In this paper, the problems faced in the constrained force control is studied (uncertainties in dynamic model and the unknown constraints). A neural network (NN) controller is proposed based on the derived dynamic model of robot in the task space. The feed-forward neural network is used to adaptively compensate for the uncertainties in the robot dynamics. Training signals are proposed for the f...

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...

The conventional PID (proportional-integralderivative) controller is widely applied to industrial automation and process control field because its structure is sample and its robust is well, but it do not work well for nonlinear system, time-delayed linear system and timevarying system. This paper provides a new style of PID controller that is based on artificial neural network and evolutionary...

This paper presents a direct multivariable adaptive controller using neural network which adapts to the changing parameters of the multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. It base on the theory which a nonlinear multivariable systems to be controlled is divided a linear part and a nonlinear part. The controller parameters of the linear ...

In this study, a robust neuro-adaptive controller for cable-driven parallel robots is proposed. The robust neuroadaptive control system is comprised of a computation controller and a robust controller. The computation controller containing a neural-network-estimator with radial basis function activator is the principal controller and the robust controller is designed to achieve tracking perform...

This paper considers the identification and fuzzy controller design for nonlinear uncertain systems in presence of unknown input time-delay. Firstly, a time-delay Takagi-Sugeno-Kang (TSK) type fuzzy neural system (TDFN) is proposed to identify a class of nonlinear input time-delay systems. The input-output signals of nonlinear systems are used to identify the system dynamics and unknown time-de...

This paper presents a Multilayer Neural Network controller for real time control applications. A model reference structure is developed and a neural network is used as a compensator in the closed loop system. This scheme can be used in the control of nonlinear systems and/or as an adaptive controller if desired.

This paper proposes a direct adaptive control strategy for a class of nonlinear systems for which the dynamics is incompletely known and time varying. The nonlinear controller design is based on the input-output linearizing technique. The only information required about the process is the measurements of the state variables and its relative degree. Unknown controller functions are approximated ...

This paper presents the design of a neural network based feedback linearization (NNFBL) controller for a two degree-offreedom (DOF), quarter-car, servo-hydraulic vehicle suspension system. The main objective of the direct adaptive NNFBL controller is to improve the system’s ride comfort and handling quality. A feedforward, multi-layer perceptron (MLP) neural network (NN) model that is well suit...

Automatic and robust ink feed control in a webfed offset printing press is the objective of this work. To achieve this goal an integrating controller and a multiple neural models-based controller are combined. The neural networks-based printing process models are built and updated automatically without any interaction from the user. The multiple models-based controller is superior to the integr...