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In this paper, we presents a new class of iterative learning control law for a class of nonlinear systems, denoted PD-IPD, which is composed by adding a derivative term in the integrator of PID learning control law. By using Bellman-Gronwall lemma, the simple explicit conditions on the learning gains to ensure convergence are provided in the sense of Lambda norm. An attractive feature of PD-IPD learning...
In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control)...
In this paper, a robust-adaptive controller is developed for trajectory tracking of two rigid-link electrically-driven (RLED) robot manipulators carrying a rigid object. First, the dynamic model of the cooperative robots is derived. This model is written in a combined form such that the forces exerted by the object on the manipulators are not explicitly appeared in the dynamic model. A robust-adaptive...
Under the condition that the base of a robot manipulator has motion disturbance, the control of the end-effector target is difficult. In this paper, we show a method for robot control under base motion disturbance and load change with passivity-based adaptive control and to compensate viscous and coulomb friction. We insert target in cartesian space and calculate the inverse kinematics. Then the trajectory...
To resolve the problem of trajectory tracking of robot which have uncertain parameters and many uncertain ingredients. In high precise control, feedforward control can improve the tracking performance of system, this text present a kind of PD controller with feedforward based on adaptive theory. And by MATLAB simulation, this method is effective.
This paper presents the tuning and implementation of a computationally efficient adaptive predictive control algorithm for robotic utility. The controller addresses the need for practical, computationally efficient, robust real-time adaptive control for multivariable robotic systems. It exploits a special matrix representation to obtain substantial reductions in the computational expense relative...
The modified transpose Jacobian (MTJ) algorithm has been proposed, based on an approximated feedback linearization approach, in which there is no need to a priori knowledge of the plant dynamics. In this paper, a Learning-based modified transpose Jacobian (LMTJ) control algorithm is presented for trajectory tracking of robotic manipulators in an iterative operation mode. In this new control method,...
Recent biological studies reveals that animals utilize (or exploit) biological fluctuation to achieve flexibility and robustness against environmental disturbances. In this paper, we propose a simple but flexible control mechanism inspired by biological adaptation mechanism. Experimental results show that our proposed method can be applied to the control of a robot without prior knowledge about the...
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