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...

The certainty equivalence and polynomial approach, widely used for designing adaptive controllers, leads to “simple” adaptive control designs that guarantee stability, asymptotic error convergence, and robustness, but not necessarily good transient performance. Backstepping and tuning functions techniques, on the other hand, are used to design adaptive controllers that guarantee stability and g...

This paper presents a new adaptive DMC controller that improves the controller performance in case of plant-model mismatch. The new controller monitors the plant measured output, compares it with the model output and calculates weights applied to the controller move. Simulations show that the new controller can help improve control performance and avoid instability in case of severe model misma...

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of nonlinear MIMO systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. Under the condition that the output functional dependence is unknown, a neural network based adaptive observer is designed to estimate the derivatives of the ...

This paper presents an adaptive Power System Stabilizer (PSS) using an Adaptive Network Based Vague Inference System (ANVIS). An Adaptive Vague Set Based Controller Power System Stabilizer (AVCPSS) has been evaluated. This AVCPSS is capable of providing appropriate stabilization signals over a broad range of operating conditions and disturbances. A Vague Controller (VC) is synthesized by using ...

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of nonlinear MIMO systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. Under the condition that the output functional dependence is unknown, a neural network based adaptive observer is designed to estimate the derivatives of the ...

This paper presents an adaptive steering controller for achieving consistent, sensitive, and accurate steering control with noisy and drifting control signals on an automated agricultural tractor. Conventional model-based PID controller is difficult to solve this problem because of too many disturbances and unknown factors. The authors have developed an adaptive steering controller consisting o...

In this paper, an observer-based direct adaptive fuzzy-neural network (FNN) controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system is presented. The direct adaptive control (DAC) has the advantage of less design effort by not using FNN to model the plant. By using an observer-based output feedback control law and adaptive law, the free parameters o...

In this paper, an adaptive fuzzy neural network (FNN) controller is proposed for missile guidance. The objective is for one defending missile (DM) to intercept an attacking missile (AM) in air battle scenario. The adaptive FNN controller is adopted to force the DM toward the AM under the existence of disturbance, and a monitoring controller is also designed to reduce the error between FNN contr...

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...