Name
Abstract | ||
|---|---|---|
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/5326.740668 | IEEE Transactions on Systems, Man, and Cybernetics, Part C |
Keywords | Field | DocType |
lyapunov synthesis technique,lyapunov synthesis approach,adaptive control scheme,neural network,overall control scheme,novel neural network architecture,nonlinear system,adaptive control algorithm,adaptive control,gaussian radial basis function,variable neural network,dynamic system,orthogonality,control systems,accuracy,indexing terms,smoothness,neural networks,optimal control,network synthesis,stability,model error,radial basis function,nonlinear systems,basis functions,design strategies | Lyapunov function,Feedforward neural network,Computer science,Control theory,Stochastic neural network,Recurrent neural network,Probabilistic neural network,Time delay neural network,Artificial intelligence,Adaptive control,Artificial neural network,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 1 | 1094-6977 |
Citations | PageRank | References |
47 | 3.83 | 22 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. P. Liu | 1 | 48 | 4.19 |
| V. Kadirkamanathan | 2 | 355 | 39.25 |
| S. A. Billings | 3 | 365 | 60.58 |