A novel adaptive controller for the stochastic third-order robot system with uncertain virtual control gains and input nonlinearities

This article focuses on the adaptive control for the stochastic manipulator system with uncertain input nonlinearities, whose virtual control gains (VCGs) are divided into known and unknown parts for the purpose of more information being employed for controller design. To deal with the unknown parts of VCGs, a new bounded smooth auxiliary function is established. To eliminate the problem of virtual control signals being not differentiable enough and handle the uncertainties from the input saturation and deadzone, new virtual and actual control laws are constructed and applied to a third-order robot system, such that all the signals in the closed-loop system are bounded in possibility. The stability analysis and comparison of simulation results demonstrate the effectiveness and superiority of the proposed controller.