Constrained controller design for a class of nonlinear discrete-time uncertain systems
10 Jun 2009-pp 549-554
TL;DR: In this paper, constrained controller design is proposed for a class of nonlinear discrete-time uncertain system having matched type system uncertainties, using the solution of HJB (Hamilton-Jaccobi-Bellman) equation.
Abstract: In this paper, constrained controller design is proposed for a class of nonlinear discrete-time uncertain system having matched type system uncertainties, using the solution of HJB (Hamilton-Jaccobi-Bellman) equation. The discrete-time HJB equation is formulated using a suitable non-quadratic term in the performance functional to tackle constraints on the control input. Based on the non-quadratic functional, a greedy HDP algorithm is used to obtain the constrained robust-optimal controller. The constrained robust controller requires knowledge of the upper bound of system uncertainties. For facilitating the implementation of the iterative algorithm, two neural networks are used to approximate the value function and to compute the optimal control policy, respectively. Their weights have been tuned using least squares method. Proposed algorithm has been applied on a nonlinear discrete-time system with matched uncertainties.
Citations
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Journal Article•
TL;DR: In this article, new fundamental theorems are stated, which allow to easily determine a first-order majorant system of a pseudo-quadratic uncertain MIMO discrete-time system.
Abstract: In this paper new fundamental theorems are stated, which allow to easily determine a first-order majorant system of a pseudo-quadratic uncertain MIMO discrete-time system. For the above class of systems, some results and systematic methods are provided, which allow to solve, “via majorant system”, several analysis problems of robust stability, stabilization and tracking of a generic reference signal with bounded variation in presence of a generic disturbance with bounded variation. The utility and the efficiency of the main results proposed in this paper are illustrated with three significant examples.
4 citations
07 Jul 2010
TL;DR: In this paper, the problem of optimal control synthesis for a class of parabolic distributed parameter systems with constraints in control is considered and the problem is solved by using Pontryagin's maximum principle and the control is generated by a neural network.
Abstract: In the present paper the problem of optimal control synthesis for a class of parabolic distributed parameter systems with constraints in control is considered. The problem is solved by using Pontryagin's maximum principle and the control is generated by a neural network.
2 citations
27 Jul 2016
TL;DR: In this paper, the robust control problem is transformed into the optimal control problem by properly choosing a cost function that reflects the uncertainties, regulation, and control, and a computable sufficient condition is also derived to ensure that the solution to the corresponding optimal controller problem is a solution to robust control.
Abstract: In this paper, a strategy is established to design the robust controller for a class of continuous-time nonlinear systems with Lurie uncertainties.The uncertainty is decomposed into matched and unmatched components, and an augmented control is introduced for the unmatched uncertainty. An optimal control algorithm based on Hamilton-Jacobi-Bellman(HJB) equation is proposed for the design of a bounded robust controller and finite-time-horizon nonlinear systems. In this case, the robust control problem is transformed into the optimal control problem by properly choosing a cost function that reflects the uncertainties, regulation, and control. The bounded controller no longer requires the knowledge of the upper bound of systems uncertainty. A computable sufficient condition is also derived to ensure that the solution to the corresponding optimal control problem is a solution to the robust control problem. A simulation example is provided to verify the effectiveness under the present robust control scheme.
1 citations
References
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01 Jan 2009
TL;DR: In this article, the authors present the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Abstract: From the Publisher:
This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
4,251 citations
Book•
01 Jan 1996
TL;DR: This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control.
Abstract: From the Publisher:
This is the first textbook that fully explains the neuro-dynamic programming/reinforcement learning methodology, which is a recent breakthrough in the practical application of neural networks and dynamic programming to complex problems of planning, optimal decision making, and intelligent control
3,665 citations
"Constrained controller design for a..." refers background in this paper
...I. INTRODUCTION In recent years, approximate dynamic programming (ADP) algorithm has been paid much attention by researchers [1–5] in order to obtain approximate optimal control law....
[...]
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"Constrained controller design for a..." refers background in this paper
...I. INTRODUCTION In recent years, approximate dynamic programming (ADP) algorithm has been paid much attention by researchers [1–5] in order to obtain approximate optimal control law....
[...]
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