This paper develops a design methodology of sliding mode ANFIS-Based multi-inputs multi-outputs (MIMO) fuzzy neural network (AMFNN) control for robotic systems. This control system consists of a sliding mode (SM) controller and an AMFNN controller. The SM controller is used to deal with uncertain parts of system dynamics and external disturbances and the AMFNN controller is served as a controll...

A guidance law, based on supervisory recurrent fuzzy neural network control (SRFNNC), is proposed for the autonomous underwater vehicle (AUV) guidance systems. This SRFNNC system is comprised of a recurrent fuzzy neural network (RFNN) controller and a supervisory controller. The RFNN controller is used to mimic an ideal controller and the supervisory controller is designed to compensate for the...

In this paper, a decentralized adaptive neural controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, non-affine subsystems and unknown nonlinear time-delay interconnections. The stability of the closed loop system is guaranteed through Lyapunov-Krasovskii stability analysis. Simulation results are provided to show the effectiveness of the proposed approache...

Induction motors are the workhorses of industries. Indirect vector control scheme has been preferred due to its superior dynamic performance. Since the conventional PI controller has bounded operating limits and poor transient response, a search for an alternative controller arises. Recently, Artificial Neural Network (ANN) is gaining momentum as a controller for non linear systems. Herein an a...

A direct adaptive simultaneous perturbation stochastic approximation (DA SPSA) control system with a diagonal recurrent neural network (DRNN) controller is proposed. The DA SPSA control system with DRNN has simpler architecture and parameter vector size that is smaller than a feedforward neural network (FNN) controller. The simulation results show that it has a faster convergence rate than FNN ...

This paper presents the design, development and implementation of an Adaptive Recurrent Neural Networks (ARNN) controller suitable for real-time manipulator control applications. The unique feature of the ARNN controller is that it has dynamic self-organizing structure, fast learning speed, good generalization and flexibility in learning. The proposed adaptive algorithm focuses on fast and effi...

The paper presents the training problem of a set of neural nets to obtain a (gain-scheduling, adaptive) multivariable neural controller for control of a nonlinear MIMO dynamic process represented by a mathematical model of Low-Frequency (LF) motions of a drillship over the drilling point at the sea bottom. The designed neural controller contains a set of neural nets that determine values of its...

In this paper, an intelligent robust position controller for permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent robust position controller consists of a two-degrees-offreedom integral plus proportional & rate feedback (2DOF I-PD) controller in addition to an on-line trained neural-network model-following controller (NNMFC). This controller, 2DOF I-PD NNMFC, comb...

What led to the novel ideas, some of them theoretical, presented in this paper is our highly experimental approach to nonlinear control. We actually designed and fabricated a two-link inverted pendulum, and learned about the relationships and the tradeoffs that exist in such a device. In the context of controlling an inverted pendulum, the novel ideas presented in this paper relate to i) the de...

An adaptive truss structure with self-learning active vibration control system is developed. A fuzzy-neural network (FNN) controller with adaptive membership functions is presented. The experimental setup of a two-bay truss structure with active members is constructed, and the FNN controller is applied to vibration suppression of the truss. The controller first senses the output of the accelero...