A fuzzy-logic concept for highly fast and accurate position control of industrial robots

Abstract

Different approaches of a learnable Fuzzy-Logic (E) concept for highly fast and accurate position control of industrial robots will be presented which provides both time-optimality for large position errors as well as well damped response (no overshoot) near the target. For automatic optimization of the control parameters an additional Neural-Network component is introduced. Based on simulation and cxpcrimcnts the performance and robustness of the presented FL concept are discussed.Within the last 15 years many powerhd robot control concepts and algorithms have been proposed which, without exceptions, are based on analytical robot models. Based on these mathematical models servo controllers can be designed which can base on different optimization criteria. However, the practical efficiency of these modelbased control concepts depends strongly on the accuracy in which the model represents the real static and dynamic system behavior [ 11.Due to the strong kinematic nonlinearities, to the dynamic coupling of adjacent robot joints as well as to the unavoidable elasticities of the mechanical structure in some cases robot models are very complex and clumsy. Thus model identification by means of experimental system analysis can be very expensive or can't be realized in the worst case. Moreover, almost all methods of model identification are based on the simplified assumption of linear system response. Thus control concepts based on such models obviously have only a limited area of validity.In such cases control concepts based on analytical models can provide insufficient cost/performance ratio, a poor robustness with respect to model uncertainties or variation as well as a lack of physically clear transparency of their functionality.Thus there is an increasing demand for alternative control concepts which do not presume an analytical robot model, are based on unsharp ("fuzzy") linguistic description of the measured process state and control input signals, can be specified and programmed by a set of heuristic application oriented rules and have the optimal capability of learning or adapting their control parameters to the desired optimum. Rule-based Fuzzy-Logic (FL-) IEEE International Conference o n Robotics a n d Autoniatlon -1184 0-7803-1965-6/95 $4.00 01995 IEEE controllers seem to comply with these steadily increasing requests.Within this paper several structurally different FLconcepts of robot position control as well as Neural Network (NN) component for control parameter optimization will be introduced. Their performance and robustness will be discussed by simulation and experimental results. Control problemA very important objective of point-to-point (PTP-) movements of robots is the ambiguous demand -to run as fast as possible in the case of far target distances (time optimality) and-to approach the target smoothly without overshoot collision avoidance).As illustrated by the simulated step responses of a robot joint in Figure 1.1 a linear servo-control concept cannot achieve this desired system behavior. Either ...

Erfolgreiche Anwendungen von Fuzzy Logik und Fuzzy Control (Teil 1) (Successful Applications of Fuzzy Logic and Fuzzy Control (Part 1))

“…Fuzzy-Regelung von Robotern [24], Fuzzy-Regelung eines Glasziehprozesses [26], Fuzzy-Parameteradaption eines PID-Gießspiegel-Reglers in Abhängigkeit von Regelabweichung und Arbeitspunkt [28]. Lineare Direktantriebe [29], Multikriterielle Fuzzy-Optimierung von Reglerparametern [30].…”

Integration of fuzzy control and model based concepts for disturbed industrial plants with large dead-times

“…Within the last 15 years many powerful concepts of model based predictive control have been developed [1,2]. They are characterized by a physically based plant model which permits predicting the consequences of certain control actions and optimizing the control concept with respect to user specific performance criteria.…”

“…In the past different structural approaches to combine fuzzy control and model based concepts have been proposed. One possible option is to insert a fuzzy component which adapts the parameters of the model based controller or switches softly between different controller blocks depending on the actual process state [2,3].…”

Human like contact forces adjusting for dexterous robot hands

“…In order to express human's knowledge, fuzzy rules based method is often used that's bemuse it has the advantage of being sample and efficient, and a lot of researches have been reported in this area [l][2] [3].This kind of knowledge system at least has a fixzzy rules base, a fuzzy reference machine and a data base, and make its decisions on the basis of the inputs in the form of linguistic variables derived fiom membershp functions which are formulas used to determine the fuzzy set to which a value belongs and the degree of membership in that set. Then the fuzzy reason machine matches these variables with the preconditions of fuzzy linguistic logic rules, and the response of each rule is obtained through fuzzy implication.…”