Heading control of variable configuration unmanned ground vehicle using PID-type sliding mode control and steering control based on particle swarm optimization

Zhang, Lei; Jiang, Yue; Chen, Guanpeng; Tang, Yuanjiang; Lu, Shuai; Xue, Gi

doi:10.1007/s11071-022-07998-z

Cited by 11 publications

(1 citation statement)

References 36 publications

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“…In [21], a finite-time robust sliding mode path following control was studied for a linearized AGV dynamic model. In [22], a particle swamp optimization algorithm was used to optimize the gains of the sliding mode controller to improve path tracking accuracy and robustness. In [23], immersion and invariance control is proposed to minimize tracking errors.…”

Section: Introductionmentioning

confidence: 99%

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Maaruf,

Mysorewala

2024

Preprint

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This article investigates the path-following control problem of an autonomous ground vehicle (AGV) with unknown external disturbances and input deadzones. Neural networks are used to estimate unknown external disturbances, dead zones, and nonlinear functions. A backstepping control is proposed to facilitate the tracking of the target path. The steady-state path-following error is decreased by adding an integral error term to the backstepping controller. Command filtering is employed to address the explosion of the complexity issue of the conventional backstepping approach, and the filtering error is compensated via an auxiliary signal. Lyapunov stability study indicates that the AGV closed-loop system is bounded by the proposed control with reasonable accuracy. At last, simulations are given to demonstrate the potential of the proposed scheme in path-following control.

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Section: Introductionmentioning

confidence: 99%

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Maaruf,

Mysorewala

2024

Preprint

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Observer-based prescribed performance path-following control for autonomous ground vehicles via error shifting method

Wang,

Liang,

Ding

2024

Nonlinear Dyn

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Neuro-adaptive path following control of autonomous ground vehicles with input deadzone

Maaruf,

Mysorewala

2024

Discov Appl Sci

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This article investigates the path-following control problem of an autonomous ground vehicle (AGV) with unknown external disturbances and input deadzones. Neural networks are used to estimate unknown external disturbances, dead zones, and nonlinear functions. The minimum learning parameter scheme is employed to adjust the neural network to reduce the computational load. A backstepping control is proposed to facilitate the tracking of the target path. The steady-state path-following error is decreased by adding an integral error term to the backstepping controller. Command filtering is employed to address the explosion of the complexity issue of the conventional backstepping approach, and the filtering error is compensated via an auxiliary signal. Lyapunov stability study indicates that the AGV closed-loop system is bounded by the proposed control with reasonable accuracy. At last, simulations are given to demonstrate the potential of the proposed scheme in path-following control.

show abstract

Heading control of variable configuration unmanned ground vehicle using PID-type sliding mode control and steering control based on particle swarm optimization

Cited by 11 publications

References 36 publications

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Neuro-Adaptive Path following Control of Autonomous Ground Vehicles with Input Deadzone

Observer-based prescribed performance path-following control for autonomous ground vehicles via error shifting method

Neuro-adaptive path following control of autonomous ground vehicles with input deadzone

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