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024 7 _a10.1007/978-981-10-4080-1
_2doi
050 4 _aTJ212-225
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100 1 _aWei, Qinglai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_933786
245 1 0 _aSelf-Learning Optimal Control of Nonlinear Systems
_h[electronic resource] :
_bAdaptive Dynamic Programming Approach /
_cby Qinglai Wei, Ruizhuo Song, Benkai Li, Xiaofeng Lin.
250 _a1st ed. 2018.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2018.
300 _aXVIII, 230 p. 86 illus., 73 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v103
505 0 _aChapter 1. Principle of Adaptive Dynamic Programming -- Chapter 2. An Iterative ϵ-Optimal Control Scheme for a Class of Discrete-Time Nonlinear Systems With Unfixed Initial State -- Chapter 3. Discrete-Time Optimal Control of Nonlinear Systems Via Value Iteration-Based Q-Learning -- Chapter 4. A Novel Policy Iteration Based Deterministic Q-Learning for Discrete-Time Nonlinear Systems -- Chapter 5. Nonlinear Neuro-Optimal Tracking Control Via Stable Iterative Q-Learning Algorithm -- Chapter 6. Model-Free Multiobjective Adaptive Dynamic Programming for Discrete-Time Nonlinear Systems with General Performance Index Functions -- Chapter 7. Multi-Objective Optimal Control for a Class of Unknown Nonlinear Systems Based on Finite-Approximation-Error ADP Algorithm -- Chapter 8. A New Approach for a Class of Continuous-Time Chaotic Systems Optimal Control by Online ADP Algorithm -- Chapter 9. Off-Policy IRL Optimal Tracking Control for Continuous-Time Chaotic Systems -- Chapter 10. ADP-Based Optimal Sensor Scheduling for Target Tracking in Energy Harvesting Wireless Sensor Networks.
520 _aThis book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.
650 0 _aControl engineering.
_931970
650 0 _aComputational intelligence.
_97716
650 0 _aMultibody systems.
_96018
650 0 _aVibration.
_96645
650 0 _aMechanics, Applied.
_93253
650 1 4 _aControl and Systems Theory.
_931972
650 2 4 _aComputational Intelligence.
_97716
650 2 4 _aMultibody Systems and Mechanical Vibrations.
_932157
700 1 _aSong, Ruizhuo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_933787
700 1 _aLi, Benkai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_933788
700 1 _aLin, Xiaofeng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_933789
710 2 _aSpringerLink (Online service)
_933790
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811040795
776 0 8 _iPrinted edition:
_z9789811040818
776 0 8 _iPrinted edition:
_z9789811350436
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v103
_933791
856 4 0 _uhttps://doi.org/10.1007/978-981-10-4080-1
912 _aZDB-2-ENG
912 _aZDB-2-SXE
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