Predictive Control for Visual Servo Stabilization of Nonholonomic Mobile Robots

Cao, Zhengcai; Yin, Longjie; Fu, Yili; Liu, Tianlong

doi:10.3724/sp.j.1004.2013.01238

Cited by 3 publications

(11 citation statements)

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“…Then for any e(0), define the following optimal control problem to determine the parameters m where function u(e, l, m) is given by equation 11, set U is identified by equation 18, and set D = (0.1) 3 (0.1). From Theorem 1, it is known that there exists at least a set of m satisfying the constraints in problem (20), that is, the problem is always feasible for any initial state e(0) 2 Sˆ. Using numerical algorithms, for example, sequence quadratic program (SQP), mesh segmentation, we can derive some optimal solutions to the problem (20).…”

Section: ð17þmentioning

confidence: 99%

“…From Theorem 1, it is known that there exists at least a set of m satisfying the constraints in problem (20), that is, the problem is always feasible for any initial state e(0) 2 Sˆ. Using numerical algorithms, for example, sequence quadratic program (SQP), mesh segmentation, we can derive some optimal solutions to the problem (20). Let m * be the optimal solution to equation (20).…”

Section: ð17þmentioning

confidence: 99%

“…Using numerical algorithms, for example, sequence quadratic program (SQP), mesh segmentation, we can derive some optimal solutions to the problem (20). Let m * be the optimal solution to equation (20). Then, the visual feedback-based servo optimal control law of the mobile robot is defined as where…”

Section: ð17þmentioning

confidence: 99%

“…Hence, model predictive control (MPC) has been used to develop the visual servo control schemes of NMRs in recent years, as MPC has advantages to explicitly deal with constraints and optimization control problems. [17][18][19] For instance, Cao et al 20 proposed an image-based MPC for visual servo stabilization of NMRs with the constraints on visibility and actuators. In order to improve the computational efficiency of visual servo MPC of nonlinear NMRs, Li et al 21 employed the neurodynamic optimization technique and He et al 22 adopted the linear matrix inequality technique to design the visual servo MPC of nonlinear NMRs.…”

Section: Introductionmentioning

confidence: 99%

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Visual servo optimization stabilization of nonholonomic mobile robots based on control Lyapunov functions

Lin

Xing

2020

Measurement and Control

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This study considers the visual stabilization problem of nonholonomic mobile robots and proposes a novel optimization stabilization method for visual servo control of nonholonomic mobile robots with monocular cameras fixed onboard. The main idea of the method is to utilize control Lyapunov functions of discrete-time nonlinear systems to design a family of explicit stabilization control laws of the visual servo error system. The parameters of the control laws can indirectly reflect the performance of the visual servo controllers. Then taking account of visibility constraints and actuator limitations, a set of optimal parameters of the control laws is calculated by offline solving a constrained finite horizon optimal control problem. Moreover, the stabilization results on the optimal visual servo controller are established based on the properties of control Lyapunov functions. Finally, some simulation experiments are used to illustrate and evaluate the performance of the visual servo control scheme proposed here.

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Section: ð17þmentioning

confidence: 99%

Section: ð17þmentioning

confidence: 99%

Section: ð17þmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visual servo optimization stabilization of nonholonomic mobile robots based on control Lyapunov functions

Lin

Xing

2020

Measurement and Control

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“…However, the solution of this problem does not consider both the visibility constraints and the actuator limitations, jeopardizing the performance of the controller in practical applications. In their paper, Cao et al 17 introduce a predictive controller for the visual servo stabilization of a mobile robot. The authors propose a kinematic predictive stabilization controller that is used to generate the command of velocity and then they design a dynamic predictive controller in order to make the actual velocity of the mobile robot asymptotically approach the desired one.…”

Section: Vision Systems and Sensor Issuesmentioning

confidence: 99%

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Nascimento

Dórea²,

Gonçalves³

2018

Robotica

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SUMMARYModel predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

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Quasi‐min‐max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

Lin

Xing

et al. 2022

Asian Journal of Control

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This paper proposes a new visual servoing quasi‐min‐max MPC algorithm for stabilization control of an omnidirectional wheeled mobile robot subject to physical and visual constraints. The visual servoing dynamics of the robot are modeled as the state‐dependent linear error system with nonlinear control inputs of rotation and deflection velocities of wheels. The state‐dependent linear error system is covered as linear parameters‐varying models which is used to design the visual servoing quasi‐min‐max MPC controller. The actual control inputs of the robot are then computed by the solution of an inverse algebraic equation of the MPC actions. The recursive feasibility and stability of the new visual servoing MPC are ensured by some LMIs conditions. The performance and practicability of the visual servoing MPC are verified by some simulation and experiment results.

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Predictive Control for Visual Servo Stabilization of Nonholonomic Mobile Robots

Cited by 3 publications

References 0 publications

Visual servo optimization stabilization of nonholonomic mobile robots based on control Lyapunov functions

Visual servo optimization stabilization of nonholonomic mobile robots based on control Lyapunov functions

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Quasi‐min‐max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

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