Name
Title | ||
|---|---|---|
Radial basis functions and multilayer feedforward neural networks for optimal control of nonlinear stochastic systems |
Abstract | ||
|---|---|---|
The problem of designing a feedback feedforward controller to drive the state of a dynamic system so as to track any desired stochastically specified trajectory is addressed. In general, the dynamic system and the state observation channel are nonlinear, the cost function is non-quadratic, and process and observation noises are non-Gaussian. As the classical linear-quadratic-Gaussian (LQG) assumptions are not verified, an approximate solution is sought by constraining control strategies to take on a fixed structure in which a certain number of parameters have to be optimized. Two nonlinear control structures are considered, i.e., radial basis functions (RBFs) and multilayer feedforward neural networks. The control structures are also shaped on the basis of the linear structure preserving principle (the LISP principle). The original functional problem is then reduced to a nonlinear programming one, which is solved by means of a gradient method. Simulation results related to non-LQG optimal control problems show the effectiveness of the proposed technique |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1109/ICNN.1993.298839 | San Francisco, CA |
Keywords | Field | DocType |
control system synthesis,feedforward neural nets,nonlinear programming,nonlinear systems,optimal control,stochastic systems,dynamic system,feedback feedforward controller,linear structure preserving principle,linear-quadratic-gaussian,multilayer feedforward neural networks,nonlinear stochastic systems,radial basis functions,constraint optimization,state observer,control structure,linear quadratic gaussian,neurofeedback,control systems,cost function,radial basis function,trajectory,feedforward neural networks,neural networks,gradient method,nonlinear control | Feedforward neural network,Control theory,Nonlinear system,Optimal control,Linear-quadratic-Gaussian control,Control theory,Nonlinear control,Nonlinear programming,Mathematics,Feed forward | Conference |
Citations | PageRank | References |
2 | 0.43 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| T Parisini | 1 | 935 | 113.17 |
| R. Zoppoli | 2 | 279 | 51.51 |