Locally Weighted Learning Control for Dynamic Restricted Manipulators

doi:10.16182/j.issn1004731x.joss.17-0133

Abstract

Abstract: This paper proposes a locally weighted learning control law for a manipulator with state and input constraints and modeling uncertainties. By visualizing the control input as an extended state, the control problem is converted into control design for a state-constraint uncertain nonlinear system. Barrier Lyapunov functions are introduced into a backstepping procedure and a locally weighted learning control is designed, which ensures the exponential convergence of the barrier functions to a small neighborhood of zero and then guarantees satisfaction of system constraints and the tracking error convergence. The control feasibility and effectiveness is validated by theoretical analysis and simulation results.

Key words: neural network control, mobile robot manipulator, barrier function, locally weighted learning, system constraints

CLC Number:

TP241

Wang Gang, Sun Tairen, Ding Shengpei. Locally Weighted Learning Control for Dynamic Restricted Manipulators[J]. Journal of System Simulation, 2019, 31(4): 733-739.

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