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020 _a9783031293108
_9978-3-031-29310-8
024 7 _a10.1007/978-3-031-29310-8
_2doi
050 4 _aTJ212-225
050 4 _aTJ210.2-211.495
072 7 _aTJFM
_2bicssc
072 7 _aTEC007000
_2bisacsh
072 7 _aTJFM
_2thema
082 0 4 _a629.8
_223
100 1 _aCohen, Max.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978945
245 1 0 _aAdaptive and Learning-Based Control of Safety-Critical Systems
_h[electronic resource] /
_cby Max Cohen, Calin Belta.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXX, 194 p. 31 illus., 28 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 _aSynthesis Lectures on Computer Science,
_x1932-1686
505 0 _aIntroduction -- Lyapunov-based Control Design -- Safety-Critical Control -- Adaptive Control Lyapunov Functions -- Adaptive Safety-Critical Control -- Modular Adaptive Safety-Critical Control -- Robust Safety-Critical Control for Systems with Actuation Uncertainty -- Safe Exploration in Model-Based Reinforcement Learning -- Temporal Logic Guided Safe Model-Based Reinforcement Learning -- Conclusion.
520 _aThis book stems from the growing use of learning-based techniques, such as reinforcement learning and adaptive control, in the control of autonomous and safety-critical systems. Safety is critical to many applications, such as autonomous driving, air traffic control, and robotics. As these learning-enabled technologies become more prevalent in the control of autonomous systems, it becomes increasingly important to ensure that such systems are safe. To address these challenges, the authors provide a self-contained treatment of learning-based control techniques with rigorous guarantees of stability and safety. This book contains recent results on provably correct control techniques from specifications that go beyond safety and stability, such as temporal logic formulas. The authors bring together control theory, optimization, machine learning, and formal methods and present worked-out examples and extensive simulation examples to complement the mathematical style of presentation. Prerequisites are minimal, and the underlying ideas are accessible to readers with only a brief background in control-theoretic ideas, such as Lyapunov stability theory.
650 0 _aControl engineering.
_931970
650 0 _aRobotics.
_92393
650 0 _aAutomation.
_92392
650 0 _aMachine learning.
_91831
650 0 _aDynamics.
_978946
650 0 _aNonlinear theories.
_93339
650 0 _aArtificial intelligence.
_93407
650 1 4 _aControl, Robotics, Automation.
_931971
650 2 4 _aControl and Systems Theory.
_931972
650 2 4 _aMachine Learning.
_91831
650 2 4 _aApplied Dynamical Systems.
_932005
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aAutomation.
_92392
700 1 _aBelta, Calin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978947
710 2 _aSpringerLink (Online service)
_978948
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031293092
776 0 8 _iPrinted edition:
_z9783031293115
776 0 8 _iPrinted edition:
_z9783031293122
830 0 _aSynthesis Lectures on Computer Science,
_x1932-1686
_978949
856 4 0 _uhttps://doi.org/10.1007/978-3-031-29310-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c84686
_d84686