Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: neural control

Sortuj według:

Ogranicz wyniki do:

Design of a multivariable neural controller for control of a nonlinear MIMO plant

100%

Bańka S., Dworak P., Jaroszewski K.

International Journal of Applied Mathematics and Computer Science

2014

tom 24

nr 2

357-369

The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its parameters chosen on the basis of two measured auxiliary signals. These are the ship's current forward speed measured with respect to water and the systematically calculated difference between the course angle and the sea current (yaw angle). Four different methods for synthesis of multivariable modal controllers are used to obtain source data for training the neural controller with parameters reproduced by neural networks. Neural networks are designed on the basis of 3650 modal controllers obtained with the use of the pole placement technique after having linearized the model of LF motions made by the vessel at its nominal operating points in steady states that are dependent on the specified yaw angle and the sea current velocity. The final part of the paper includes simulation results of system operation with a neural controller along with conclusions and final remarks.

Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment

100%

Ferguene F., Toumi R.

International Journal of Applied Mathematics and Computer Science

2009

tom 19

nr 1

113-126

Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.

Ograniczanie wyników

2 International Journal of Applied Mathematics and Computer Science

1 Bańka S.

1 Dworak P.

1 Ferguene F.

1 Jaroszewski K.

1 Toumi R.

1 2014

1 2009

Wyniki wyszukiwania

Design of a multivariable neural controller for control of a nonlinear MIMO plant

Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment