This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for identification and control of a class of uncertain MIMO nonlinear systems. The proposed controller has the following salient features: (1) Selforganizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online learning ability of uncertain MIMO nonlinear systems; (3) ...

This paper presents a new controller for speed control problem of the BLDC motors. The nonlinear model of the motor is approximated by implementation of fuzzy rules. The uncertainties are considered in the fuzzy system. Using this model and linear matrix inequality (LMI) optimization, a robust controller for purpose of speed control of the motor has been designed and applied to it. The effectiv...

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...

This paper investigates the problem of analysis of a Takagi-Sugeno (T-S) fuzzy control system in the frequency domain. First, a linear plant with a T-S fuzzy controller is considered, and the complex dynamic behaviors of the system generated by the nonlinearity in the controller are analyzed. The global stability, instability and existence of limit cycles of the system are predicted with the ai...

This paper presents a robust ∞ H fuzzy controller for a class of T-S time-delay discrete fuzzy bilinear system (DFBS). Firstly, a discrete robust ∞ H fuzzy controller is proposed to stabilize the T-S time-delay DFBS with disturbance. Secondly, based on the Schur complement and some variable transformation, the stability conditions of the overall fuzzy control system are formulated by linear mat...

The problem of robust control for uncertain discrete-time Takagi and Sugeno (T-S) fuzzy networked control systems (NCSs) is investigated in this paper subject to state quantization. By taking into consideration network induced delays and packet dropouts, an improved model of network-based control is developed. A less conservative delay-dependent stability condition for the closed NCSs is derive...

In this paper an adaptive law with leakage is presented. This law can be used in the consequent part of Takagi–Sugeno-based control. The approach enables easy implementation in the control systems with evolving antecedent part. This combination results in a high-performance and robust control of nonlinear and slowly varying systems. It is shown in the paper that the proposed adaptive law is a n...

This paper presents a new control technique based on uncertain fuzzy models for handling uncertainties in nonlinear dynamic systems. approach is applied the stabilization of multimachine power system subject to disturbances. In this case, state-feedback controller parallel distributed compensation (PDC) system, where design laws Lyapunov function method and stability conditions are solved using...