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

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

There are many tools of good controller layout for nonlinear platforms. In looking to transcend the minimal requirement of balance, Adaptive Dynamic Programming in Discrete Time ways the tough subject of optimum keep watch over for nonlinear structures utilizing the instruments of adaptive dynamic programming (ADP). the variety of structures handled is vast; affine, switched, singularly perturbed and time-delay nonlinear structures are mentioned as are the makes use of of neural networks and methods of price and coverage generation. The textual content positive aspects 3 major facets of ADP within which the equipment proposed for stabilization and for monitoring and video games enjoy the incorporation of optimum keep watch over tools:
• infinite-horizon keep watch over 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 all of the price services bought by means of admissible regulate legislations sequences;
• finite-horizon keep an eye on, applied in discrete-time nonlinear platforms displaying the reader find out how to receive suboptimal regulate recommendations inside of a hard and fast variety of keep an eye on steps and with effects extra simply utilized in actual structures than these frequently won from infinite-horizon regulate;
• 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 lifestyles stipulations of the saddle aspect.
Non-zero-sum video games are studied within the context of a unmarried community scheme within which guidelines are got ensuring approach balance and minimizing the person functionality functionality yielding a Nash equilibrium.
In order to make the insurance appropriate for the scholar in addition to for the professional reader, Adaptive Dynamic Programming in Discrete Time:
• establishes the basic concept concerned in actual fact with every one bankruptcy dedicated to a in actual fact identifiable keep watch over paradigm;
• demonstrates convergence proofs of the ADP algorithms to deepen knowing of the derivation of balance and convergence with the iterative computational equipment used; and
• indicates how ADP tools might be positioned to exploit either in simulation and in actual purposes.
This textual content should be of substantial curiosity to researchers attracted to optimum keep an eye on and its functions in operations learn, utilized arithmetic computational intelligence and engineering. Graduate scholars operating up to speed and operations learn also will locate the information offered the following to be a resource of robust tools for furthering their study.

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

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10), there exists an integral v (x(k)) −T ¯ −1 ϕ (U s)U¯ Rds to compute, which is a large computing burden. term 2 0 i Therefore, in the following we will present another method called iterative DHP algorithm to implement the iterative ADP algorithm. Define the costate function λ(x) = ∂V (x)/∂x. Here, we assume that the value function V (x) is smooth so that λ(x) exists. 10) can be implemented as follows. First, we start with an initial costate function λ0 (·) = 0. Then, for i = 0, 1, . . 45), we obtain the corresponding control law vi (x) as 1 vi (x(k)) = U¯ ϕ − (U¯ R)−1 g T (x(k))λi (x(k + 1)) .

The parameters ε0 and imax are chosen by the designer. The smaller the value of ε0 is set, the more accurate the costate function and the optimal control law will be. If the condition set in Step 7 is satisfied, it implies that the costate function sequence is convergent with the pre-specified 44 2 Optimal State Feedback Control for Discrete-Time Systems accuracy. The larger the value of imax in Step 8 is set, the more accurate the obtained control law v(x) ˆ will be at the price of increased computational burden.

Int J Intell Control Syst 10(1):21–32 62. Liu D, Xiong X, Zhang Y (2001) Action-dependent adaptive critic designs. In: Proceeding of international joint conference on neural networks, Washington, DC, pp 990–995 63. Liu D, Zhang Y, Zhang HG (2005) A self-learning call admission control scheme for CDMA cellular networks. IEEE Trans Neural Netw 16(5):1219–1228 64. Liu D, Javaherian H, Kovalenko O, Huang T (2008) Adaptive critic learning techniques for engine torque and air–fuel ratio control. IEEE Trans Syst Man Cybern, Part B, Cybern 38(4):988–993 65.

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