ℒ2 performance control of robot manipulators with kinematics, dynamics and actuator uncertainties

Abstract

This paper deals with the task-space trajectory tracking control problem of robot manipulators. An improved adaptive backstepping controller is proposed to deal with the uncertainties in kinematics, dynamics, and actuator modeling. To avoid the explosion of computation in conventional backstepping techniques, a modified dynamic surface control algorithm is proposed, which guarantees the asymptotic convergence rather than the uniformly ultimately boundedness of tracking errors in conventional dynamic surface control methods. Furthermore, the expression of the L 2 norm of tracking errors is explicitly derived in relation to the controller parameters, which provides instructions on tuning controller parameters to adjust the system performance. Moreover, the passivity structure of the designed adaptation law is thoroughly analyzed. Simulation of a free-floating space robot is used to verify the effectiveness of the proposed control strategy in comparison with the conventional tracking control schemes.

Tracking control of robot manipulators actuated by brushless DC motors: Elimination of joint velocity measurements with a robust observer

“…Theorem 1. The velocity observer design in (13) and (14), and the control laws designed in (46) and (53) with the desired current signal in (17) ensure boundedness of closed loop system and guarantee practical tracking (i.e.,…”

“…In Reference 11, learning‐based, and in Reference 12, disturbance observer‐based composite controllers have been developed for robot manipulators actuated with BDC motors. In References 13 and 14, neural network‐based controllers were developed for robot manipulators driven by BDC motors. Only a few studies in the relevant literature focus on reducing the required state measurements while explicitly addressing actuator dynamics.…”

Bi-Level Adaptive Computed-Current Impedance Controller for Electrically Driven Robots

“…Xu et al considered the robot parameters to be unknown, thus designed the control law with the help of robot nominal parameters and RBF neural network approximation. 18 In 2018, Ahmadi introduced another control method based on Taylor-based approximation of integrated dynamics of an EDR. 19 In 2008, Fateh introduced the voltage control strategy (VCS) in which only the electrical equation of the motors has been considered to control the robot.…”