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Fuzzy Control of Robotic Manipulators and Mechanical Systems

  • Chapter
Fuzzy Reasoning in Information, Decision and Control Systems

Abstract

The Robotic Institute of America (R.I.A.) gives the following definition of robots: ” A robot is a reprogrammable multifucntional manipulator designed to move ma- terial, parts, tools or specialized devices through variable programmed motions for the performance of a variety of ‛ xc55. Based on this definition it is apparent that a robot must be able to operate automatically. This implies that in most of the robots it is possible to disnguish the following major subsystems: a manipulator (mechanical unit which can be compared to the skeleton of living beings), sensors and actuators (sensory organs and muscles of living beings), a controller (the brain), appropriate power supplies, and very often a computer system which takes care of the monitoring and control functions relative to the robot operation and which al- lows exchange of data between the robot and human operators and/or other parts of the manufacturing process in which the robot is performing some specified tasks. The motions of the manipulator must be controlled and the control system obeys the same basic principles as for control of motions of any mechanical system from simple servomechanisms up to complex machines or vehicles. It implies that positions and velocities or displacemets of the various parts of the mechanical system must be monitored and that related data must be transmitted to the control system. Then the latter is able to determine the driving forces and/or torques which must be applied to the mechanical system in order to force tha actual positions and displacement to track the desired ones.

The next section of this chapter is dedicated to the dynamics and control of robotic manipulators as they are relatively complex mechanical systems. Then the basic principles of control of motions in mechanical systems are reviewed in the following section. The two major classes of conventional position controllers are intoduced in a unifying presentation. This presentation aims at introdcing fuzzy control in the following section, fuzzy control appearing than as a natural extension of multilevel discontinuous control. The advantages of simplicity and reliability of discontinuous control are retaine by fuzzy control, but not its main drawback which is contin- uous cycling between different type of operation. Therefore fuzzy control can be viewed as an intermediate class bertween discontinuous and linear control systems, resulting in an acceptable compromise between advantages and drawbacks of both. Positions and velocities or displacemets are the usual state variables defining the state of a mechanical system. Control laws based on measurements or estimates of those variables allow some changes in the dynamics of the system, in particular the stabilization of unstable or neutrally stable systems, and give them the capability or reproducing desired motions with an accuracy which depends on the gains of the control system. However in such control systems static errors due to steady-state loading forces cannot be avoided. The only way to cope with such disturbances and reject their effect on the system is the introduction of a reset action in the control system. This can be achived through parallel controllers using control laws based on triples instead of pairs of data. However it may result in some deterioration of the dynamic performances and lead to motr difficulties in the design of fuzzy controllers. Self-organizing controller are a possible solution to the latter problem. Nevertheless there is another way since an indirect reset action can be introduces via model-based control schemes. These allow a neat separation of the two basic tasks of the control system: following the desired trajectory (tracking) on one hand and reducing the effect of disturbances (regulation or disturbance rejection) on the other hand. Such control schemes consist of two control loops, one of them including a basic position+ velocity controller and the other one a model of the system. THe basic controller and/or the model can be implemented as numerical or fuzzy systems as it is show in the fourth section of this chapter.

This paper presents research results of the Belgian Programme on Interuniversity Poles of Attraction initiated by the Belgian State, Prime Minister’s Office, Science Policy Programming. The scientific responsibility rests with its authors.

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  1. Centre for Systems Engineering and Applied Mechanics, University of Louvain, Bdlg Euler, Av. G. Lemaitre, 4-6, B-1348, Louvain-La-Neuve, Belgium

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Gorez, R., De Neyer, M. (1994). Fuzzy Control of Robotic Manipulators and Mechanical Systems. In: Fuzzy Reasoning in Information, Decision and Control Systems. International Series on Microprocessor-Based and Intelligent Systems Engineering, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-0-585-34652-6_17

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