NARMA-L2–based online computed torque control for robotic manipulators

Abstract

Computed torque is an effective method for control of robotic manipulators. In this paper, a novel approach is proposed for computed torque control method, and also, it is integrated with online least squares support vector regression algorithm. The motive is to improve control performance and to build an adaptive architecture where no prior information on system model is required; the model is obtained continuously using a machine learning technique. There are two main contributions of the proposed work: the first one is the formulation of the computed torque control law using the nonlinear autoregressive moving average-L2 control methodology and the second contribution is the implementation of online least squares support vector regression method in estimating the dynamical model of the system and calculating the control law. The performance of the proposed method has been evaluated by simulations on a two-link robotic arm. The robustness of the method has also been evaluated for cases with disturbance and model uncertainty. Comparison with an existing computed torque control approach in the literature demonstrates the superiority of the proposed approach. Thus, it can be concluded that the method can be effectively used for first-order and second-order nonlinear systems without inherent instability.

In-Depth Review of Advanced Control Strategies and Cutting-Edge Trends in Robot Manipulators: Analyzing the Latest Developments and Techniques

“…However, this time again the hardware validation with software simulations had not been performed. While performing a literature review one may witness a nonlinear autoregressive moving average-L2 commonly known as NARMA-L2 controller-based torque control strategy which was integrated very nicely with an online version of least square vector (LSV) regression method Sen and Öke [35]. In this technique, machine learning had been utilized to develop the model whereas the two-link robotic arm's simulations were employed to assess the performance in the presence of exogenous disturbances.…”

A Novel Adaptive LSSVR-Based Inverse Optimal Controller With Integrator for Nonlinear Non-Affine Systems

“…In the N ARM A − L2 model, the control input u n is separated from the nonlinear dynamics and appears linearly. This is the main strength of N ARM A − L2 model since it provides practicality in control design.Thus N ARM A − L2 modeling has been used in various researches [90], [98], [99] Additionally, it provides a good representation for an affine system model. The work proposed in this paper mainly concentrates on designing an inverse optimal controller for non-affine systems.…”