Output feedback control of nonlinear systems using RBF neural networks

Abstract

An adaptive output feedback control scheme for the output tracking of a class of continuous-time nonlinear plants is presented. An RBF neural network is used to adaptively compensate for the plant nonlinearities. The network weights are adapted using a Lyapunov-based design. The method uses parameter projection, control saturation, and a high-gain observer to achieve semi-global uniform ultimate boundedness. The effectiveness of the proposed method is demonstrated through simulations. The simulations also show that by using adaptive control in conjunction with robust control, it is possible to tolerate larger approximation errors resulting from the use of lower order networks.

Characteristics and Prediction of Frost Heave of Saline Soil in Western Jilin Province

“…Neural network structure of RBF RBF, a kind of efficient feed-forward network, is based on function approximation theory [14]. As shown in Figure 1, RBF is formed of three layers, namely the input layer, hidden layer and output layer.…”

Robust Adaptive Control of Transverse Flux Permanent Magnet Machines Using Neural Networks

“…High gain and control saturation has also been considered in a similar way by (Jankovic, 1996;Seshagiri and Khalil, 2000) and they showed that the tracking error can be made as small as desired by increasing the observer and parameter adaptation gains.…”

Adaptive nonlinear control using input normalized neural networks

“…[9][10][11][12][13] Adaptive output feedback control using a high-gain observer and radial basis function neural network was proposed for nonlinear systems represented by input-output models. [14,15] Also, a nonlinear adaptive flight control system was designed by backstepping and neural network controller. [16] In the previous works, stability analysis of the closed-loop system using the neural network is rather involved in mathematical development.…”