Global-Stabilizing Near-Optimal Control Design for Nonholonomic Chained Systems

Qu, Zhihua; Wang, Jing; Plaisted, C.E.; Hull, Richard A.

doi:10.1109/tac.2006.880965

Cited by 54 publications

(49 citation statements)

References 31 publications

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“…Assumption 3 [7] The desired trajectory q d (t) to be followed is persistent such that the u 1d (t) in the chained form is uniformly right continuous, uniformly bounded, and uniformly nonvanishing.…”

Section: Model Of Nonholonomic Systemmentioning

confidence: 99%

“…Define the auxiliary tracking error u e = u − u * . By differentiating u e and using (7), the system dynamic model can be written in terms of the tracking error u e as…”

Section: Design Of Adaptive Neural Control For Dynamic Subsystemmentioning

confidence: 99%

“…In this paper, as a natural extension to our recent results on globally stabilizing near-optimal kinematic tracking control for nonholonomic chained systems [7], we propose an adaptive tracking control for uncertain dynamic nonholonomic systems with the aid of neural network approximation. The proposed design consists of two steps.…”

Section: Introductionmentioning

confidence: 99%

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Approximation Based Adaptive Tracking Control of Uncertain Nonholonomic Mechanical Systems

Wang

Obeng

et al. 2009

Control and Intelligent Systems

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Abstract-In this paper, the trajectory tracking control problem of uncertain nonholonomic mechanical systems is investigated. By separately considering kinematic and dynamic models of a nonholonomic mechanical system, a new adaptive tracking control is proposed based on neural network approximation. The proposed design consists of two steps. First, the nonholonomic kinematic subsystem is transformed into a chained form, and the corresponding optimal control is derived. Second, an adaptive neural control is designed for the dynamic subsystem to make the outputs of the dynamic subsystem asymptotically track the optimal control signals chosen for the kinematic subsystem. The proposed control is simulated on a unicycle wheeled mobile robot.

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Section: Model Of Nonholonomic Systemmentioning

confidence: 99%

“…Define the auxiliary tracking error u e = u − u * . By differentiating u e and using (7), the system dynamic model can be written in terms of the tracking error u e as…”

Section: Design Of Adaptive Neural Control For Dynamic Subsystemmentioning

confidence: 99%

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Approximation Based Adaptive Tracking Control of Uncertain Nonholonomic Mechanical Systems

Wang

Obeng

et al. 2009

Control and Intelligent Systems

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“…Therefore, many controllers for chained systems have been proposed. Such controllers include time-varying functions or non-smooth functions such as a sign function [5][6][7][8][9][10][11][12]. The control design method based on the time-state control form is also one such controller [13].…”

Section: Introductionmentioning

confidence: 99%

Control of mobile robot by switching traveling direction and control gain

Yamakawa

Ebara

2017

Robomech J

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The control method based on the time-state control form has been proposed to stabilize the chained system, which is a canonical-form nonlinear system. In this study, the control method is used for controlling a mobile robot in auto-parking situations. The proposed controller includes a parameter that is allowed to switch at arbitrary times without loss of the stability of the system. The robot employing the proposed controller reaches the target position by switching its traveling direction to avoid collisions with obstacles. However, the shape of the robot gives a problem. We resolve this by using the switchable parameter included in the proposed controller, and show the availability of switching the parameter. Furthermore, the appropriate switching of the traveling direction and the parameter enables the robot to reach the target faster. Thus, we search the appropriate values of the parameter and the switching points of the traveling direction using the genetic algorithm. In the auto-parking experiments that incorporate the search results, the robot can reach the target position faster.

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“…For instance, [9] has used nonlinear H ∞ control via quasi-linear parameter varying representation to control wheeled robots and [10] has presented an implementation of integral sliding mode controller on a two-wheeled mobile robot. Other control methods, such as back-stepping control [11], adaptive control [12], [13], fuzzy control [14], [15] and control based on the representation as chained system [16], [17], [18], have also been explored and implemented.…”

Section: Introductionmentioning

confidence: 99%

Shared Control for the Kinematic and Dynamic Models of a Mobile Robot

Jiang

Franco

Astolfi

2016

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper presents shared-control algorithms for the kinematic and the dynamic models of a mobile robot with a feasible configuration set defined by means of linear inequalities. The shared-control laws based on a hysteresis switch are designed in the case in which absolute positions are not available. Instead, we measure the distances to obstacles and angular differences. Formal properties of the closed-loop systems with the sharedcontrol are established by a Lyapunov-like analysis. Simulation results and experimental results are presented to show the effectiveness of the algorithm.

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Global-Stabilizing Near-Optimal Control Design for Nonholonomic Chained Systems

Cited by 54 publications

References 31 publications

Approximation Based Adaptive Tracking Control of Uncertain Nonholonomic Mechanical Systems

Approximation Based Adaptive Tracking Control of Uncertain Nonholonomic Mechanical Systems

Control of mobile robot by switching traveling direction and control gain

Shared Control for the Kinematic and Dynamic Models of a Mobile Robot

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