Home  About us  Contact
 

Continuous-time Nonlinear Systems (continuous-time + nonlinear_system)

Distribution by Scientific Domains
Engineering100%

Selected Abstracts

Numerical nonlinear observers using pseudo-Newton-type solvers

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 17 2008
Shigeru HanbaArticle first published online: 12 DEC 200
Abstract In constructing a globally convergent numerical nonlinear observer of Newton-type for a continuous-time nonlinear system, a globally convergent nonlinear equation solver with a guaranteed rate of convergence is necessary. In particular, the solver should be Jacobian free, because an analytic form of the state transition map of the nonlinear system is generally unavailable. In this paper, two Jacobian-free nonlinear equation solvers of pseudo-Newton type that fulfill these requirements are proposed. One of them is based on the finite difference approximation of the Jacobian with variable step size together with the line search. The other uses a similar idea, but the estimate of the Jacobian is mostly updated through a BFGS-type law. Then, by using these solvers, globally stable numerical nonlinear observers are constructed. Numerical results are included to illustrate the effectiveness of the proposed methods. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Identification of continuous-time nonlinear systems by using a gaussian process model

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 6 2008
Tomohiro Hachino Member
Abstract This paper deals with a nonparametric identification of continuous-time nonlinear systems by using a Gaussian process model. Genetic algorithm is applied to train the Gaussian process prior model by minimizing the negative log marginal likelihood of the identification data. The nonlinear term of the objective system is estimated as the predictive mean function of the Gaussian process, and the confidence measure of the estimated nonlinear function is given by the predictive covariance function of the Gaussian process. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]

Constructive model predictive control for constrained nonlinear systems

OPTIMAL CONTROL APPLICATIONS AND METHODS, Issue 6 2008
De-Feng He
Abstract This paper develops a new model predictive control (MPC) design for stabilization of continuous-time nonlinear systems subject to state and input constraints. The key idea is to construct an analytic form of the controller with some undetermined parameters and to calculate the parameters by minimizing online a performance index. By using the method of control Lyapunov functions (CLFs), we construct an appropriate variation on Sontag's formula, with one degree of freedom reflecting ,decay rate' of CLFs. Moreover, the constructed univariate control law is used to characterize the terminal region that guarantees the feasibility of the optimal control problem. Provided that the initial feasibility of the optimization problem is satisfied, the stability of the control scheme can be guaranteed. An example is given to illustrate the application of the constructive MPC design. Copyright © 2008 John Wiley & Sons, Ltd. [source]

State waypoint approach to continuous-time nonlinear optimal control problems

ASIAN JOURNAL OF CONTROL, Issue 6 2009
Mohamadhadi Honarvarmahjoobin
Abstract In this paper, we propose an optimal control technique for a class of continuous-time nonlinear systems. The key idea of the proposed approach is to parametrize continuous state trajectories by sequences of a finite number of intermediate target states; namely, waypoint sequences. It is shown that the optimal control problem for transferring the state from one waypoint to the next is given an explicit-form suboptimal solution, by means of linear approximation. Thus the original continuous-time nonlinear control problem reduces to a finite-dimensional optimization problem of waypoint sequences. Any efficient numerical optimization method, such as the interior-reflection Newton method, can be applied to solve this optimization problem. Finally, we solve the optimal control problem for a simple nonlinear system example to illustrate the effectiveness of this approach. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source]