Abstract
This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.
Highlights
Robotic manipulators are highly coupled, time-varying, and multivariable nonlinear dynamic systems
It should be noted that dynamic uncertainties in robotic system models are unavoidable due to the unknown load, mass, etc., and such modeling uncertainties may lead to a degradation on the control accuracy or even cause instability of the robotic system. e modeling uncertainties can be divided as structured uncertainties and unstructured uncertainties
In order to deal with the unstructured uncertainties of robotic manipulator systems, various learningbased control methods [13–17], including neural network (NN) and fuzzy systems, have been proposed to overcome them
Summary
Robotic manipulators are highly coupled, time-varying, and multivariable nonlinear dynamic systems. As the robotic system with uncertain parameters is very complex, some researchers combine multiple-model strategy with neural networks to improve the control performance in recent years [24, 25]. Rough the WMMAC method, the control range can cover the variation range of system parameter change, which can solve the control problem of the complex nonlinear system well when parameters change or jump unexpectedly It enhances the robustness of the system, effectively reduce the model identification time, and decrease the system transient error. Aiming at stabilizing the two-link robotic manipulator with largely jumping parameters, uncertainties, and external disturbance, a WMMAC scheme combining multiple NNbased controllers is proposed. (i) A WMNNAC scheme is proposed for robotic manipulators to deal with dynamic uncertainties and largely jumping parameters It can improve the transient performance of the system. R denotes the space of real numbers; Rn denotes the n-dimensional Euclidean space with the vector norm ‖ · ‖; and Rn×m is the set of all n × m real matrices. λmin(·) and λmax(·) are the minimum and maximum eigenvalues of matrix ·, respectively. |∗| denotes taking the absolute values of all the elements in the vector ∗
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