Disturbance Observer-Based Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robot Subject to Saturated Velocity Constraint

Yue, Ming; Wu, Guoqiang; Wang, Shuang; An, Cong

doi:10.1080/08839514.2014.952918

Cited by 21 publications

(10 citation statements)

References 20 publications

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“…There are six variables to be restricted. However, the conventional methods, such as the use of a saturated function with compensation 13,14 and the BLF 15 and the active constraint performance method, 16 cannot be used to simultaneously restrain multivariable ranges. Therefore, a nonfragile nonlinear MPC method that can implement the tracking-error constraints is proposed in Section 3.…”

Section: Dynamics Model Of the Omnidirectional Walkermentioning

confidence: 99%

“…For instance, the traditional approach is to add a saturation function to meet the system requirements with constraints. 13,14 The barrier Lyapunov function (BLF) can be used in the controller design to handle constraints. 15 Alternatively, an active velocity constraint algorithm can enable the controller to directly restrict the system speed.…”

Section: Introductionmentioning

confidence: 99%

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Nonfragile predictive control for an omnidirectional rehabilitative training walker with constraints on the tracking errors of position and velocity

Sun

Zhang

Wang

et al. 2020

Optim Control Appl Methods

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Summary Here, we propose a novel nonfragile predictive control method for an omnidirectional rehabilitative training walker. The aim of the study was to design a tracking controller that includes simultaneous constraints on the tracking errors in the position and velocity in order to guarantee safe system states for the omnidirectional walker. The nonfragile controller uses incremental control to solve the optimal quadratic program problem. The exponential stability of the system is guaranteed by constructing constraints on the trajectory‐tracking error and the velocity‐tracking error. Simulation and experimental testing were conducted to confirm the effectiveness of the proposed method and verify that it can provide safe positions and velocities when applied to control the motion of an omnidirectional walker.

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Section: Dynamics Model Of the Omnidirectional Walkermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonfragile predictive control for an omnidirectional rehabilitative training walker with constraints on the tracking errors of position and velocity

Sun

Zhang

Wang

et al. 2020

Optim Control Appl Methods

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“…From a theoretical point of view, a finite prediction and control horizon, i.e., N, N u which are large enough in stage cost P is desirable as it will guarantee stability. For the system (10), we can acquire a quadratic problem for optimization by using the cost function (11), and its optimal solution can be obtained efficiently and reliably.…”

Section: A the Formulation Of Model Predictive Controlmentioning

confidence: 99%

“…The singularity problem caused by the state or input transformation can be avoided by the original system based switching control. In [11], an integral sliding mode controller was developed for the trajectory tracking of a nonholonomic mobile robot, in its inner loop, an improved velocity saturated controller based on hyperbolic tangent function is combined. In [14]- [16], the vector field feedback control approach was proposed for the mobile robot to achieve position stabilization, planned trajectory tracking and obstacles avoidance can be also combined.…”

Section: Introductionmentioning

confidence: 99%

Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization

Xiao

Yang

et al. 2017

IEEE Trans. Ind. Electron.

128

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In this paper, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation into the control input u1 an additional exponential decaying term. An MPC based control method is then designed for the robot in the presence of external disturbances. The MPC optimization can be formulated as a convex nonlinear minimization problem and a primal-dual neural network (PDNN) is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been improved by the proposed neuro-dynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach.Index Terms-Robust nonholonomic mobile robots, Scaling transformation, Model predictive control(MPC), Primal-dual neural network (PDNN).

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“…In [5] kinematic equations behaves as a control trouble and in [6] only the dynamic model along with velocity saturation and interruption adaptability limitations are measured simultaneously to track the trajectory of a nonholonomic WMR. A doubled close loop control scheme is presented to achieve a stability.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking of a Nonholonomic Wheeleed Mobile Robot Using Hybrid Controller

Ali¹,

Wang²,

Safwan³

et al. 2016

IJMO

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Disturbance Observer-Based Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robot Subject to Saturated Velocity Constraint

Cited by 21 publications

References 20 publications

Nonfragile predictive control for an omnidirectional rehabilitative training walker with constraints on the tracking errors of position and velocity

Nonfragile predictive control for an omnidirectional rehabilitative training walker with constraints on the tracking errors of position and velocity

Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization

Trajectory Tracking of a Nonholonomic Wheeleed Mobile Robot Using Hybrid Controller

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