Disturbance observer-based robust control for trajectory tracking of wheeled mobile robots

Huang, Dawei; Zhai, Junyong; Ai, Weiqing; Fei, Shumin

doi:10.1016/j.neucom.2015.11.099

Cited by 97 publications

(52 citation statements)

References 16 publications

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“…In addition, parallel parking trajectory tests are presented to show the regulation performance. The structure parameters of the WMR are borrowed from the work of Huang et al, which are given as m =4 kg, L =0.3 m, r =0.1 m, d =0.25 m, and

I_{0} = 2.5 kg \cdot {normalm}^{2}

.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The WMR is controlled to follow a reference trajectory with the control input

ν_{r} = 1 m/s, ω_{r} = \{\begin{matrix} 0.75 rad/s, & if t < 17 normals \\ 0.75 false(18 - t false) rad/s, & if 17 normals \leq t \leq 18 normals \\ 0 rad/s, & if t > 18 normals . \end{matrix}

The initial conditions of the reference trajectory and the WMR are set to be [ x r (0) y r (0) θ r (0)] T =[0 0 π /4] T and [ x (0) y (0) θ (0)] T =[3−2 π ] T , respectively. Both the disturbances F ( q ) and τ d are assumed to be f 1 = f 2 = f 3 = ν c + ω c +0.2 and

τ_{d 1} = τ_{d 2} = τ_{d 3} = \{\begin{matrix} \sin false(t false) + \cos false(t false), & if 0 \leq t < 10 normals \\ \sin false(t false) + \cos false(t false) + 3, & if t \geq 10 normals, \end{matrix}

respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The half length of the wheelbase and the radius of motorized wheels are labeled with L and r , respectively. The kinematic model of the real WMR is written as

\overset{q}{true} = f false(q, u_{c} false) = ([], \begin{matrix} center center center \\ array \cos θ & array - d \sin θ \\ array \sin θ & array d \cos θ \\ array 0 & array 1 \end{matrix}) ([], \begin{matrix} center \\ array ν_{c} \\ array ω_{c} \end{matrix}),

where q =[ ξ θ ] T denotes the state with coordinate ξ =[ x y ] of point o in the global coordinate frame and orientation θ of the local coordinate frame measured from X axis, and u c =[ ν c ω c ] T is the control input vector with linear velocity ν c and angular velocity ω c , respectively.…”

Section: Wmr Model and Control Structurementioning

confidence: 99%

“…For the tracking objective, we define q e ( t )=[ ξ e θ e ] T as the state‐following error, which is defined with respect to the local coordinate frame { o x y } fixed on the WMR and is given by

ξ_{e} = R false(θ false) false(ξ_{r} - ξ false),

θ_{e} = θ_{r} - θ,

where

ξ_{e} = {[x_{e} y_{e}]}^{T}, R (θ) = [\begin{array}{cc} \cos false(θ false) & \sin false(θ false) \\ - \sin θ & \cos θ \end{array}] .

By taking the derivative of ξ e in as in the work of Huang et al, the position tracking error system is given as follows:

{\overset{ξ}{true}}_{e} = ([], \begin{matrix} center center \\ array 0 & array ω_{c} \\ array - ω_{c} & array 0 \end{matrix}) ([], \begin{matrix} center \\ array x_{e} \\ array y_{e} \end{matrix}) + u_{e},

where

u_{e} = ([], \begin{matrix} center \\ array u_{e 1} \\ array u_{e 2} \end{matrix}) = ([], \begin{matrix} center \\ array - ν_{c} + ν_{r} \cos θ_{e} - d ω_{r} \sin θ_{e} <...> \end{matrix})

…”

Section: Wmr Model and Control Structurementioning

confidence: 99%

“…Therefore, some researchers changed the object from the design of kinematic controller to the integration design of kinematic controller and dynamic controller. The work of Huang et al advocated both kinematic controller and adaptive torque controller in the presence of external disturbances and unknown parameters. Lin and Yang presented an adaptive critic motion control to autonomously generate the control ability by learning through trials.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Yang

Guo

Xia

et al. 2019

Intl J Robust & Nonlinear

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Summary A dual closed‐loop tracking control is proposed for a wheeled mobile robot based on active disturbance rejection control (ADRC) and model predictive control (MPC). In the inner loop system, the ADRC scheme with an extended state observer (ESO) is proposed to estimate and compensate external disturbances. In the outer loop system, the MPC strategy is developed to generate a desired velocity for the inner loop dynamic system subject to a diamond‐shaped input constraint. Both effectiveness and stability analysis are given for the ESO and the dual closed‐loop system, respectively. Simulation results demonstrate the performances of the proposed control scheme.

