Download Adaptive Dynamic Programming for Control: Algorithms and by Huaguang Zhang, Derong Liu, Yanhong Luo, Ding Wang PDF

By Huaguang Zhang, Derong Liu, Yanhong Luo, Ding Wang

There are many tools of strong controller layout for nonlinear platforms. In looking to transcend the minimal requirement of balance, Adaptive Dynamic Programming in Discrete Time methods the tough subject of optimum keep an eye on for nonlinear structures utilizing the instruments of adaptive dynamic programming (ADP). the variety of platforms handled is huge; affine, switched, singularly perturbed and time-delay nonlinear structures are mentioned as are the makes use of of neural networks and strategies of price and coverage new release. The textual content beneficial properties 3 major features of ADP during which the equipment proposed for stabilization and for monitoring and video games enjoy the incorporation of optimum keep an eye on equipment:
• infinite-horizon keep an eye on for which the trouble of fixing partial differential Hamilton–Jacobi–Bellman equations without delay is conquer, and evidence only if the iterative worth functionality updating series converges to the infimum of the entire price capabilities got through admissible keep watch over legislation sequences;
• finite-horizon keep an eye on, carried out in discrete-time nonlinear structures displaying the reader how one can receive suboptimal keep an eye on suggestions inside a set variety of keep watch over steps and with effects extra simply utilized in genuine structures than these often won from infinite-horizon keep an eye on;
• nonlinear video games for which a couple of combined optimum regulations are derived for fixing video games either whilst the saddle element doesn't exist, and, while it does, averting the life stipulations of the saddle aspect.
Non-zero-sum video games are studied within the context of a unmarried community scheme within which guidelines are received making certain approach balance and minimizing the person functionality functionality yielding a Nash equilibrium.
In order to make the assurance compatible for the scholar in addition to for the specialist reader, Adaptive Dynamic Programming in Discrete Time:
• establishes the elemental thought concerned sincerely with every one bankruptcy dedicated to a essentially identifiable keep an eye on paradigm;
• demonstrates convergence proofs of the ADP algorithms to deepen realizing of the derivation of balance and convergence with the iterative computational tools used; and
• indicates how ADP tools may be positioned to exploit either in simulation and in genuine purposes.
This textual content could be of substantial curiosity to researchers drawn to optimum regulate and its functions in operations study, utilized arithmetic computational intelligence and engineering. Graduate scholars operating on top of things and operations learn also will locate the tips awarded the following to be a resource of strong tools for furthering their study.

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Extra resources for Adaptive Dynamic Programming for Control: Algorithms and Stability

Sample text

Cheng T, Lewis FL, Abu-Khalaf M (2007) Fixed-final-time-constrained optimal control of nonlinear systems using neural network HJB approach. IEEE Trans Neural Netw 18(6):1725–1736 27. Cheng T, Lewis FL, Abu-Khalaf M (2007) A neural network solution for fixed-final time optimal control of nonlinear systems. Automatica 43(3):482–490 References 21 28. Costa OLV, Tuesta EF (2003) Finite horizon quadratic optimal control and a separation principle for Markovian jump linear systems. IEEE Trans Autom Control 48:1836–1842 29.

Therefore we need to use parametric structures, such as fuzzy models [15] or neural networks, to approximate the costate function and the corresponding control law in the iterative DHP algorithm. In this subsection, we choose radial basis function (RBF) NNs to approximate the nonlinear functions. An RBFNN consists of three-layers (input, hidden and output). Each input value is assigned to a node in the input layer and passed directly to the hidden layer without weights. Nodes at the hidden layer are called RBF units, determined by a vector called center and a scalar called width.

IEEE Trans Syst Man Cybern, Part B, Cybern 38(4):982– 987 36. Goebel R (2002) Convexity in zero-sum differential games. In: Proceedings of the 41th IEEE conference on decision and control, Las Vegas, Nevada, pp 3964–3969 37. Goulart PJ, Kerrigan EC, Alamo T (2009) Control of constrained discrete-time systems with bounded L2 gain. IEEE Trans Autom Control 54(5):1105–1111 38. Gu D (2008) A differential game approach to formation control. IEEE Trans Control Syst Technol 16(1):85–93 39. Hanselmann T, Noakes L, Zaknich A (2007) Continuous-time adaptive critics.

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