This paper deals with adaptive control of nonlinear dynamic systems preceded by unknown backlash-like hysteresis nonlinearities, where the hysteresis is described by a dynamic equation. By utilizing this dynamic model and by combining a fuzzy universal function approximator with adaptive control techniques, a stable adaptive fuzzy control algorithm is developed without constructing a hysteresis...

the error of inertial navigation systems increase versus time, therefore for achieving higher accuracy specially in long time navigations we have to use an aiding system. global positioning system is the best aiding system in this case. in this paper we first simulate a gps and ins; then simulate tightly integration and finally review adaptation method of kalman filtering a fuzzy adaptive kalma...

This paper focuses on the problem of direct adaptive fuzzy control for nonlinear strict-feedback systems with time-varying delays. Based on the Razumikhin function approach, a novel adaptive fuzzy controller is designed. The proposed controller guarantees that the system output converges to a small neighborhood of the reference signal and all the signals in the closed-loop system remain bounded...

This paper presents an adaptive neuro-controller for intelligent control of non-linear dynamical system. The formed as the fuzzy selective neural net the adaptive neuro-controller on the basе of system’s state, creates the effective control signal under random perturbations. The validity and advantages of the proposed adaptive neuro-controller are demonstrated by numerical simulations. The simu...

In this paper, the discrete-time nonlinear systems identification and control based on an adaptive filter are introduced. This adaptive filter is mplemented using the adaptive network called Multi Input Fuzzy Rules Emulated Network (MIFREN). Inspired by the neuro-fuzzy network, the tructure of MIFREN resembles the human knowledge in the form of fuzzy IF-THEN rules. The initial value of MIFREN’s...

This paper presents an adaptive interval type-2 fuzzy sliding mode controller for a class of unknown nonlinear discrete-time systems with training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 fuzzy control scheme and sliding mode control (SMC) approach are incorporated to implement the main objective of controlling the plant to track a ...

In this work, non-linear control of CSTR for reversible reaction is carried out using Fuzzy Logic as design tool. The Model Reference Adaptive Control approach in used to design Fuzzy controller. The idea is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. We use PID control scheme ...

This paper analyzes the structure principle of the actuator simulated loading system with variable stiffness, and establishes the simplified model. What’s more, it also does a research on the application of the self-adaptive tuning of fuzzy PID (Proportion Integration Differentiation) in actuator simulated loading system with variable stiffness. Because the loading system is connected with the ...

In this paper, an Adaptive Neuro Fuzzy Inference System (ANFIS) based control is proposed for the tracking of a Micro-Electro Mechanical Systems (MEMS) gyroscope sensor. The ANFIS is used to train parameters of the controller for tracking a desired trajectory. Numerical simulations for a MEMS gyroscope are looked into to check the effectiveness of the ANFIS control scheme. It proves that the sy...

In this paper, a stable adaptive type-2 fuzzy tracking control equipped with sliding mode and Lyapunov synthesis approaches is proposed to attenuate the effects from unmodeled dynamics, external disturbance and approximation errors for nonlinear SISO systems. By employing adaptive fuzzy-neural control theory incorporated with Lyapunov stability criterion, the adaptive laws will be derived for a...