Characteristic model-based control of robotic manipulators with dynamic uncertainties

In this paper, we investigate the problem of the discrete-time adaptive control design of robotic manipulators by utilizing the characteristic model theory. Different from the traditional plants manipulated by the characteristic model-based control strategy, the Coriolis matrix in the robotic dynamics makes the bound of the characteristic model coefficients rely on the state information, proposing great challenge for the controller design and stability analysis. To conquer this problem, we develop a projection estimation algorithm depending on the state information, and propose an adaptive control law via introducing a discrete-time sliding vector. A new stability analysis framework is proposed by use of the recursive induction methodology, giving rise to the asymptotic convergence of joint positions. Simulations are performed to test the effectiveness of our methodology. Our design extends the characteristic model theory frame by proposing the control design and stability analysis methods for the the characteristic model with the state-relevance coefficients.