Robust adaptive neural network-based trajectory tracking control approach for nonholonomic electrically driven mobile robots

Boukens, Mohamed; Boukabou, Abdelkrim; Chadli, Mohammed

doi:10.1016/j.robot.2017.03.001

Cited by 80 publications

(34 citation statements)

References 23 publications

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“…Moreover, to demonstrate the effectiveness of the approach, the proposed networks were evaluated considering not only their approximation capabilities, but also their real time performance in comparison with the traditional iterative procedures used in robotics. Boukens et al [14] presented a robust intelligent controller to be applied to a class of nonholonomic electrically driven mobile robots. the robust adaptive neural network tracking controller developed here introduced adaptive laws to estimate a local upper bound of each subsystem of the nonholonomic mobile robot, then, these laws were used on-line as controller gain parameters in order to robustly improve the transient response of the closed-loop system and reduce conservative, in the sense that the local upper bounds to characterize the corresponding uncertainties dynamics for each subsystem, initially computed based on the worse-case scenario, were not updated during the effective control of the mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Using artificial neural network for forward kinematic problem of under-constrained cable robots

Heidari¹,

Faritus²,

Shateyi³

2018

J. vibroeng.

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Cable-Driven Parallel Robot has many advantages. However, the problems of cable collision between each other and environment, the lack of proper structure and non-positive cable tension prevent the spread of them. In this work, a neural network (NN) model of under constrained cable robots is presented with external forces applied to the end-effector (EE) for computing the position of it. As in under-constrained robot's kinematics and statics are innately coupled together, and they contemporaneously should be considered the forward kinematic problem of the robot change to an optimization problem. This approach does not require pre-knowledge of the uncertainties upper bounds and linear regression form of kinematic and dynamic models. Moreover, to ensure that all cables remain in tension, proposed control algorithm benefit the internal force concept in its structure. The main contribution of this paper has three goals. First, a method is used toward kinematic problem of the under constrained cable robot modeling using four bar linkage kinematic concept, which could be used in online control approaches for real-time purposes. Second, in order to track the position of end-effector, an online PD controller is designed by the three error criteria methods such as IAE, ISE and ITSE. Finally, as the third contribution, NN control approach is applied in order to validate the model. A model is created based on the robot's geometry and dynamic to solve the forward kinematics problem. So, the forward kinematic problem is solved offline and used online. Moreover, an analysis of workspace is performed which discovers that the solution of the forward kinematic problem of the under-constrained cable robots is unique in this case. In addition, a modified local linear model tree algorithm for nonlinear system modelling are proposed. The results show the effectiveness of the proposed approach in modeling the under constrained cable robot.

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Section: Introductionmentioning

confidence: 99%

Using artificial neural network for forward kinematic problem of under-constrained cable robots

Heidari¹,

Faritus²,

Shateyi³

2018

J. vibroeng.

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“…Theorem 1. For an underactuated MSV given by equations (8) to (12) with system uncertainties and external disturbances satisfying Assumptions 1 to 3, the predefined path is generated by equation (16), the kinematic controller is developed as equation (21), the dynamic control law is proposed as equation (31), and the composite NN adaptive laws are designed as equations (33) and (34), then all the closed-loop system signals z e À d e ; e ; y i ; i e ;J i , and z inn are UUB.…”

Section: Resultsmentioning

confidence: 99%

Robust composite neural dynamic surface control for the path following of unmanned marine surface vessels with unknown disturbances

Zeng

Wan

et al. 2018

International Journal of Advanced Robotic Systems

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This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.

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“…Most researches shown the control schemes for mobile robots are based on the assumption that the wheels roll without slipping and skidding [1][2][3]. In previous work, many intelligent control technologies, such as kinematic∕ torque control method using backstepping [4], a modified input-output linearization method [5], a data-based tracking control [6,7], a sliding-mode based tracking algorithms [8][9][10], neural networks tracking control method [11,12], extended state observer based nonlinear tracking and obstacle avoidance control method [13], disturbance observer-based robust trajectory tracking method [14], based on this assumption have been proposed for the robotics research. However, when the mobile robot performs certain motion tasks in complex environment, such as in wet or icy ground, high velocity starting and stopping and so on, these assumptions cannot be met due to slipping effect.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Adaptive Motion Control for a Mobile Robot with Unknown Longitudinal Slipping

Wang

Liu

Zhao

et al. 2019

Chin. J. Mech. Eng.

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When the mobile robot performs certain motion tasks in complex environment, wheel slipping inevitably occurs due to the wet or icy road and other reasons, thus directly influences the motion control accuracy. To address unknown wheel longitudinal slipping problem for mobile robot, a RBF neural network approach based on whole model approximation is presented. The real-time data acquisition of inertial measure unit (IMU), encoders and other sensors is employed to get the mobile robot's position and orientation in the movement, which is applied to compensate the unknown bounds of the longitudinal slipping using the adaptive technique. Both the simulation and experimental results prove that the control scheme possesses good practical performance and realize the motion control with unknown longitudinal slipping. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Robust adaptive neural network-based trajectory tracking control approach for nonholonomic electrically driven mobile robots

Cited by 80 publications

References 23 publications

Using artificial neural network for forward kinematic problem of under-constrained cable robots

Using artificial neural network for forward kinematic problem of under-constrained cable robots

Robust composite neural dynamic surface control for the path following of unmanned marine surface vessels with unknown disturbances

Neural Network-Based Adaptive Motion Control for a Mobile Robot with Unknown Longitudinal Slipping

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