this paper presents a discrete-time robust control for electrically driven robot manipulators in the task space. a novel discrete-time model-free control law is proposed by employing an adaptive fuzzy estimator for the compensation of the uncertainty including model uncertainty, external disturbances and discretization error. parameters of the fuzzy estimator are adapted to minimize the estimat...

During the last two decades, there has been significant progress in the area of adaptive control design of nonlinear systems (Krstic et al., 1995; Sastry & Isidori, 1989; Slotine & Li 1991; Spooner et al., 2002). Most of the developed adaptive control schemes assume that an accurate model of the system is available and the unknown parameters appear linearly with respect to known nonlinear funct...

Abst rac t The slip power recovery configuration is an attractive scheme of variable speed drive, with high efficiency and low converter rating; however, high performance control has being difficult. In this paper, novel applications of fuzzy logic for the intelligent control of a slip power recovery system are presented. A direct fuzzy logic controller and an adaptive fuzzy controller, based o...

In this paper a novel direct adaptive fuzzy system is proposed to control flexible-joints robot including actuator dynamics. The design includes two interior loops: the inner loop controls the motor position using proposed approach while the outer loop controls the joint angle of the robot using a PID control law. One novelty of this paper is the use of a PSO algorithm for optimizing the contro...

this paper presents a novel indirect adaptive power system stabilizer (pss) via a developed synergetic control methodology and fuzzy systems. fuzzy system is utilized in an adaptive scheme to estimate the system using a nonlinear model. the synergetic control guarantees robustness of the controller and makes the controller easy to implement because of using a chatter free continuous control law...

This paper studies the iterative learning control of robotic systems with repetitive tasks. A fuzzy neural network is applied to design a direct adaptive iterative learning controller. The fuzzy neural network is introduced for compensation of the unknown certainty equivalent controller. A new adaptive law using mixed time-domain and iteration-domain adaptation is developed. It is shown that th...

in this paper a novel direct adaptive fuzzy system is proposed to control flexible-joints robot including actuator dynamics. the design includes two interior loops: the inner loop controls the motor position using proposed approach while the outer loop controls the joint angle of the robot using a pid control law. one novelty of this paper is the use of a pso algorithm for optimizing the contro...

This paper analyses coupled control of a nonlinear, time-varying plant. Uncoupled and coupled direct adaptive controllers, based on fuzzy logics, are synthesized to control the water level and the air pressure in a closed tank. The satisfactory efficiency of the controllers is experimentally demonstrated running the plant under the different working conditions. Coupled fuzzy controllers are com...

Abstract: In this paper, in order to achieve tracking performance of uncertain fractional order chaotic systems an adaptive hybrid fuzzy controller is proposed. During the design procedure, a hybrid learning algorithm combining sliding mode control and Lyapunov stability criterion is adopted to tune the free parameters on line by output feedback control law and adaptive law. A weighting factor,...

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...