The design and application of indirect adaptive fuzzy controller is developed and applied to Unmanned Aerial Vehicles (UAVs). The parameters of identified model are adapted on-line based on the error between the identified model and the actual output. The model process sensitivity factor m y u ∂ ∂ and the error between the reference input and process output are used to adapt the controller para...

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...

An indirect adaptive control for an autonomous underwater vehiclemanipulator system (UVMS) based on an extended Kalman filter (EKF) is presented in this paper. This method overcomes the disadvantages of existing disturbance observers and direct adaptive control schemes, which are designed or analyzed by linear system techniques and regressor-based techniques. The proposed control scheme can be ...

This paper investigates robust adaptive control for unknown nonlinear systems with fully connected recurrent neural networks. On-line weights updating law and closed loop performance are derived from the Lyapunov approach. Mathematical proof for the robust stability under the parametric uncertainties due to disturbances of the overall system is provided. This analysis is concerned by combining ...

A nonlinear adaptive framework for bounded-error tracking control of a class of non-minimum phase marine vehicles is presented. The control algorithm relies on a special set of tracking errors to achieve satisfactory tracking performance while guaranteeing stable internal dynamics. First, the design of a model-based nonlinear control law, guaranteeing asymptotic stability of the error dynamics,...

This paper presents an indirect adaptive control scheme for a pH process, which is diicult to control due to its nonlinear dynamics with uncertainties. A nonlinear controller is designed using a backstepping technique which can deal with a large class of nonlinear systems with parametric uncertainty. The nonlinear controller is combined with a parameter estimator to estimate the concentrations ...

In a general DIARC framework [13], the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in the presence of uncertain nonlinearity and parametric uncertainties along with accurate parameter estimation for secondary purpose such as system health monitoring and prognosis. Need for accurate parameter estimation calls for the use of Least Square Estimation ...

The paper proposed a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants. The proposed RNN identifier is implemented in direct and indirect adaptive control schemes, incorporating a noise rejecting plant output filter and recurrent neural or linear-sliding mode controllers. For sake of comparison, the RNN model is learned both by the bac...