an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...

Real-time robust adaptive fuzzy fractional-order control of electrically driven flexible-joint robots has been addressed in this paper. Two important practical situations have been considered: the fact that robot actuators have limited voltage, and the fact that current signals are contaminated with noise. Through of a novel voltage-based fractional order control for an integer-order dynamical ...

Torque ripple minimization of switched reluctance motor drives is a major subject based on these drives’ extensive use in the industry. In this paper, by using a well-known cascaded torque control structure and taking the machine physical structure characteristics into account, the proposed energy-based (passivity-based) adaptive sliding algorithm derived from the view point of energy dissipati...

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...

In a general Adaptive Robust Control (ARC) framework, the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in the presence of uncertain nonlinearity and parametric uncertainties. However, when secondary purposes such as system health monitoring and prognosis are of equal importance, intelligent integration of output tracking performance oriented direct...

In this paper, a class of uncertain chaotic systems preceded by unknown backlash nonlinearity is investigated. Combining backstepping technique with fuzzy neural network identifying, an adaptive backstepping fuzzy neural controller (ABFNC) for uncertain chaotic systems with unknown backlash is proposed. The proposed ABFNC system is comprised of a fuzzy neural network identifier (FNNI) and a rob...

The objective of this study is to design a robust direct model reference adaptive controller (DMRAC) for a nonlinear cardiovascular model over a range of plant parameters representing a variety of physical conditions. The direct adaptive controllers used in thisd study require the plant to be almost strictly positive real (ASPR) that is, for a plant to be controlled there must exist a feedback ...

In this paper, a new adaptive robust control scheme is developed for a class of uncertain dynamical systems with time-varying state delay, unknown parameters and disturbances. By incorporating adaptive techniques into the robust control method, we propose a continuous adaptive robust controller which guarantees the uniform boundedness of the system and at the same time, the regulating error ent...

This paper suggests a method for designing PSS to damp multi-machine power system oscillations.  The method is based on robust control theory. First, the conventional method for designing robust controller in LMI framework is illustrated. Then, the suggested method is given, in which, a PID output feedback controller is tuned using the LMI approach. Mostly, the classical methods are used t...

In this paper, an adaptive nonlinear controller is designed for rotor Surface Permanent Magnet Synchronous Motor (SPMSM) drive on the basis of Input-Output Feedback Control (IOFC), and Recursive Least Square (RLS) method. The RLS estimator detects the motor electromechanical parameters, including the motor iron loss resistance online. Moreover, a Sliding-Mode (SM) observer is developed for onli...