seminor on abhishek

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SEMINOR ON

Intelligent Hybrid Control Strategy for

Trajectory Tracking

of Robot Manipulators



INTRODUCTION

Robot control has been an active research field during the pastdecade. A main concern of the control task is to find an effectivecontroller to achieve accurate tracking of desired motions.However, robotic manipulators have to face many uncertainties intheir dynamics, such as payload parameter, friction, and

disturbance. Yet, the work using PD plus-feed forward on robustcontrol of robot shows great attraction to researches. In a PD-plus-feed forward controller is firstly proposed for robot manipulatorswhich guarantees exponential convergence. The adaptive version of this controller is addressed later considering the robustness of velocity measurement. However, the main drawback of them is that

they require high controller gains in order to overcome theuncertainty in the initial parameter errors. Combining the ideas of ,put forward a modified plan ensuring global exponentialconvergence.



PRELIMINARIES

In this section, we firstly give some

descriptions of intelligent systems, and afterthat, present the robot manipulator model

and some dynamic properties which are

crucial to our work.



Description of fuzzy-logic systems

The basic configuration of the fuzzy system is constructedfrom the fuzzy if-then rules using some specific inference,fuzzification, and defuzzification strategies Let x = ( x1, . . . ,

xN)T U RN and y V R be the i nput an output of the

fuzzy system. The fuzzifier maps a crisp point in U i nto a fuzzy set i n U. T he fuzzy rul e base consi st s o f a collection of fuzzy if-then rules. R( l): If x1 i s Fl1 and · · · and xN i s FlN ,then y i s Gl, i n w hich Fl i and Gl ar e fuzzy set s , a n d l = 1 , . . .

,M, w her e M d enotes the number of fuzzy if-then rules. Thefuzzy inference engine performs a mapping from fuzzy sets

in U to fuzzy set s i n V. The defuzzifier maps a fuzzy set in V to a cr i sp poi nt i n V. A more detailed description of thesesystems can be found



Description of robot manipulator

model

The basics of robot dynamics and control aresufficiently well known by now that we will bebrief in our derivation of the controlalgorithm. Thus, given the Euler-Lagrangedynamic equations for an n-l i nk r obot .

M(q )q¨ + Nq , q = ,Nq , q = C q , qq + G(q ) + Fd q + Fsq ) + d t , q , q



High-performance robot used as testbed.



EXPERIMENTS

In this section, the experimental setup and

system realization are described. Then

experimental data is discussed and analyzed.



1.H ardware

A 2-DOF direct-drive ShinMaywa R3C r obot arm i sused as testbed, see Figure 6. It is a rigid-link,low-friction, planar robot with two joints. The

dimensions of the robot are given, wheresubindexes 1 and 2 stand for the first and secondlinks,



2.F irmware

bit expansion bus slot of a DX486 personal computer.

TMS320 fl oati ng poi nt DSP C v.1.0.1 compi l er pr ov i d ed the

programming environment. The control input is transmitted

to the servomotors through the Shinmaywa servosystem,

which powers the pulse-wide modulators motor drives at

each joint. The performance of the proposed controller is shown incomparison with the neurofuzzy adaptive controller and fuzzysupervisory sliding-mode controller.Experiments are carried out at

high velocities in order to show the performance of the system atinertial dominated dynamics



CONCLUSION

A new adaptive intelligent hybrid robust controller based

on sliding mode is proposed in this paper for the trajectory

tracking of robot manipulators with unknown nonlinear

dynamics. We use intelligent systems to approximate the

upper bound on the parametric uncertainty, and propose

the idea of fuzzy bound later used to weaken the chattering

effect. The system global stability and the tracking error

convergence are proved by Lyapunov techniques. Simulation

results and experimental data show that the intelligent hybridcontroller make a significant improvement in the tracking

performance.



THANK YOU

seminor on abhishek

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