This paper presents an adaptive nonlinear position controller with velocity constraint, capable of combining the input-output linearization technique and Lyapunov stability theory. Based on the Lyapunov stability theory, the adaptation law of the proposed controller is derived along with the verification of the overall system’s stability. Computer simulation results demonstrate that the propose...

Control of a class of uncertain nonlinear systems, which estimates unavailable state variables, is considered. A new approach for robust tracking control problem of satellite for large rotational maneuvers is presented in this paper. The features of this approach include a strong algorithm to estimate attitude, based on discrete extended Kalman filter combined with a continuous extended Kalman ...

This paper presents an investigation on the trajectory control of a robot using a new type of recurrent neural network. A three-layered recurrent neural network is employed to estimate the forward dynamics model of the robot. Standard backpropagation (BP) algorithm is used as a learning algorithm for this network to minimise the difference between the robot actual response and that predicted by...

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 ...

A neural network robust controller (NNRC) to enhance the robustness of the conventional feedback controller is proposed in this paper. The control system consists of an optimal conventional feedback controller and a neural network robust controller in parallel to the controlled system. The optimal controller is used to guarantee the stability of the whole control system and present the optimal ...

In the present paper we propose a globally convergent adaptive control scheme for robot motion control with the following features: First, the adaptation law possesses enhanced robustness with respect to noisy velocity measurements. Secondly, the controllex does not require the inclusion of high gain loops that may excite the unmodeled dynamics and amplify the noise level. Thirdly, we derive fo...

Control robustness is study for uncertain nonlinear systems transformed in semi strict feedback systems. An adaptive backstepping sliding mode control and an adaptive backstepping control robustness is considered. Robustness is studied when the unknown parameter are constants and varied. The simulation results prove that the sliding mode control is more robust than the backstepping control to t...

The authors develop a systematic procedure for obtaining robust adaptive controllers that achieve asymptotic tracking and disturbance attenuation for a class of nonlinear systems that are described in the parametric strict-feedback form and are subject to additional exogenous disturbance inputs. Their approach to adaptive control is performance-based, where the objective for the controller desi...

This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory controller without any modelling information. ASMCNN designed have robust Robot Manipulator, which combines Estimation Control. mode...