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 paper presents designing an optimal adaptive controller for tracking control of robot manipulators based on particle swarm optimization (pso) algorithm. pso algorithm has been employed to optimize parameters of the controller and hence to minimize the integral square of errors (ise) as a performance criteria. in this paper, an improved pso using logic is proposed to increase the convergenc...

A large class of problems in parameter estimation concerns nonlinearly parametrized systems. Over the past few years, a stability framework for estimation and control of such systems has been established. We address the issue of parameter convergence in such systems in this paper. Systems with both convex/concave and general parameterizations are considered. In the former case, sufficient condi...

This paper presents two adaptive control laws that have improved parameter convergence properties. These adaptive control laws are derived through the framework of Model Reference Adaptive Control and are applicable to plants with structured or unstructured modeling uncertainty. The presented adaptive control laws use both recorded and current data concurrently to improve parameter convergence ...

A position and attitude tracking control law for autonomous underwater vehicles (AUVs) in 6 degrees of freedom (DOF) is derived. The 4-parameter unit quaternion (Euler parameters) is used in a singularity-free representation of attitude. Global convergence of the closed-loop system is proven. In addition several 3-parameter representations in terms of the Euler parameters are discussed with app...

In this article, the output feedback-based direct model reference adaptive control of piecewise affine systems and its parameter convergence are investigated. Under slow switching assumption, it is shown that all closed-loop signals bounded tracking error small in mean square sense. Built upon result, estimation controller parameters proved to converge a residual set if input signal sufficientl...

in this study, based on the optimal free derivative without memory methods proposed by cordero et al. [a. cordero, j.l. hueso, e. martinez, j.r. torregrosa, generating optimal derivative free iterative methods for nonlinear equations by using polynomial interpolation, mathematical and computer modeling. 57 (2013) 1950-1956], we develop two new iterative with memory methods for solving a nonline...

Adaptive feedback linearizing control schemes are used to suppress limit cycle oscillations in nonlinear systems where the system parameters are either unknown or uncertain. Parameter convergence is desirable in these schemes as it provides a measure of robustness of the scheme and also permits the unknown/uncertain system parameters to be estimated. In recent work, we have shown how using a pe...

Convergence of controller parameters in standard model reference adaptive control (MRAC) requires the system states to be persistently exciting (PE), a restrictive condition to be verified online. A recent data-driven approach, concurrent learning, uses information-rich past data concurrently with the standard parameter update laws to guarantee parameter convergence without the need of the PE c...