Neuro-Adaptive Force/Position Control With Prescribed Performance and Guaranteed Contact Maintenance

Abstract

In this paper, we address unresolved issues in robot force/position tracking including the concurrent satisfaction of contact maintenance, lack of overshoot, desired speed of response, as well as accuracy level. The control objective is satisfied under uncertainties in the force deformation model and disturbances acting at the joints. The unknown nonlinearities that arise owing to the uncertainties in the force deformation model are approximated by a neural network linear in the weights and it is proven that the neural network approximation holds for all time irrespective of the magnitude of the modeling error, the disturbances, and the controller gains. Thus, the controller gains are easily selected, and potentially large neural network approximation errors as well as disturbances can be tolerated. Simulation results on a 6-DOF robot confirm the theoretical findings.

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

“…To solve partial tracking error constraints, a fuzzy dynamic surface control design was developed in [49,50] for a class of strict-feedback nonlinear systems by transforming the state tracking errors into new virtual variables. However, the existing control schemes, such as [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51], can only guarantee the stability of closed-loop systems with different constraints, which are not capable of achieving the learning of unknown system dynamics. The main reason is that the derived closed-loop error system is extremely complex, such that its exponential convergence is difficult to be verified using the existing stability analysis tools.…”

Time domain passivity control of time-delayed bilateral telerobotics with prescribed performance

“…Xie et al propose a new control algorithm combined with neural network for a robot system to assure the system's tracking error to be restricted by a prescribed decreasing boundary [4]. Bechlioulis et al proposes a new neuroadaptive force/position control algorithm [5]. The performance of error evolution within prescribed bounds in both problems of regulation and tracking in robotic system is achieved in [6].…”

Adaptive Jacobian force/position tracking for space free-flying robots with prescribed transient performance

“…However, this guarantee depends on control gains and system parameters. Controllers with capabilities to guarantee prescribed performance are presented for the force/position tracking of manipulators with fixed bases in [8,9,4], which are developed based on the error transformation proposed in [10] for affine nonlinear systems. These error transformation functions are slightly modified in [11,12] from original ones so as to achieve asymptotical convergence of tracking errors.…”