We show in the paper that the, so–called, “new architecture” of L1–adaptive control is, indeed, different from classical model reference adaptive control. Alas, it is not new, since it exactly coincides with a full–state feedback, linear time–invariant proportional plus integral (PI) controller with a decaying additive disturbance. Moreover, it is shown that if the PI controller does not stabil...

In this paper, adaptive neural network sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. The adaptive neural sliding mode control system is comprised of neural network (NN) and a compensation controller. The NN is the main tracking controller, which i...

Abstrud-In this paper, a discrete model reference adaptive controller is designed and implemented to let the performance of the field-oriented induction motor drives be insensitive to the parameter changes. Only the information of the reference model and the plant output are required for the control. No online explicit parameter identification is required. Hence, the proposed controller is easy...

In this paper control of a flexible joint manipulator is designed using an adaptive output-feedback controller based on a backstepping design. The parameters of system are assumed to be unknown and only motor position and link position measurements are used for the synthesis of the controller. A canonical state representation of the system is derived and filters are designed to obtain the estim...

Abstract: In this paper, a type-2 fuzzy adaptive controller of a Lotak-Volterra system is considered. For bettering the active system properly, the approximated error is introduced into the design of the controller. By using Lyapunov synthesis approach, we attain an adaptive law adjusting the free parameters in the controller such that the closed-loop system follows a desired trajectory. The no...

For a class of uncertain systems we analyze passification-based adaptive controller in the presence of small, unavoidable input and output time-varying delays as may be present in controller implementation. We derive upper bounds for time delays such that in some domain of initial conditions the states of the closed-loop system tend to zero, whereas an adaptive controller gain tends to a consta...

An indirect adaptive controller is developed for a class of multiple-input multiple-output (MIMO) nonlinear systems with unknown uncertainties. This control system is comprised of an L 1 adaptive controller and an auxiliary neural network (NN) compensation controller. The L 1 adaptive controller has guaranteed transient response in addition to stable tracking. In this architecture, a low-pass f...

This paper discusses a model reference adaptive sliding-mode control of the sensorless vector controlled induction motor drive in a wide speed range. The adaptive speed controller uses on-line trained fuzzy neural network, which enables very fast tracking of the changing speed reference signal. This adaptive sliding-mode neuro-fuzzy controller (ASNFC) is used as a speed controller in the direct...

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed cont...