Model predictive control for the tracking of autonomous mobile robot combined with a local path planning

Li, Jianhua; Sun, Jianfeng; Liu, Liqun; Xu, Jianing

doi:10.1177/00202940211043070

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…Li and Xiong (2019) performed obstacle avoidance of mobile manipulators using an MPC. Li et al (2021) used MPC for trajectory tracking and obstacle avoidance of mobile robots. Oleinikov et al (2021) used MPC for controlling a robot manipulator that avoids fixed and human obstacles.…”

Section: Related Workmentioning

confidence: 99%

Kinematic-Model-Free Predictive Control for Robotic Manipulator Target Reaching With Obstacle Avoidance

Chappell

Kormushev

2022

Front. Robot. AI

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Model predictive control is a widely used optimal control method for robot path planning and obstacle avoidance. This control method, however, requires a system model to optimize control over a finite time horizon and possible trajectories. Certain types of robots, such as soft robots, continuum robots, and transforming robots, can be challenging to model, especially in unstructured or unknown environments. Kinematic-model-free control can overcome these challenges by learning local linear models online. This paper presents a novel perception-based robot motion controller, the kinematic-model-free predictive controller, that is capable of controlling robot manipulators without any prior knowledge of the robot’s kinematic structure and dynamic parameters and is able to perform end-effector obstacle avoidance. Simulations and physical experiments were conducted to demonstrate the ability and adaptability of the controller to perform simultaneous target reaching and obstacle avoidance.

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Section: Related Workmentioning

confidence: 99%

Kinematic-Model-Free Predictive Control for Robotic Manipulator Target Reaching With Obstacle Avoidance

Chappell

Kormushev

2022

Front. Robot. AI

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“…f fj ðtÞ, f fj ðtÞ ¼ τ fj ðtÞ . r w , j ¼ 1; 2; 3 (7) where f j ðtÞ and τ j ðtÞ, j ¼ 1; 2; 3 are the forces and torques exerted by each wheel; f f j ðtÞ and τ f j ðtÞ are the friction forces and torques of each wheel; l v is the distance between the wheel and the center of mass of the vehicle; and r w is the radius of the wheel. From the geometric relations, the following equations can be obtained…”

Section: Modeling Of An Omrmentioning

confidence: 99%

“…The advantage of the proposed control scheme is that it does not require an explicit friction model and it is computationally less intensive than some other techniques, such as MPC. 6,7 It is clear from Refs. [4 and 5] that closed-loop performance improves when friction forces and torques are directly addressed in the control design.…”

Section: Introductionmentioning

confidence: 99%

Frictional forces and torques compensation based cascaded sliding-mode tracking control for an uncertain omnidirectional mobile robot

Karkoub

2022

Measurement and Control

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The work presented here deals with the trajectory-tracking control of omnidirectional mobile robots using a novel robust control technique. The aim is to achieve good tracking performance in the presence of frictional forces and torques, uncertainties, and saturated control inputs. First, a dynamic model of the omnidirectional mobile robot incorporating motor dynamics and frictional forces and torques is derived leading to a higher order system model. Subsequently, a novel cascaded sliding-mode tracking control scheme is proposed to perform trajectory-tracking with the omnidirectional mobile robot, which includes direct and indirect sliding-mode controllers in a cascaded structure. The indirect sliding-mode controller is utilized to attain tracking of the indirect reference input (i.e., desired trajectory) in the first subsystem and constitutes a direct reference input for the second one. A direct sliding-mode controller (i.e., actual control) is then designed to achieve tracking of the direct reference input. This way, the indirect reference input can be effectively manipulated through the proposed cascaded sliding-mode tracking control and individual subsystems are guaranteed to have not only Uniform Ultimate Boundedness but also asymptotic convergent performance. The stability of the closed-loop system is demonstrated via the Lyapunov stability criteria and validated through simulations. It is shown that the cascaded sliding-mode tracking control leads to good trajectory-tracking performance in the presence of uncertainties, friction, and saturated control input.

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“…Model predictive control (MPC) is an advanced control method based on time-domain optimization techniques in presence of constraints (Han et al, 2018; Inel et al, 2021; Li et al, 2021; Tian and Wang, 2022). Recently, along with the development of computer technology and the availability of models of many engineering subjects, the construction and implementation of MPCs in hardware systems is not a challenging problem anymore.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

Mạnh

Nguyen

Duy

et al. 2022

Transactions of the Institute of Measurement and Control

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The paper proposes an adaptive Lyapunov-based nonlinear model predictive control (MPC) to cope with the problems in nonlinear systems subjecting to system constraints and unknown disturbances of the parallel car driving simulator. Commonly, standard nonlinear controllers could guarantee the overall system stability for the parallel structure. However, the constraints tend to impact the control performance and stability adversely. Therefore, MPC plays a vital role in the proposed technique to explicitly consider all the practical constraints and simultaneously enhance the system’s robustness. Nevertheless, the accuracy of the modeling process has a significant effect on the MPC performance, and thus, the convergence cannot be guaranteed in the presence of the model uncertainties. To overcome this problem, by the merit of the fuzzy adaptive law, the control system takes the disturbances and unmodelled parameters into account. Moreover, the feasibility and stability of the approach, which is the fundamental problem of MPC, are ensured according to the Lyapunov-based nonlinear controller, backstepping aggregated with sliding mode control (SMC), and hence inherit advantages of these controls. Simulation results show the efficiency and superior constituted controllers of the proposed method.

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Model predictive control for the tracking of autonomous mobile robot combined with a local path planning

Cited by 14 publications

References 21 publications

Kinematic-Model-Free Predictive Control for Robotic Manipulator Target Reaching With Obstacle Avoidance

Kinematic-Model-Free Predictive Control for Robotic Manipulator Target Reaching With Obstacle Avoidance

Frictional forces and torques compensation based cascaded sliding-mode tracking control for an uncertain omnidirectional mobile robot

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

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