Wiesław Żylski

Discrete dual-heuristic programming in 3DOF manipulator control

In this paper we propose a discrete tracking control system for 3 degrees of freedom (DOF) robotic manipulator control. The control system is composed of Adaptive Critic Design (ACD), a PD controller and a supervisory term derived from the Lyapunov stability theory. ACD in Dual-Heuristic Programming (DHP) configuration consists of two structures realized in a form of neural networks (NN): actor - generates a control signal and critic approximates a derivative of the cost function with respect to the state. The control system works on-line, does not require a preliminary learning and uses the 3DOF manipulator dynamicsmodel for a state prediction in ACD structure. Verification of the proposed control algorithm was realized on a SCORBOT 4PC manipulator.

Verification of Multilayer Neural-Net Controller in Manipulator Tracking Control

In this paper a multilayer neural-net (NN) controller is applied for tracking control of robotic manipulator, which is a nonlinear object having unknown and changeable parameters. Dynamics equations of a rigid manipulator are presented. The NN controller is used for compensating manipulator nonlinearities. The controller is realized in a form of a multilayer NN, which is nonlinear in the weights. The standard delta rule using backpropagation tuning is inadequate, so a term correcting the delta rule as well as a robustifying term is added. The presented control law and tuning algorithm are derived from the Lyapunov’s direct method. Results of the experiment are presented in this paper.

Tracking Control of Manipulator

Abstract In this paper a neural-net (NN) controller for tracking control of a manipulator is used. A manipulator is a nonlinear object which usually has unknown and changeable parameters. Dynamics equations of a rigid manipulator are presented. A NN controller is used for compensating manipulators nonlinearities. The controller is realized in a form of a linear NN in the weights. The NN is learned by using backpropagation tuning. The presented control law and tuning algorithm are derived from the Lyapunov direct method. In this paper results of simulation and experiment are presented.

Dynamics and Robust Control of Wheeled Mobile Robot

Abstract In the paper a model of 3-wheel mobile robot is proposed. The model has been reduced to the model with two substitute wheels, one driven wheel and one steered wheel. The motion phenomenon is described using Lagrange equations with multipliers, which are equivalent to dry friction forces that act on wheels of the model. The control algorithm is based on the input-output linearization method and variable structure control approach. The major contribution of this paper lies in application of new approach to control wheeled mobile robot along desired trajectory in the present of parametric and nonparametric modelling inaccuracies.

Neuro-Fuzzy Control of a Robotic Manipulator

Abstract In this paper, to solve the problem of control of a robotic manipulator’s movement with holonomical constraints, an intelligent control system was used. This system is understood as a hybrid controller, being a combination of fuzzy logic and an artificial neural network. The purpose of the neuro-fuzzy system is the approximation of the nonlinearity of the robotic manipulator’s dynamic to generate a compensatory control. The control system is designed in such a way as to permit modification of its properties under different operating conditions of the two-link manipulator

The comparison of parallel and serial-parallel structures of mobile robot pointer 2DX state emulator

Abstract This paper presents problems concerning the identification of dynamic motion equations derived on the basis of Maggi equations for a 2-wheeled mobile robot. The possibility of sending data between MapleTM and MatlabTM has been discussed. Off-line identification structures have been presented with the use of fuzzy logic.