An Analytic Approach to Fuzzy Logic Robot Control Synthesis (original) (raw)

An adaptive fuzzy robot control without a fuzzy rule base

Proceedings of the 15th IFAC World Congress, 2002, 2002

A vexing problem in a conventional fuzzy control is the exponential growth in rules as the number of variables increases. This problem is avoided here by the introduction of a new, nonconventional analytic adaptive method for synthesising the fuzzy robot control. For this purpose a new adaptive analytic function is defined that determines the positions of the centres of the output fuzzy sets, instead of the definition of a fuzzy rule base. This function is adapted by chaning the free fuzzy-set parameters. The proposed analytic approach to the synthesis of an adaptive fuzzy logic control, has been tested by a numerical simulation of an analytic adaptive fuzzy control system for a robot with four degrees of freedom. Copiright  2002 IFAC

Systematic design and analysis of fuzzy-logic control and application to robotics

Robotics and Autonomous Systems, 2000

A systematic methodology for synthesis and analysis of fuzzy-logic controllers is proposed in this paper (Part I) and its follow up (Part II) [M.R. Emami, et al., Robotics and Autonomous Systems 33 (2000) 89-108]. A robust model-based control structure is suggested that includes a fuzzy-logic inverse dynamics model and several robust fuzzy control rules. The model encapsulates the knowledge of the system dynamics in the form of IF-THEN rules. The paper focuses on how to obtain this knowledge systematically from the input-output data of a complex system; one that is ill-defined or contains complicated phenomena that are difficult to interpret analytically. All practical steps, from data acquisition to model validation, are illustrated using a four degree-of-freedom robot manipulator. Comparing the results with those of a complete analytical model and a heuristic fuzzy modeling technique illustrates the strength of the proposed methodology in terms of capturing effects that are difficult to model. In the follow-up paper, this model is used in the proposed control structure.

Fuzzy control of robot manipulators:: some issues on design and rule base size reduction

Engineering Applications of Artificial Intelligence, 2002

This paper is aimed at looking into the automatic design and the size reduction of the rule base of fuzzy logic controllers. The first part is concerned with an automatic generation method of the fuzzy rule base. This is done by the use of an intelligent optimization method, and its implementation to the design of a fuzzy controller for robot manipulators. It is assumed that such system is known but ill-defined because of the inherent uncertainties associated with the model. Thus, an accurate mathematical model is not required, but a simplified one is acceptable. The second part treats the reduction of large scale fuzzy rule bases. Two approaches are used for this purpose: the boolean method and the decoupling approach giving a local control loop yielding smaller fuzzy controllers.The boolean approach is based on the equivalence between fuzzy preconditions and on boolean expressions. Using the fact that fuzzy sets are a generalization of classical subsets, we introduced some operations on fuzzy sets that are equivalent to those applied in the boolean logic approach.The paper then discusses the reduction of large scale fuzzy rule bases by the use of a decoupling approach, and its application to the case of an optimal fuzzy logic controller of a three-links robot manipulator using local PID controllers.

Fuzzy logic based robotic controller

1994

Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience frictionl stiction and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operations. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses endpoint error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of : computationally intensive kinematic equations. : Computer simulation results of this approach as obtained , from software implementation are also discussed. lalzada.f.liaa Fuzzy set theory was developed in 1965 by Zadeh [1], and permits the treatment of vague, uncertain, imprecise, and ill-defined knowledge and concepts in an exact mathematical way. This theory addresses the uncertainty that results from boundary conditions as opposed to Probability theory of mathematics. It allows one to express the operational and control laws of a system, linguistically in words such as "too cold", Copyright

Fuzzy control of robot manipulators

Engineering Applications of Artificial Intelligence, 1988

Parameters Optimization of Analytic Fuzzy Controllers for Robot Manipulators

An open question in fuzzy logic control of robot manipulators is how to modify the fuzzy controller parameters to guarantee appropriate performance specifications. In this paper a new approach to performance tuning of analytic fuzzy controllers for robot manipulators is presented. The analytic fuzzy control is a nonconventional approach that uses an analytic function for output determination, instead of a fuzzy rule base. The proposed approach is based on construction of a parameter dependent Lyapunov function. With the appropriate choice of the free parameter an estimation of integral performance index is obtained. The estimated performance index depends on controller parameters and few parameters which characterize the robot dynamics. The optimal values of the controller gains are obtained by minimization of the performance index. An example is given to demonstrate the obtained results.

Design of the Fuzzy Control Systems Based on Genetic Algorithm for Intelligent Robots

Interdisciplinary Description of Complex Systems, 2014

This paper gives the structure optimization of fuzzy control systems based on genetic algorithm in the MATLAB environment. The genetic algorithm is a powerful tool for structure optimization of the fuzzy controllers, therefore, in this paper, integration and synthesis of fuzzy logic and genetic algorithm has been proposed. The genetic algorithms are applied for fuzzy rules set, scaling factors and membership functions optimization. The fuzzy control structure initial consist of the 3 membership functions and 9 rules and after the optimization it is enough for the 4 DOF SCARA Robot control to compensate for structured and unstructured uncertainty. Fuzzy controller with the generalized bell membership functions can provide better dynamic performance of the robot then with the triangular membership functions. The proposed joint-space controller is computationally simple and had adaptability to a sudden change in the dynamics of the robot. Results of the computer simulation applied to the 4 DOF SCARA Robot show the validity of the proposed method.

An Analytic Approach to Fuzzy Logic Robot Control Synthesis (original) (raw)

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