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This paper addresses the stability and performance of adaptive control in robotic manipulators, which represents an important and unique class of nonlinear, time-varying, multi-input multi-output dynamic systems. An adaptive control was developed to attenuate the effect of the unknown parameter. This paper, after summarizing the mechanical design and dynamic equation, shows that the dynamic parameters...
An adaptive control for robot manipulators based on multiple incremental fuzzy neural networks (FNNs) is proposed in this paper. The overall controller is comprised of a feedback controller and multiple FNNs which learn inverse dynamics of the robot manipulator for different tasks. The multiple FNNs are switched or blended to improve the transient response when manipulating objects are changed. The...
This paper points out two new aspects of ILC (iterative learning control). One is to find out new applications of ILC and the other is to use feedforward input patterns obtained through ILC in order to generate new motions of robots. As new applications, passive human motions and underwater robot arm motions are explained in this paper. As the use of feedforward input patterns, the key ideas of a...
An adaptive control scheme for mechanical manipulators is proposed. The control loop consists of a network for learning the robot??s inverse dynamics and online generating the control signal. Some simulation results are provided to evaluate the design. A supervisor is used to improve the performances of the system during the adaptation transients. The supervisor exerts two actions. The first one consists...
This paper describes the programming of a reconfigurable environment to handle inverse dynamics computation for robotics control. Instruction parallelism/pipelining and avoidance of carry propagation while evaluating a lengthy sequence of sum of products is proposed. The difficulties of programming a reconfigurable platform are overcome by defining a fixed Processing Element (PE) model with multiple...
In this paper we will study dynamical modeling of a parallel robot Hexa using Lagrangian equation of the first type. Because of complexity and nonlinearity of parallel robotspsila relationships few works are done on their dynamical modeling. Although Newtonian approach is a straightforward method, it may not directly result in a suitable model for most of famous control schemes such as robust, adaptive...
Aiming at the slow convergence speed of iterative learning controller for trajectory tracking of manipulator, a new iterative learning controller based on a constructive RBF neural network is proposed by well considered the past experience of tracking various trajectories to select the initial control input of an iterative learning controller properly. A new desired trajectory can be decomposed into...
This paper utilizes a novel neural-adaptive method for controlling a two-link robotic manipulator. We do not need to resort to estimating the inverse dynamics. Our control utilizes the full dynamic model estimate including an inertia matrix estimate, referred to as a forward dynamics approach. Our novel contribution is to use an inertia matrix estimate to supervise the training of the neural networks...
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