Discrete-time optimal adaptive RBFNN control for robot manipulators with uncertain dynamics

Yang, Runxian; Yang, Chenguang

doi:10.1016/j.neucom.2016.12.048

Cited by 32 publications

(11 citation statements)

References 24 publications

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“…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. Yang et al [15] investigated, a novel optimal adaptive radial basis function neural network (RBFNN) control for a class of multiple-input-multiple-output (MIMO) nonlinear robot manipulators with uncertain dynamics in discrete time. To facilitate digital implementations of the robot controller, a robot model in discrete time has been employed.…”

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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“…e dynamic parameters which describe the dynamic model are important for the control algorithms, effective simulation results, and accurate trajectory tracking algorithms. Dynamic equation of the robotic manipulator withn-DOF has been characterized in many literature studies [1][2][3][4][5][6][7][8][9][10][11] as follows:…”

Section: Description Of Link Parameters Of Robotic Manipulatormentioning

confidence: 99%

“…e first stage of trajectory tracking is to establish the precise mathematical model of the robotic manipulator. However, the nonlinear part of the dynamic model of the robotic manipulator is ignored in many literatures [1][2][3][4][5] or parameter identification by many approaches [6][7][8]; even the torque in the joint space and the moment of inertia were ignored in [9]. By calculating kinetic energy, potential energy, and generalized force, the Lagrange equation was utilized to build the dynamic equation for robotic manipulator [10,11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Zhu

Puchen

et al. 2020

Complexity

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To achieve precise trajectory tracking of robotic manipulators in complex environment, the precise dynamic model, parameters identification, nonlinear characteristics, and disturbances are the factors that should be solved. Although parameters identification and adaptive estimate method were proposed for robotic control in many literature studies, the essential factors, such as coupling and friction, are rarely mentioned as it is difficult to build the precise dynamic model of the robotic manipulator. An adaptive backstepping sliding mode control is proposed to solve the precise trajectory tracking under external disturbances with complex environment, and the dynamic response characteristics of a two-link robotic manipulator are described in this paper. First, the Lagrange kinetic method is used to derive the precise dynamic model which includes the nonlinear factor with friction and coupling. Moreover, the dynamic model of two-link robotic manipulator is built. Second, the estimate function for the nonlinear part is selected, and backstepping algorithm is used for analyzing the stabilities of the sliding mode controller by using Lyapunov theory. Furthermore, the convergence of the proposed controller is verified subject to the external disturbance. At last, numerical simulation results are reported to demonstrate the effectiveness of the proposed method.

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“…In [33], the NN approximation strategy was used to compensate for the uncertain dynamics of the manipulated object and the robot manipulator. A class of multiinput-multi-output (MIMO) nonlinear manipulators adaptive control problem was investigated in [34]. In this work, two RBFNN namely, a critic NN and an actor NN are employed to achive the optimal control.…”

Section: Introductionmentioning

confidence: 99%

Admittance-Based Adaptive Cooperative Control for Multiple Manipulators With Output Constraints

Yang

Yan

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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This paper proposes a novel adaptive control methodology based on admittance model for multiple manipulators transporting a rigid object cooperatively along a predefined desired trajectory. Firstly, an admittance model is creatively applied to generate reference trajectory online for each manipulator according to the desired path of the rigid object, which is the reference input of the controller. Then, an innovative integral Barrier Lyapunov function (iBLF) is utilized to tackle the constraints due to the physical and environmental limits. Adaptive neural networks (NN) are also employed to approximate the uncertainties of the manipulator dynamics. Different from the conventional NN approximation method, which is usually semiglobally uniformly ultimately bounded (SGUUB), a switching function is presented to guarantee the global stability of the closed-loop. Finally, the simulation studies are conducted on planar 2-link robot manipulators to validate the efficacy of the proposed approach.

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Discrete-time optimal adaptive RBFNN control for robot manipulators with uncertain dynamics

Cited by 32 publications

References 24 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

Adaptive Backstepping Sliding Mode Control of Trajectory Tracking for Robotic Manipulators

Admittance-Based Adaptive Cooperative Control for Multiple Manipulators With Output Constraints

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