Motion control of mobile robots is a typical nonlinear tracking control issue and has been discussed with different control schemes such as PID, GPC, sliding mode, predictive control etc[1]-[3]. Intelligent control techniques, based on neural networks and fuzzy logic, have also been developed for path tracking control of mobile robots[4][5]. While conventional neural networks have good ability ...

The purpose of this paper is to apply adaptive fuzzy sliding mode control for structurally controlling buildings with sliding bearing isolation. Combining fuzzy control and robust control such as sliding mode control, reduces fuzzy rule bases complexity and ensures stability and robustness. We use Lyapunov theory to develop a self-tuning law. Stiffness uncertainty and time delay is used to demo...

This paper, deals with an indirect adaptive fuzzy control of discrete-time MIMO nonlinear systems with parametric variations. The synthesis of the state feedback control law is based on the T-S fuzzy models developed by a local description of the considered system. In the first step, gradient method is used to adjust model parameters locally estimated by the fuzzy model. The state feedback cont...

This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of inducti...

This talk will consider several types of adaptive control structure for nonlinear systems, based on the approximation of nonlinear functions by neural or fuzzy networks, which are also used to generate the control signals. The dynamic equations will be formulated as a generalisation of direct self-tuning or modelreference adaptation, in order to address the issues of parameter convergence and s...

In this paper, an adaptive fuzzy tracking control approach is proposed for a class of single-inputsingle-output (SISO) nonlinear systems in which the unknown continuous functions may be nonlinearlyparameterized. During the controller design procedure, the fuzzy logic systems (FLS) in Mamdani type are applied to approximate the unknown continuous functions, and then, based on the minimal learnin...

We discuss several fuzzy models to approximate friction and other disturbances in mechatronic systems, especially linear and rotarional electrical drives. Some methods of experimental identification of disturbance forces are presented. We consider several fuzzy models to compromise between model accuracy and complexity. Fuzzy model is used in an adaptive control loop. Several adaptive control a...

Since the large erecting mechanism has nonlinearities, parameter uncertainties and external disturbance, it is difficult to realize a model-based sliding mode control design. So a fuzzy adaptive sliding mode control scheme which combined fuzzy control with sliding mode control is proposed to achieve nonlinear control of the erecting mechanism. This control scheme is mainly use the fuzzy system ...

In this paper, a robust adaptive fuzzy control system using feedback linearization is proposed for a three-phase active power filter (APF). The APF system is divided into two separate loops: the current dynamic inner loop and the DC voltage dynamic outer loop. Adaptive fuzzy tracking control using feedback linearization is employed for the current dynamic inner loop to overcome the drawbacks of...