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This chapter deals with the design of a Fuzzy Logic Controller based Optimal Linear Quadratic Regulator (FC-LQR) for the control of a robotic system. The main idea is to design a supervisory fuzzy controller capable to adjust the controller parameters in order to obtain the desired axes positions under variations of the robot parameters and payload variations. In the advanced control of robotic...

This paper presents a method for robot self-recognition and self-adaptation through the analysis of the contact between the robot end-effector and its surrounding environment. Often, in off-line robot programming the idealized robotic environment (the virtual one) does not reflect accurately the real one. In this situation we are in the presence of a Partially Unknown Environment (PUE). Thus, r...

A robust tracking control design of robot systems including motor dynamics with parameter perturbation and external disturbance is proposed in this study via adaptive fuzzy cancellation technique. A minimax controller equipped with a fuzzy-based scheme is used to enhance the tracking performance in spite of system uncertainties and external disturbance. The design procedure is divided into thre...

This paper addresses the reliability analysis for a real-world apparel manufacturing system by using fuzzy mathematics. The studied apparel manufacturing system is a precise handicraft profession which involves a great amount of labor-intensive processes. To consider human performance, the apparel manufacturing system is constructed as a fuzzy multistate network, termed apparel manufacturing ne...

This paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. The proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. This kind of control law is in the class of torque-based control in which the joint torques are generated by permanen...

Neural network has good nonlinear function approximation ability. It can be widely used to identify the model of controlled plant. In this chapter, the theories of modeling uncertain plant by using two kinds of neural networks: feed-forward neural network and recurrent neural network are introduced. And two adaptive control strategies for robotic tracking control are developed. One is recurrent...

Models of linear programming problems with fuzzy constraints are very well known in the current literature. In almost all cases, to solve these problems, linear membership functions are used because they have very good properties and are very easy to manipulate. In some cases, however, because of the knowledge that the decision maker has, such membership functions could be modeled as nonlinear,...

This thesis deals with the analysis and design of robust fuzzy controllers for uncertain nonlinear systems using Takagi-Sugeno (T-S) model based approach. A T-S fuzzy model is used here to approximate the uncertain nonlinear systems where the nominal model and uncertain terms of the consequent parts of the fuzzy model are identified by a linear programming approach and then they are expressed i...