Okyay Kaynak

A generalized approach for Lyapunov design of sliding mode controllers for motion control applications

In the application of sliding mode controllers, the main problems that are encountered are chattering and computational power required for the calculation of equivalent control. In this paper, novel approaches for the design of SMC based on the selection of a Lyapunov function are proposed for motion control applications that evade these problems and are easily implementable. A general criterion is established for stability and chattering elimination. Two novel schemes are proposed for the estimation of the equivalent control to avoid the computational burden. Simulation studies carried out on a direct drive arm indicate that the proposed approaches are a good candidate for motion control applications.

Fuzzy identifier based inverse dynamics control for a 3-DOF articulated manipulator

A method using fuzzy logic and neural network structures for the identification and control of robot dynamics is presented in this paper. Fuzzy identifiers are employed to match the effects in the robot dynamics model. Fuzzy logic systems used are represented as three-layer feedforward neural networks. Fuzzy system parameters are adjusted via backpropagation. An on-line tuning algorithm is employed. Simulation results obtained with a three degrees of freedom articulated arm are given.

A study of fuzzy schemes for control of robotic manipulators

Fuzzy control of robotic manipulators has found vast interest among researchers. This work studies four kinds of control with fuzzy ideas: straightforward conventional fuzzy control; fuzzy control with gravity compensation; fuzzy control with nonlinear state feedback; and a novel self-organizing fuzzy control idea. The methods are tested by simulations on a SCARA type robot and on the first three links of an articulated arm. Performances of these algorithms are compared.

An inverse dynamics based robot control method using fuzzy identifiers

Summary form only given. In the trajectory control of robotic manipulators, the main difficulty is that the dynamics involved is coupled and nonlinear. A method for obtaining a nonlinear model is presented. To match the gravity, centripetal, Coriolis and inertial effects in the robot dynamics model, fuzzy logic systems which are represented as 3-layer feedforward neural networks are used. One of the main objectives considered is to keep the fuzzy system simple with a small number of rules and free of redundant inputs to have applicability in real time. Any deficiency in the rule base is aimed to be compensated by the fast learning capacity of the system. Firstly fuzzy modeling of the robot dynamics is considered. The 3-layer feedforward neural network representation of the class of fuzzy systems used, together with the backpropagation algorithm, are detailed. The online identification method is explained. Lastly, results are presented for the industrial arm MAMROB/ER15.

Fuzzy parameter adaptation for a sliding mode controller as applied to the control of an articulated arm

A number of approaches based on sliding mode control methodology are proposed in the literature for the position control of robotic manipulators. An important problem in this context is chattering, that is high frequency oscillations in the velocity. In this paper, a novel approach is considered which eliminates chattering if the controller parameters are set suitably. A fuzzy adaptation scheme is devised for the online adaptation of the parameters of this method. Simulation results show that the fuzzy rule based adaptation scheme ensures good controller performance without chattering.