Trajectory tracking of a mobile robot with frictions and uncertainties using hierarchical sliding‐mode under‐actuated control

Hwang, Chih‐Lyang; Wu, Hsiu-Ming

doi:10.1049/iet-cta.2012.0750

Cited by 44 publications

(36 citation statements)

References 26 publications

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“…After carrying out the review of the literature associated with design of controllers for the trajectory tracking task in differential drive WMRs, it was found that, generally, this task has been solved in three directions: (a) by only using the kinematic or dynamic model of the mechanical structure , (b) by employing the kinematic/dynamic model of the mechanical structure along with the dynamics of the actuators [85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102][103], and (c) by considering the kinematic model of the mechanical structure along with the dynamics of the actuators and power stage [104,105]. In the last direction, contributions that have carried out interesting efforts are [106][107][108][109][110][111][112][113][114].…”

Section: Discussion and Contributionmentioning

confidence: 99%

“…More recently, Luo et al [93] elaborated an adaptive neural network dynamic surface controller based on a disturbance observer, where uncertain parameters were taken into account. Lastly, other contributions that take into account the dynamic model of the mechanical structure and the dynamics of the actuators in the control design are reported in [94][95][96][97][98][99][100][101][102][103].…”

Section: Dynamic Modelmentioning

confidence: 99%

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Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

et al. 2017

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The trajectory tracking task in a wheeled mobile robot (WMR) is solved by proposing a three-level hierarchical controller that considers the mathematical model of the mechanical structure (differential drive WMR), actuators (DC motors), and power stage (DC/DC Buck power converters). The highest hierarchical level is a kinematic control for the mechanical structure; the medium level includes two controllers based on differential flatness for the actuators; and the lowest hierarchical level consists of two average controllers also based on differential flatness for the power stage. In order to experimentally validate the feasibility of the proposed control scheme, the hierarchical controller is implemented via a Σ-Δ-modulator in a differential drive WMR prototype that we have built. Such an implementation is achieved by using MATLAB-Simulink and the real-time interface ControlDesk together with a DS1104 board. The experimental results show the effectiveness and robustness of the proposed control scheme.

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Section: Discussion and Contributionmentioning

confidence: 99%

Section: Dynamic Modelmentioning

confidence: 99%

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

et al. 2017

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“…Further, the closed-loop system responses are compared with two baseline controllers, that is, a slidingmode controller and a PI controller combined with a parameter-varying Smith predictor. The sliding-mode controller u(t) can be designed as [22] …”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

Wu¹,

Tafreshi²

2017

Asian Journal of Control

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Minimization of emissions of carbon dioxide and harmful pollutants and maximization of fuel economy for lean-burn spark ignition (SI) engines relies to a large extent on precise air-fuel ratio (AFR) control. However, the main challenge of AFR control is the large time-varying delay in lean-burn engines. Since the system is usually subject to external disturbances and uncertainties, a high level of robustness in AFR control design must be considered. We propose a fuzzy sliding-mode control (FSMC) to track the desired AFR in the presence of periodic disturbances. The proposed method is model free and does not need any system characteristics. Based on the fuzzy system input-output data, two scaling factors are first employed to normalize the sliding surface and its derivative. According to the concept of the if-then rule, an appropriate rule table for the logic system is designed. Then, based on Lyapunov stability criteria, the output scaling factor is determined such that the closed-loop stability of the internal dynamics with uniformly ultimately bounded (UUB) performance is guaranteed. Finally, the feasibility and effectiveness of the proposed control scheme are evaluated under various operating conditions. The baseline controllers, namely, a PI controller with Smith predictor and sliding-mode controller, are also used to compare with the proposed FSMC. It is shown that the proposed FSMC has superior regulation performance compared to the baseline controllers.

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“…In addition, it requests the robot to be able to give as large acceleration as possible, which may be unsafe for robot movement. The other one that is sliding mode control [7][8][9]. This one has outstanding merit of robustness against structured and unstructured uncertainties, on the other hand, exploring uncertainty and determining the information about the uncertainty is difficult in practice.…”

Section: Figure 1 Wheeled-drive Mobile Robot Prototypementioning

confidence: 99%

Vision Based Autonomous Path/Line Following of a Mobile Robot Using a Hybrid Fuzzy PID Controller

Thuy¹,

Cường²

2016

Tuyển Tập Công Trình HNKH Toàn Quốc Lần Thứ 3 Về Điều Khiển &Amp; Tự Động Hoá VCCA - 2015

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Hội nghị toàn quốc lần thứ 3 về Điều khiển và Tự động hoá -VCCA-2015 VCCA-2015Rôbốt di động bám quỹ đạo tự động dựa trên xử lý ảnh sử dụng bộ điều khiển lai PID-mờ Tóm tắtTrong bài báo này, chúng tôi đề xuất một phương pháp điều khiển cho robốt tự hành để bám quỹ đạo cho trước trong môi trường trong nhà. Để bám được theo quỹ đạo, các kỹ thuật sử dụng logíc mờ với thông tin đầu vào từ hệ thống xử lý ảnh đã được nghiên cứu và áp dụng. Phương pháp điều khiển sử dụng trong hệ thống là sự kết hợp giữa bộ điều khiển lái -bộ điều khiển lai PID-mờ, và bộ điều khiển vận tốc tuyến tính -bộ điều khiển mờ. Để khẳng định tính đúng đắn của giải thuật điều khiển, chúng tôi đã tiến hành thử nghiệm trên một robốt tự hành, robốt thiết kế dựa trên cơ sở các linh kiện sẵn có trên thị trường. Thử nghiệm chỉ ra rằng, robốt sử dụng giải thuật chúng tôi đề xuất bám tốt theo quỹ đạo cho trước -bao gồm cả quỹ đạo cong và quỹ đạo thẳng. Từ khóa: Hệ thống điều khiển, robốt tự hành, hệ thống xử lý ảnh, logíc mờ, bộ điều khiển lái, bộ điều khiển tốc độ. AbstractIn this work, we propose a method for autonomous path/line following of differential-drive wheeled robot to track straight and curved paths in an indoor environment. In order to follow the path/line, fuzzy logic techniques are applied to generate movement with the information extracted from vision system. The control method in the system combines a steering controller, which is implemented by an intelligent hybrid fuzzy PID controller, controlling the steering angle of the robot, and fuzzy velocity controller, which controls the forward linear velocity with the purpose of safe tracking. We do experiments using an open source differential-drive wheeled robot platform (Figure 1). In the experiments, the robot is successfully able to follow a pre-defined path/line containing both straight and curved sections. The effectiveness of the control algorithm, combining steering and velocity controllers, is examined in these experiments. Keywords: Control systems, vision system, fuzzy logic, steering controller, velocity controller. List of Symbols

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Trajectory tracking of a mobile robot with frictions and uncertainties using hierarchical sliding‐mode under‐actuated control

Cited by 44 publications

References 26 publications

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

Tracking Control for Mobile Robots Considering the Dynamics of All Their Subsystems: Experimental Implementation

Fuzzy Sliding‐mode Strategy for Air–fuel Ratio Control of Lean‐burn Spark Ignition Engines

Vision Based Autonomous Path/Line Following of a Mobile Robot Using a Hybrid Fuzzy PID Controller

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