Displaying similar documents to “Event-triggered optimal control of completely unknown nonlinear systems via identifier-critic learning”

Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks

Zahir Ahmida, Abdelfettah Charef, Victor Becerra (2005)

International Journal of Applied Mathematics and Computer Science

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A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed on-line by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear...

Robust optimal PID controller design for attitude stabilization of flexible spacecraft

Chutiphon Pukdeboon (2018)

Kybernetika

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This paper presents a novel robust optimal control approach for attitude stabilization of a flexible spacecraft in the presence of external disturbances. An optimal control law is formulated by using concepts of inverse optimal control, proportional-integral-derivative control and a control Lyapunov function. A modified extended state observer is used to compensate for the total disturbances. High-gain and second order sliding mode algorithms are merged to obtain the proposed modified...

An optimal sliding mode congestion controller for connection-oriented communication networks with lossy links

Andrzej Bartoszewicz, Piotr Leśniewski (2014)

International Journal of Applied Mathematics and Computer Science

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A new discrete-time sliding-mode congestion controller for connection-oriented networks is proposed. Packet losses which may occur during the transmission process are explicitly taken into account. Two control laws are presented, each obtained by minimizing a different cost functional. The first one concentrates on the output variable, whereas in the second one the whole state vector is considered. Weighting factors for adjusting the influence of the control signal and appropriate (state...

Approach to the design of robust networked control systems

Michał Morawski, Antoni M. Zajączkowski (2010)

International Journal of Applied Mathematics and Computer Science

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The paper describes the application of the traffic engineering framework together with application layer procedures as mechanisms for the reduction of network latency lags. These mechanisms allow using standard and inexpensive hardware and software technologies typically applied for office networking as a means of realising networked control systems (NCSs) with high dynamic control plants, where a high dynamic control plant is the one that requires the sampling period several times shorter...

Inverse optimal control for linearizable nonlinear systems with input delays

Xiushan Cai, Jie Wu, Xisheng Zhan, Xianhe Zhang (2019)

Kybernetika

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We consider inverse optimal control for linearizable nonlinear systems with input delays based on predictor control. Under a continuously reversible change of variable, a nonlinear system is transferred to a linear system. A predictor control law is designed such that the closed-loop system is asymptotically stable. We show that the basic predictor control is inverse optimal with respect to a differential game. A mechanical system is provided to illustrate the effectiveness of the proposed...

Objective function design for robust optimality of linear control under state-constraints and uncertainty

Fabio Bagagiolo, Dario Bauso (2011)

ESAIM: Control, Optimisation and Calculus of Variations

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We consider a model for the control of a linear network flow system with unknown but bounded demand and polytopic bounds on controlled flows. We are interested in the problem of finding a suitable objective function that makes robust optimal the policy represented by the so-called linear saturated feedback control. We regard the problem as a suitable differential game with switching cost and study it in the framework of the viscosity solutions theory for Bellman and Isaacs equations. ...

Objective function design for robust optimality of linear control under state-constraints and uncertainty

Fabio Bagagiolo, Dario Bauso (2011)

ESAIM: Control, Optimisation and Calculus of Variations

Similarity:

We consider a model for the control of a linear network flow system with unknown but bounded demand and polytopic bounds on controlled flows. We are interested in the problem of finding a suitable objective function that makes robust optimal the policy represented by the so-called linear saturated feedback control. We regard the problem as a suitable differential game with switching cost and study it in the framework of the viscosity solutions theory for Bellman and Isaacs equations. ...

A nonlinear dynamic inversion-based neurocontroller for unmanned combat aerial vehicles during aerial refuelling

Jimoh Olarewaju Pedro, Aarti Panday, Laurent Dala (2013)

International Journal of Applied Mathematics and Computer Science

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The paper presents the development of modelling and control strategies for a six-degree-of-freedom, unmanned combat aerial vehicle with the inclusion of the centre of gravity position travel during the straight-leg part of an in-flight refuelling manoeuvre. The centre of gravity position travel is found to have a parabolic variation with an increasing mass of aircraft. A nonlinear dynamic inversion-based neurocontroller is designed for the process under investigation. Three radial basis...

Supervisory predictive control and on-line set-point optimization

Piotr Tatjewski (2010)

International Journal of Applied Mathematics and Computer Science

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The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear...