Finite Horizon Robust Nonlinear Model Predictive Control for Wheeled Mobile Robots

Nam, Đào Phương; Quang, Nguyen Hong; Nguyen, Thanh Long; Sinh, Xuan

doi:10.1155/2021/6611992

Cited by 13 publications

(7 citation statements)

References 37 publications

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“…Consider that system ( 14) is controlled by the NMPC control law κ(p(k)) = u * (k), obtained from NLP (17), then a closed loop system can be expressed as:…”

Section: Stability Analysismentioning

confidence: 99%

“…The advent of recently developed open-source optimisation toolkits [14] - [16], coupled with the enhanced processing capabilities of modern computers, has facilitated the rapid implementation of NMPC for real-time applications. Various NMPC schemes have been proposed to address both control problems using a singular NMPC approach, as detailed in [17] - [19]. These studies typically assume detailed knowledge of the robot's dynamic model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

Nguyen,

Van,

McIlvanna

et al. 2024

IEEE Trans. Intell. Veh.

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Within the context of Nonlinear Model Predictive Control (NMPC) design for autonomous mobile robots, which face challenges such as parametric uncertainty and measurement inaccuracies, focusing on dynamic modelling and parameter identification becomes crucial. This paper presents a novel safety-critical control framework for a mobile robot system that utilises NMPC with a prediction model derived entirely from noisy measurement data. The Sparse Identification of Nonlinear Dynamics (SINDY) is employed to predict the system's state under actuation effects. Meanwhile, the Control Barrier Function (CBF) is integrated into the NMPC as a safety-critical constraint, ensuring obstacle avoidance even when the robot's planned path is significantly distant from these obstacles. The closed-loop system demonstrates Input-to-State Stability (ISS) with respect to the prediction error of the learned model. The proposed framework undergoes exhaustive analysis in three stages, training, prediction, and control, across varying noise levels in the state data. Additionally, validation in Matlab and Gazebo illustrates that the NMPC-SINDY-CBF approach enables smooth, accurate, collision-free movement, even with measurement noise and short prediction times. Our findings, supported by tests conducted with the Husky A200 robot, confirm the approach's applicability in real-time scenarios.

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“…Consider that system ( 14) is controlled by the NMPC control law κ(p(k)) = u * (k), obtained from NLP (17), then a closed loop system can be expressed as:…”

Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

Nguyen,

Van,

McIlvanna

et al. 2024

IEEE Trans. Intell. Veh.

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show abstract

“…In general, MPCs require a system model for predicting the state of the system over a finite horizon Dao et al (2021). This imposes that the system to be controlled and its dynamic model must be known prior to control.…”

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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“…However, finite-time control, which enables the controlled system to reach the desired trajectory in a finite time, achieves lower costs and superior efficiency. In addition to faster convergence speed, finite-time control generally achieves higher accuracy, better interference immunity, and robustness in closed-loop systems, which has been established both theoretically [33,34] and experimentally [35]. Due to the various advantages of finite-time control, the finite-time control technique is employed in this study to improve the tracking performance of WMRSs, while making the controller design more stringent.…”

Section: Introductionmentioning

confidence: 99%

Neural‐based online finite‐time optimal tracking control for wheeled mobile robotic system with inequality constraints

Ding,

Zheng,

et al. 2023

Asian Journal of Control

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In this study, a finite‐time online optimal controller was designed for a nonlinear wheeled mobile robotic system (WMRS) with inequality constraints, based on reinforcement learning (RL) neural networks. In addition, an extended cost function, obtained by introducing a penalty function to the original long‐time cost function, was proposed to deal with the optimal control problem of the system with inequality constraints. A novel Hamilton‐Jacobi‐Bellman (HJB) equation containing the constraint conditions was defined to determine the optimal control input. Furthermore, two neural networks (NNs), a critic and an actor NN, were established to approximate the extended cost function and the optimal control input, respectively. The adaptation laws of the critic and actor NN were obtained with the gradient descent method. The semi‐global practical finite‐time stability (SGPFS) was proved using Lyapunov's stability theory. The tracking error converges to a small region near zero within the constraints in a finite period. Finally, the effectiveness of the proposed optimal controller was verified by a simulation based on a practical wheeled mobile robot model.

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Finite Horizon Robust Nonlinear Model Predictive Control for Wheeled Mobile Robots

Cited by 13 publications

References 37 publications

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments

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

Neural‐based online finite‐time optimal tracking control for wheeled mobile robotic system with inequality constraints

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