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...

Iterative data-based controller tuning consists of iterative adjustment of the controller parameters towards the parameter values which minimize an H2 performance criterion. The convergence to the global minimum of the performance criterion depends on the initial controller parameters and on the step size of each iteration. This paper presents convergence properties of iterative algorithms when...

this paper deals with a globally convergent adaptive and sliding mode control of a cart-pole inverted pendulum for trajectory tracking in the presence of a bounded measurement noise and parameter uncertainty. two kinds of controllers have been used for evaluation of tracking error in presence of a bounded noise; as a result, we want to compare that at what time we can see the convergence of tra...

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...

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...

In this paper, quadratic stability of adaptive dynamic surface control for a class of nonlinear systems in strict-feedback form is analyzed in the framework of linear matrix inequality. While the existence of controller gains and filter time constants for semi-global stability was theoretically proved in the literature, it is not sufficient to describe how a set-point value and parameter update...

Dynamic surface control (DSC) method uses high gain filters to avoid the ”explosion of complexity” issue inherent in backstepping based controller designs. As a result, the closed loop system and filter dynamics possess time scale separation between them. This paper attempts to design a novel disturbance observer based dynamic surface controller using contraction framework. In doing so the stea...

this paper proposes a novel robust control scheme based on delay-dependent h∞for unified power quality conditioner (upqc) in a microgrid under the influence of the delay and parameter uncertainties. a new upqc model considering the effects of the delay and parameter uncertainties is established. then, the h∞ controller is designed based on the cone complementarity linearization (ccl) algorithm....

this paper proposes a new method of gain scheduling control design for a nonlinear system which is described as linear parameter varying form. a performance measure based on linear matrix inequality is introduced. to consider stability and performance measures in design process, the h∞ loop-shaping method is used to design the local controllers, which can be described as state feedback observer...