Robust Tracking Control of Heterogeneous Robots With Uncertainty: A Super-Exponential Convergence Neurodynamic Approach

Abstract

Abstract

The immediate feedback tracking control system design of heterogeneous robots with uncertainty is considered to be a significant issue in robotic research. Note that when the robot information is uncertain, the scale of computation would become increasingly large and the accuracy of tracking control would become exceptionally low. The realization of the immediate feedback control system of heterogeneous robots with uncertainty remains to be a challenging problem. Many conventional zeroing neural network (CZNN) models have been developed accordingly. However, most of them are supported by the hypothesis that the robot parameters are complete and accurate, and the associated models possess the exponential convergence property. To handle the robot uncertainty as well as to improve the convergence performance, a new zeroing neural network (ZNN) with super-exponential convergence (SEC) rate is put forward in this paper termed SEC-ZNN, to resolve the robust control issue of uncertain heterogeneous robots. The proposed SEC-ZNN takes full advantage of effector real-time information, with robust controlling and super-exponential convergence performance so far as to the robot information is uncertain. Theoretically, the super-exponential convergence properties including lower error bound and faster convergence rate are rigorously proved. Moreover, circular path-tracking example, comparisons and tests via MATLAB, Coppeliasim and experiment via robot INNFOS substantiate the efficaciousness and preponderance of the SEC-ZNN for the immediate feedback control system for heterogeneous robots with uncertainty. Note to Practitioners —This paper is motivated by the problem that most robots which need real-time tracking control in real applications come with uncertainty. It is important to note that traditional robot tracking control algorithms mostly require complete robot information or assume information complete, which does not correspond to the actual situation of robot control. Moreover, for practical applications in robotics, the real-time tracking control problem is very attractive. Therefore, an accurate, efficient and stable solution is of great significance to practitioners in this area. In this paper, the SEC-ZNN algorithm is proposed to solve the problem of real-time control of heterogeneous robots with uncertainty in real applications for practitioners. The proposed methos makes full use of the real-time feedback infromation to solve the real-time tracking control problem of heterogeneous robots with uncertainty at the velocity level. The algorithmic steps and principle explanation of the SEC-ZNN scheme are also presented for better understanding. Simulation studies and comparisons are performed on a Stewart robot to confirm the effectiveness and superiority of the proposed scheme. Furthermore, the simulation experiment in Coppeliasim platform is performed to confirm the possibility of portability of the SEC-ZNN to real robot operations. Finally, applications on a real-world robot INNFOS verify the physical relizability of the proposed SEC-ZNN for the engineering practice via heterogeneous robots.