Jun Oh Jang

Neuro-fuzzy control for DC motor friction compensation

This paper presents an application of a neuro-fuzzy controller for compensating the effects induced by the friction in a DC motor system. The neuro-fuzzy controller is a combination of a linear controller and a neuro-fuzzy network which compensates for nonlinear friction. The proposed scheme is implemented and tested on an IBM PC-based DC motor control system. The algorithm, simulations, and experimental results are described. The results are relevant for precision drive, such those found in industrial robots.

Backlash compensation of nonlinear systems using fuzzy logic

A backlash compensator is designed for nonlinear systems using fuzzy logic. The classification property of the fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the backlash, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the backlash compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic backlash compensator is simulated on a nonlinear system to show its efficacy.

Backlash compensation of systems using fuzzy logic

A backlash compensator is designed for systems using the fuzzy logic. The classification property of fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the backlash, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the backlash compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic backlash compensator is simulated on a system to show its efficacy.

Saturation and deadzone compensation of systems using neural network and fuzzy logic

A saturation and deadzone compensator is designed for systems by the fuzzy logic (FL) and the neural network (NN). The classification property of the FL system and the function approximation ability of the NN make them the natural candidate for the rejection of errors induced by the saturation and deadzone. The tuning algorithms are given for the fuzzy logic parameters and the NN weights, so that the saturation and deadzone compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The NN saturation and FL deadzone compensator is implemented on a system to show its efficacy.

Neuro-fuzzy controller for a XY positioning table

This paper presents control designs using an neuro-fuzzy network. (NFN) for il XY positioning table. The neuro-furzy controller is composed of an outer PD tracking loop for stabilization of the fast flexible mode dynamics and an NFN inner loop used to compensate for the system nonlinearities. A tuning algorithm is given for the NFN parameters so that the NFN control scheme becomes adaptive, guaranteeing small tracking errors and bounded weight estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The proposed new-fuzzy controller is implemented and texted on an IBM PC-based XY positioning table, and is applicable to many precision XY tables. The algorithm, simulation, and experimental results we described. The experimental results are shown to be superior to those of conventional control.

Output backlash compensation of systems using fuzzy logic

An output backlash compensator is designed for systems using dynamic inversion by the fuzzy logic. The classification property of fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the backlash, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the output backlash compensation scheme becomes adaptive. The fuzzy backlash compensator is simulated on a system to show its efficacy.

BACKLASH COMPENSATION OF NONLINEAR SYSTEMS USING FUZZY LOGIC

Abstract A backlash compensator is designed for nonlinear systems using the fuzzy logic. The classification property of fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the backlash, which has regions in which it behaves differently. A tuning algorithm is given for the fuzzy logic parameters, so that the backlash compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic backlash compensator is simulated on a nonlinear system to show its efficacy.

Neuro-fuzzy network control for a mobile robot

A control structure that makes possible the integration of a kinematic controller and a neuro-fuzzy network (NFN) dynamic controller for mobile robots is presented. A combined kinematic/dynamic control law is developed using backstepping and stability is guaranteed by Lyapunov theory. The NFN controller proposed in this work can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamic in the mobile robot. On-line NFN parameter tuning algorithms do no require off-line learning yet guarantee small tracking errors and bounded control signals are utilized.

Backlash compensation of discrete time systems using fuzzy logic

A backlash compensator is designed for discrete time systems using fuzzy logic. The classification property of fuzzy logic systems makes them a natural candidate for the rejection of errors induced by the backlash, which has regions in which it behaves differently. A discrete time tuning algorithm is given for the fuzzy logic parameters, so that the backlash compensation scheme becomes adaptive, guaranteeing small tracking errors and bounded parameter estimates. Formal discrete time nonlinear stability proofs are given to show that the tracking error is small. The fuzzy logic backlash compensator is simulated on a discrete time system to show its efficacy.