This paper introduces a new decentralized adaptive neural network controller for a class of large-scale nonlinear systems with unknown non-affine subsystems and unknown interconnections represented by nonlinear functions. A multilayer feedforward neural network is used to represent the controller’s structure. The ultimate boundedness of the closed loop system is guaranteed through Lyapunov stab...

Abstract: In this paper an application of the adaptive neuro-fuzzy inference system has been introduced to predict the behavior of a chaotic robot. The chaotic mobile robot implies a mobile robot with a controller that ensures chaotic motions. Chaotic motion is characterized by the topological transitivity and the sensitive dependence on initial conditions. We have used the controller such that...

An approach to control non-linear objects based on evolving Rule-based (eR) models is presented in the paper. Fuzzy rules, representing the structure of the controller are generated based on data collected during the process of control using newly introduced technique for on-line identification of Takagi-Sugeno type of fuzzy rule-based models. Initially, the process is supposed to be controlled...

This paper proposes a new adaptive controller for the robust control of a grid-connected multi-DG microgrid (MG) with the main aim of output active power and reactive power regulation as well as busbar voltage regulation of DGs. In addition, this paper proposes a simple systematic method for the dynamic analysis including the shunt and series faults that are assumed to occur in the MG. The pres...

in this paper, an observer based fuzzy adaptive controller (fac) is designed fora class of large scale systems with non-canonical non-affine nonlinear subsystems. it isassumed that functions of the subsystems and the interactions among subsystems areunknown. by constructing a new class of state observer for each follower, the proposedconsensus control method solves the problem of unmeasured sta...

Power system stabilizers (PSSs) must be capable of providing appropriate stabilization signals over abroad range of operating conditions and disturbances. The main idea of this paper is changing aclassic PSS (CPSS) to an adaptive PSS using genetic algorithm. This new genetic algorithm based onadaptive PSS (GAPSS) improves power system damping, considerably. The controller design issue isformula...

This paper presents a direct neural-fuzzy-based Model Reference Adaptive Controller (MRAC) for nonlinear dynamical systems with unknown parameters. The two-phase learning is implemented to perform structure identification and parameter estimation for the controller. In the first phase, similarity index-based fuzzy c-means clustering technique extracts the fuzzy rules in the premise part for the...

During the machining process of a complex rotating proÞle, numerous nonlinear characteristics of the process come into effect. It is thus difficult to control such a system efficiently. In this paper we propose a method to control such systems. We develop a direct adaptive control scheme for such processes, with a dynamic neural network as a controller. According to the idea of a direct adaptiv...

This paper presents a new adaptive fuzzy wavelet network controller (A-FWNC) for control of nonlinear affine systems, inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts. The proposed adaptive gain controller, which results from the direct adaptive approach, has the ability to tune the adaptation parameter in the THEN-part of each fuzzy rule during ...

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold ...