposed a steering control system comprising of online reinforcement learning and fuzzy expert system for the motion control of the self-driving cars. The proposed system based on control law which derived directly from a real vehicle, the derived control law allows the vehicle to navigate on different types of lanes.…”

Section: B Steering Controllermentioning

confidence: 99%

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Rasib¹,

Butt²,

Riaz³

et al. 2021

IEEE Access

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With the recent emergence of deep learning, computer vision-based applications have demonstrated better applicability in accomplishing driving tasks including drivable road region detection, lane keeping and steering control in self-driving vehicles. Till recently, numerous lane-marking detection based steering control and lane keeping methods have been proposed to perform autonomous driving on urban well-structured roads. But the matter of fact is that these methods are not feasible on roads where lane markings are not available or faded over time which makes drivable road region detection a crucial task. Also, it is highly difficult task to estimate the steering angle on such highly deteriorated roads using existing road detection and steering angle estimation methods. To the best of our knowledge, there is no standard benchmark available for drivable road region detection and steering angle estimation on unstructured roads. To this end, we present a large-scale dataset for drivable road detection, comprising of 15,000-pixel level high-quality fine annotations. Also, we present a novel end-to-end drivable road region detection and steering angle estimation method to ensure the autonomous driving on unstructured roads. The proposed method performs pixel-level segmentation to extract drivable road region and calculates the lane interception to estimate the steering angle of self-driving vehicles. Lastly, a comprehensive qualitative and quantitative analysis has been carried out to demonstrate the effectiveness of our proposed dataset, road detection and steering angle estimation method. Our benchmark is available at https://carl-dataset.github.io/index/.

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Section: B Steering Controllermentioning

confidence: 99%

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Rasib¹,

Butt²,

Riaz³

et al. 2021

IEEE Access

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show abstract

“…The second trajectory is a circle-shaped path calculated using (24) and (25). The symbol 𝑎 also denotes the radius of the circle.…”

Section: 𝑥(𝑡) = Sin 𝑡mentioning

confidence: 99%

“…3, December 2021, pp. 368-379 Path Tracking and Position Control of Nonholonomic Differential Drive Wheeled Mobile Robot (Muhammad Auzan) 𝑥 = sin(𝑡) 𝑎(24)𝑦 = cos(𝑡) 𝑎(25)…”

mentioning

confidence: 99%

Path Tracking and Position Control of Nonholonomic Differential Drive Wheeled Mobile Robot

Auzan

Hujja

Fuadin

et al. 2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Differential drive wheeled mobile robot (DDWMR) is one example of a robot with a constrained movement, Multiple Input Multiple Output (MIMO), and nonlinear system. Designing a low resource position and heading controller using linear MIMO methods such as LQR became a problem because of the linearization of robot dynamics at zero value. One of the solutions is to design a MIMO controller using a Single Input Single Output (SISO) controller. This work design a controller using PID for DDWMR Jetbot and selects the best feedback gain using different scenarios. The designed controller manipulates both motors by using calculated control signal to achieve a complex task such as path tracking with robot position in x-Axis, y-Axis, and heading angle as the feedback. The priority between position and heading angle can be adjusted by changing three feedback gains. The controller was tested, and the best gain was selected using Integral Absolute Error (IAE) metrics in a path tracking task with four different path shapes. The proposed methods can track square, circle, and two types of infinity shape paths, with the less well-formed shape being the four edges square path.

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“…The first group of research focuses on models. Proportional-integral-derivative (PID) controller has advantages due to its relatively simple formula (Zhao, 2012; Mai et al , 2021). However, the PID controller is more sensitive to errors, and the system stability is poor.…”

Section: Introductionmentioning

confidence: 99%

An improved pure pursuit path tracking control method based on heading error rate

Wang

Chen

Wang

2022

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Purpose In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]). Design/methodology/approach First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error. Findings The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality. Originality/value An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.

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A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots

Cited by 47 publications

References 38 publications

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Path Tracking and Position Control of Nonholonomic Differential Drive Wheeled Mobile Robot

An improved pure pursuit path tracking control method based on heading error rate

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