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A neural network solution for fixed-final time optimal control of nonlinear systems
2007
Automatica
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In this paper, fixed-final time optimal control laws using neural networks and HJB equations for general affine in the input nonlinear systems are proposed. ...
The result is a neural network feedback controller that has time-varying coefficients found by a priori offline tuning. Convergence results are shown. ...
Simulation We now show the power of our NN control technique for finding nearly optimal fixed-final time controllers to a mobile robot, which is a nonholonomic system (Kolmanovsky & McClamroch, 1995) ...
doi:10.1016/j.automatica.2006.09.021
fatcat:kh5isoqinjak3kjelde72dpxz4
A Neural Network Solution for Fixed-Final Time Optimal Control of Nonlinear Systems
2006
2006 14th Mediterranean Conference on Control and Automation
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Public Reporting Burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining ...
Send comment regarding this burden estimate or any other aspect of this collection of information, including suggesstions for reducing this burden, to Washington Headquarters Services, Directorate for ...
the solution to fixed-final time optimal control for general nonlinear systems. ...
doi:10.1109/med.2006.328821
fatcat:o4tdhz3tm5adxi3qhvtxjgabxu
Neural Control Toward a Unified Intelligent Control Design Framework for Nonlinear Systems
[chapter]
2011
Recent Advances in Robust Control - Novel Approaches and Design Methods
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The study begins with a generic presentation of the solution scheme for fixed-parameter nonlinear systems. ...
a fixed parameter vector, the control solution characterized by a set of optimal state and control trajectories shall be approximated by a neural network, which may be called a nominal neural network for ...
As shown in (Chen, Yang & Moher, 2006) , the desired prediction or control can be achieved by a properly designed hierarchical neural network. ...
doi:10.5772/16593
fatcat:unsybtsxfrbptmc2g6vzvpe5t4
Adaptive critic based neural networks for control-constrained agile missile control
1999
Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251)
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In this study we investigate the use of an 'adaptive critic' based controller to steer an agile missile with a constraint on the angle of attack from various initial Mach numbers to a given final Mach ...
We use neural networks with a two-network structure called 'adaptive critic' in this study to carry out the optimization process. ...
The neural network controllers are able to provide (near) optimal control to the missile from an envelope of initial Mach numbers to a fixed final Mach number of 0.8 in minimum time. ...
doi:10.1109/acc.1999.786536
fatcat:j24n5h273fbpbjzoafp54lr5ym
Fixed-Final-Time-Constrained Optimal Control of Nonlinear Systems Using Neural Network HJB Approach
2007
IEEE Transactions on Neural Networks
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In this paper, fixed-final time-constrained optimal control laws using neural networks (NNS) to solve Hamilton-Jacobi-Bellman (HJB) equations for general affine in the constrained nonlinear systems are ...
Index Terms-Constrained input systems, finite-horizon optimal control, Hamilton-Jacobi-Bellman (HJB), neural network (NN) control. ...
Substituting (6) into (5) yields the well-known time-varying HJB equation [33] (7) Equations (6) and (7) provide the solution to fixed-final-time optimal control for affine nonlinear systems. ...
doi:10.1109/tnn.2007.905848
fatcat:glkjxxsjevagzgq6hjl43khy6q
Optimal Neuro-controller Synthesis for Impulse-Driven System
2007
American Control Conference (ACC)
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This paper presents a new controller design technique for systems driven with impulse inputs. Necessary conditions for optimal impulse control are derived. ...
A neural network structure to solve the resulting equations is presented. The solution concepts are illustrated with a few example problems that exhibit increasing levels of difficulty. ...
Infinite Time Adaptive Critic Neural Network Scheme For infinite time optimal control, the mapping between the states and the costates is not a function of time. ...
doi:10.1109/acc.2007.4282911
dblp:conf/acc/WangB07
fatcat:kndleztt3zgmjcs76ifhedzhoe
Neural ODEs as Feedback Policies for Nonlinear Optimal Control
[article]
2022
arXiv
pre-print
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In this work we propose the use of a neural control policy capable of satisfying state and control constraints to solve nonlinear optimal control problems. ...
Neural ordinary differential equations (Neural ODEs) define continuous time dynamical systems with neural networks. ...
ACKNOWLEDGEMENTS The authors would like to thank Christopher Rackauckas and the Julia language community for helpful discussions on the numerical implementation of this work. ...
arXiv:2210.11245v2
fatcat:itbvddpyx5afflburegckqzkcy
Fixed-Final Time Constrained Optimal Control of Nonlinear Systems Using Neural Network HJB Approach
2006
Proceedings of the 45th IEEE Conference on Decision and Control
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In this paper, fixed-final time-constrained optimal control laws using neural networks (NNS) to solve Hamilton-Jacobi-Bellman (HJB) equations for general affine in the constrained nonlinear systems are ...
