Model Predictive Control for Robot Manipulators Using A Neural Network Model
نویسنده
چکیده
The model predictive control (MPC) technique for an articulated robot with n joints is introduced in this paper. The .proposed MPC control action is conceptually different with the traditional robot control methods in that the control action is determined by optimising a performance index over the time horizon. A neural network (NN) is used in this paper as the predictive model. The training data to set up the NN model corne from measuring the robot joint variables and torques during the running of the real robot. The computer simulations are given in the paper to demonstrate the advantages of the method.
منابع مشابه
Adaptive RBF network control for robot manipulators
TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...
متن کاملUsing a Neural Network instead of IKM in 2R Planar Robot to follow rectangular path
Abstract— An educational platform is presented here for the beginner students in the Simulation and Artificial Intelligence sciences. It provides with a start point of building and simulation of the manipulators, especially of 2R planar Robot. It also displays a method to replace the inverse kinematic model (IKM) of the Robot with a simpler one, by using a Multi-Layer Perceptron Neural Network ...
متن کاملSaturated Neural Adaptive Robust Output Feedback Control of Robot Manipulators:An Experimental Comparative Study
In this study, an observer-based tracking controller is proposed and evaluatedexperimentally to solve the trajectory tracking problem of robotic manipulators with the torque saturationin the presence of model uncertainties and external disturbances. In comparison with the state-of-the-artobserver-based controllers in the literature, this paper introduces a saturated observer-based controllerbas...
متن کاملDiscrete time robust control of robot manipulators in the task space using adaptive fuzzy estimator
This paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. A novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. Parameters of the fuzzy estimator are adapted to minimize the estimat...
متن کاملRejection of the Feed-Flow Disturbances in a Multi-Component Distillation Column Using a Multiple Neural Network Model-Predictive Controller
This article deals with the issues associated with developing a new design methodology for the nonlinear model-predictive control (MPC) of a chemical plant. A combination of multiple neural networks is selected and used to model a nonlinear multi-input multi-output (MIMO) process with time delays. An optimization procedure for a neural MPC algorithm based on this model is then developed. T...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2008