M. De Neyer

Passivity approach to fuzzy control systems

Fuzzy controllers bring treated as passive dynamic nonlinear controllers, stability of fuzzy control loops is proven with the unique condition that the controlled plant can be made passive by zero shifting. For linear time-invariant plants, this approach leads to frequency response conditions similar to the previous results in the literature, but which are more general and can include robust stability considerations

Fuzzy and quantitative model-based control systems for robotic manipulators

Generally fuzzy control systems use simple controllers with a few inputs and one output. Here more complex control systems, based explicitly on a model of the controlled process and primarily developed in the frame of quantitative control, are adapted to fuzzy control. Three model-based control schemes are proposed for position control of a robotic manipulator. The feasibility of such control systems and the ability of their quantitative and fuzzy implementations to cope with disturbances, parameter variations and unmodelled dynamics, are evaluated and compared by simulation analysis. The extension of the model-based control paradigm to fuzzy control pinpoints a concept unknown in the usual fuzzy controllers, i.e. intrinsically fuzzy variables that may be a source of problems in fuzzy feedback loops.

Comments on “Practical design of nonlinear fuzzy controllers with stability analysis for regulating processes with unknown mathematical models”

It is shown in this note that the fuzzy controller with (locally) linear control rules used in Ying (1994) cannot be linearized around the origin, and therefore local stability of closed-loop systems including this type of fuzzy controller cannot be analyzed by means of the Lyapunovs first method. Besides, it is proved that, with another choice of inference law, the fuzzy controller with (locally) linear control rules can be linearized in a neighbourhood of the origin. Copyright (C) 1996 Elsevier Science Ltd.

Fuzzy internal model control

Control systems based on a qualitative internal model of the process to be controlled are presented. Two models are considered: a low rate one-step ahead shift operator and a first-order predictor. Performances of such control systems are appraised by applying them to a second-order process.

Fuzzy control of a nonlinear plant: the case of a fluid mixer

A study is made of the qualitative control of an intrinsic nonlinear plant, a fluid mixer, using a theory of fuzzy control that is presented in a functional way. The mixer is cylindrical, with two fluid inputs of different coloration and an output of the resulting mix. The main points studied are the response of coloration when the desired color is changed and when the output flow changes. The simulation results are presented.<<ETX>>

Qualitative physics versus fuzzy sets theory in modeling and control

The authors present a comparison between two modeling and control techniques of physical systems: qualitative physics based on symbolic manipulation and fuzzy logic. The main features of each approach are considered. The issues addressed are modeling and control using qualitative physics and fuzzy logic, specific domains of application for these approaches, and whether they are complementary. The concepts studied are illustrated with an example of biological control.<<ETX>>

Self-Tuning Self-Organizing Fuzzy Robot Control

Abstract The paper describes the idea of self-tuning self-organising fuzzy control. Simple fuzzy controller, introduced at the beginning of seventies as a rule-based controller, had two main disadvantages: difficulty with definition of good control rules and problems with tuning of controller parameters. Self-organising controller was developed to overcome the first problem. In this paper we propose a self-tuning procedure to overcome the second problem. That procedure is based on expert knowledge about the influence of the tuning parameters on the system response. Theoretical results are illustrated and tested by simulation a two link robot manipulator.

Building fuzzy-qualitative models for simulation and control☆

Abstract A procedure for building fuzzy-qualitative models of dynamical systems is described and the influence on the modelling accuracy of several factors related to quantity spaces and membership functions involved in fuzzification and/or defuzzification interfaces is investigated via a simulation analysis. It is shown that using multi-valued universes of discourse and introducing fuzziness allow accurate fuzzy-qualitative simulations. Simulation results for linear and nonlinear first-order systems and for second-order systems with real or complex poles are presented and discussed.

A Unifying Approach to Some Model-Based Control Structures

Abstract Different control structures including an explicit model of the system to be controlled axe particular cases of a generalized structure. This allows the derivation of common properties, especially a property of separation of the tracking and regulation dynamics which may be useful in the design of the control system. Problems related to internal stability and realization of various control structures are considered, and applications are quoted.

Fuzzy Control of Robotic Manipulators and Mechanical Systems

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.