Index Terms-Constrained input systems, finite-horizon optimal control, Hamilton-Jacobi-Bellman (HJB), neural network (NN) control. ...
Substituting (6) into (5) yields the well-known time-varying HJB equation [33] (7) Equations (6) and (7) provide the solution to fixed-final-time optimal control for affine nonlinear systems. ...
doi:10.1109/cdc.2006.377523
dblp:conf/cdc/ChengL06
fatcat:bg54cfnz7vcztg7wwuojfvhxny
Neural dynamic optimization for control systems. I. Background
2001
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. ...
Index Terms-Dynamic programming (DP), information time shift operator, learning operator, neural dynamic optimization (NDO), neural networks, nonlinear systems, optimal feedback control. ...
This paper presents neural dynamic optimization (NDO) as a practical method for nonlinear MIMO control systems. ...
doi:10.1109/3477.938254
pmid:18244815
fatcat:q7d547jrdnf27crk2i3a372nl4
Page 1203 of Mathematical Reviews Vol. , Issue 92b
[page]
1992
Mathematical Reviews
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We prove structural stability for a large class of unsupervised nonlinear feedback neural networks, adaptive bidirectional associative memory (ABAM) models. ...
Global stability is convergence to fixed points for all inputs and all parameters, Globally stable neural networks need not be struc- turally stable. ...
State-constrained agile missile control with adaptive-critic-based neural networks
2002
IEEE Transactions on Control Systems Technology
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Index Terms-Missile guidance and control, neural networks, optimal control. ...
This class of bounded state space, free final time problems is very difficult to solve due to discontinuities in costates at the constraint boundaries.We use a two-neural-network structure called "adaptive ...
Several authors have used neural networks to "optimally" solve nonlinear systems [[2]- [4] ]. ...
doi:10.1109/tcst.2002.1014669
fatcat:ujvs77k4djcz5lx6fbk62khny4
Finite-horizon input-constrained nonlinear optimal control using single network adaptive critics
2011
Proceedings of the 2011 American Control Conference
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A single neural network based controller called the Finite-SNAC is developed in this study to synthesize finitehorizon optimal controllers for nonlinear control-affine systems. ...
The resulting controller is shown to solve the associated time-varying Hamilton-Jacobi-Bellman (HJB) equation and provide the fixed-final-time optimal solution. ...
in which a fixed final time attitude maneuver is carried out optimally. ...
doi:10.1109/acc.2011.5991378
fatcat:z6bodnkn2famtnm5nha2irgdny
Synthesis of minimum-time feedback laws for dynamic systems using neural networks
1994
Journal of Guidance Control and Dynamics
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Artificial Neural Networks as Nonlinear Mapping Approximators
An artificial neural network (henceforth referred to as neural network) is a system which is composed of many simple and similar nonlinear ...
Finally, the trained network is used to output an approximate optimal control given the measured system states. ...
doi:10.2514/3.21280
fatcat:4p43vq5hu5anncmqtscwtuzs7i
Proper orthogonal decomposition based feedback optimal control synthesis of distributed parameter systems using neural networks
2002
Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301)
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The optimal control problem is then solved in the time domain, in a state feedback sense, following the philosophy of 'adaptive critic' neural networks. ...
A new method for optimal control design of distributed parameter systems is presented in this paper. ...
Towards designing a computational tool for finding a feedback form of the optimal control solution for nonlinear lumped parameter systems, an approximate dynamic programming approach, followed by the adaptive ...
doi:10.1109/acc.2002.1025337
fatcat:doeewdqseba4dgizqdazzqcnre
Neural dynamic optimization for control systems.III. Applications
2001
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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The paper presents neural dynamic optimization (NDO) as a method of optimal feedback control for nonlinear multi-input-multi-output (MIMO) systems. ...
Index Terms-Autonomous vehicles, dynamic programming, information time shift operator, learning operator, neural dynamic optimization, neural networks, nonlinear systems, optimal feedback control, robots ...
The minimum fuel control of a DIP illustrates that NDO enables neural networks to closely approximate the optimal feedback solution of a nonlinear control problem. ...
doi:10.1109/3477.938256
pmid:18244817
fatcat:nrdh3vmog5ccvjeeyawpwjqwqe
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