Control of flexible joint robots via external linearization approach

Abstract

Based on the feedback linearization structure algorithm of differential geometric nonlinear control theory, an external linearization approach to the control of multilink flexible joint robots is considered in this article. The resulting externally linearized and input‐output decoupled closed‐loop system contains a linear subsystem and a nonlinear subsystem. The linear part describing the rigid motor motions is suitable for the design of nominal trajectory following control. However, the nonlinear joint deformation subsystem will cause perturbations in the nominal trajectory. To actively damp out the elastic vibrations and to render the complete closed‐loop system robust to uncertainty in system parameters, a combined LQR stabilizer and servocompensator is used as the internal stabilization and error correcting control. The tracking errors of the end effector caused by the quasi‐static joint deflections due to gravity can be compensated for by taking into account the nominal deflections in the trajectory planning and LQ regulation. A three‐link PUMA type arm is tested via simulation.

A Survey on the Control of Flexible Joint Robots

“…Reference [63] provided a closed form formula for optimal control of a single link FJR. The authors of [4] used feedback linearization to design a controller, and, in order to stabilize nonlinear modes of the system, they used LQR for the fast term. Feedback linearization was used in [64], too.…”

“…The dynamics of PUMA 560 can be found in [155,156]. References [4,68] simulated their proposed methods of these dynamics. In [157], two improvements were made to the conventional rigid controller of the KUKA robot which has three flexible joints.…”

“…The desire for higher performance from the structure and mechanical specifications of robot manipulators has spurred designers to come up with flexible joint robots (FJR) [2]. Most robots have been designed to be mechanically stiff because of the difficulty of controlling flexible members, not since rigidity, itself, is inherently attractive [3][4][5]. On the other hand, several new applications such as space manipulators [6] and articulated hands [7] necessitate using FJRs.…”

Programming advanced control functions for enhanced intelligence of industrial robots

“…For small variance, it is expected that the robot control system is able to move the workpiece under an applied external force in order for the robot to complete the part-loading task without issuing a collision alarm. In this case, the robot programmer can use the robot compliance control functions to compensate the variances in workpiece precisions [3] [4]. However, if the workpiece variance is over the upper tolerance limit, the robot is to experience unexpected collisions that often cause the damage to the tool, workpiece, and robot.…”

Control of Flexible Joint Manipulator via Reduced Rule-Based Fuzzy Control with Experimental Validation

“…In order to achieve these objectives, various methods using different technique have been proposed such as follow: linear quadratic regulation (LQR) control [4], adaptive output-feedback controller based on a backstepping design [5][6][7], nonlinear control based on the feedback linearization technique and the integral manifold technique [8,9], robust control based on PD control [10] and robust H∞ control [11], fuzzy control, PD fuzzy and neural network [12][13][14][15], optimal control [16,17], and so forth [2,[18][19][20][21][22][23].…”