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I_{0} = 2.5 kg \cdot {normalm}^{2}

.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The WMR is controlled to follow a reference trajectory with the control input

ν_{r} = 1 m/s, ω_{r} = \{\begin{matrix} 0.75 rad/s, & if t < 17 normals \\ 0.75 false(18 - t false) rad/s, & if 17 normals \leq t \leq 18 normals \\ 0 rad/s, & if t > 18 normals . \end{matrix}

τ_{d 1} = τ_{d 2} = τ_{d 3} = \{\begin{matrix} \sin false(t false) + \cos false(t false), & if 0 \leq t < 10 normals \\ \sin false(t false) + \cos false(t false) + 3, & if t \geq 10 normals, \end{matrix}

respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The half length of the wheelbase and the radius of motorized wheels are labeled with L and r , respectively. The kinematic model of the real WMR is written as

\overset{q}{true} = f false(q, u_{c} false) = ([], \begin{matrix} center center center \\ array \cos θ & array - d \sin θ \\ array \sin θ & array d \cos θ \\ array 0 & array 1 \end{matrix}) ([], \begin{matrix} center \\ array ν_{c} \\ array ω_{c} \end{matrix}),

Section: Wmr Model and Control Structurementioning

confidence: 99%

ξ_{e} = R false(θ false) false(ξ_{r} - ξ false),

θ_{e} = θ_{r} - θ,

where

ξ_{e} = {[x_{e} y_{e}]}^{T}, R (θ) = [\begin{array}{cc} \cos false(θ false) & \sin false(θ false) \\ - \sin θ & \cos θ \end{array}] .

By taking the derivative of ξ e in as in the work of Huang et al, the position tracking error system is given as follows:

{\overset{ξ}{true}}_{e} = ([], \begin{matrix} center center \\ array 0 & array ω_{c} \\ array - ω_{c} & array 0 \end{matrix}) ([], \begin{matrix} center \\ array x_{e} \\ array y_{e} \end{matrix}) + u_{e},

where

u_{e} = ([], \begin{matrix} center \\ array u_{e 1} \\ array u_{e 2} \end{matrix}) = ([], \begin{matrix} center \\ array - ν_{c} + ν_{r} \cos θ_{e} - d ω_{r} \sin θ_{e} <...> \end{matrix})

…”

Section: Wmr Model and Control Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Yang

Guo

Xia

et al. 2019

Intl J Robust & Nonlinear

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show abstract

Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Safaei

Mahyuddin

2018

Adaptive Control & Signal

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In this paper, the design procedure for optimal model-free control algorithm is presented for the tracking problem of completely unknown nonlinear dynamic systems operating under unknown disturbances. The procedure includes a new structure in the context of model-free control and data-driven control algorithms. In the new structure, the unknown nonlinear functions are segmented into 1 unknown linear-in-states part and another unknown nonlinear part. The adaptive laws proposed for estimating the unknown system dynamics are regressor-free estimation methods in which there is no need for regressor parameters and, consequently, the persistent excitation condition is not required anymore. Moreover, the main controller gains are updated online, incorporating the adapted values of linear terms in the system dynamics. A comparative study is presented to show that the proposed optimal model-free control outperforms the state-of-the-art model-free control algorithms. In addition, the simulation results for the application of the algorithm on autonomous mobile robots are provided.

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Event‐Triggered Adaptive Neural Network Control for 4WS4WD Wheeled Mobile Robot

Liao,

Liu,

et al. 2024

Adaptive Control & Signal

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In this article, an event‐triggered adaptive neural network controller based on threshold band is designed for a four wheels independently steered and four wheels independently driven (4WS4WD) mobile robot. The 4WS4WD mobile robot is attracting attention for its excellent motion performance such as manipulation versatility and posture flexibility. However, its control difficulty is increased due to its characteristics of being controlled by eight motors for steering and driving respectively. Also, since the robot itself has limited computing and communication resources, real‐time control cannot be guaranteed. To copy with that, the kinematic and dynamics models are first introduced for the 4WS4WD mobile robot. Second, an adaptive neural network controller with low‐frequency learning rate is utilized to control the mobile robot since there are unknown perturbations in the model. It can maintain system stability while handing unidentified model perturbations. The stability of the controller is demonstrated by Lyapunov stability analysis. An event‐triggered based on threshold band is suggested to lessen the amount of computation in the control process. Finally, the simulation outcomes further demonstrate how the suggested approach can greatly lessen the computational and communication cost while maintaining control performance.

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Disturbance observer-based robust control for trajectory tracking of wheeled mobile robots

Cited by 97 publications

References 16 publications

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Dual closed‐loop tracking control for wheeled mobile robots via active disturbance rejection control and model predictive control

Optimal model‐free control for a generic MIMO nonlinear system with application to autonomous mobile robots

Event‐Triggered Adaptive Neural Network Control for 4WS4WD Wheeled Mobile Robot

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