04611nam 2200613 a 450 991014359050332120170815114441.01-281-00210-097866110021070-470-05957-50-470-05956-7(CKB)1000000000357069(EBL)315057(OCoLC)175754370(SSID)ssj0000239013(PQKBManifestationID)11186191(PQKBTitleCode)TC0000239013(PQKBWorkID)10234525(PQKB)10347137(MiAaPQ)EBC315057(EXLCZ)99100000000035706920070514d2007 uy 0engur|n|---|||||txtccrRobust control design[electronic resource] an optimal control approach /Feng LinChichester, West Sussex, England ;Hoboken, NJ John Wiley/RSPc20071 online resource (380 p.)RSP series in control theory and applicationsDescription based upon print version of record.0-470-03191-3 Includes bibliographical references (p. [351]-361) index.Robust Control Design; Contents; Preface; Notation; 1 Introduction; 1.1 Systems and Control; 1.2 Modern Control Theory; 1.3 Stability; 1.4 Optimal Control; 1.5 Optimal Control Approach; 1.6 Kharitonov Approach; 1.7 H and H2 Control; 1.8 Applications; 1.9 Use of this Book; 2 Fundamentals of Control Theory; 2.1 State Space Model; 2.2 Responses of Linear Systems; 2.3 Similarity Transformation; 2.4 Controllability and Observability; 2.5 Pole Placement by State Feedback; 2.6 Pole Placement Using Observer; 2.7 Notes and References; 2.8 Problems; 3 Stability Theory3.1 Stability and Lyapunov Theorem3.2 Linear Systems; 3.3 Routh-Hurwitz Criterion; 3.4 Nyquist Criterion; 3.5 Stabilizability and Detectability; 3.6 Notes and References; 3.7 Problems; 4 Optimal Control and Optimal Observers; 4.1 Optimal Control Problem; 4.2 Principle of Optimality; 4.3 Hamilton-Jacobi-Bellman Equation; 4.4 Linear Quadratic Regulator Problem; 4.5 Kalman Filter; 4.6 Notes and References; 4.7 Problems; 5 Robust Control of Linear Systems; 5.1 Introduction; 5.2 Matched Uncertainty; 5.3 Unmatched Uncertainty; 5.4 Uncertainty in the Input Matrix; 5.5 Notes and References5.6 Problems6 Robust Control of Nonlinear Systems; 6.1 Introduction; 6.2 Matched Uncertainty; 6.3 Unmatched Uncertainty; 6.4 Uncertainty in the Input Matrix; 6.5 Notes and References; 6.6 Problems; 7 Kharitonov Approach; 7.1 Introduction; 7.2 Preliminary Theorems; 7.3 Kharitonov Theorem; 7.4 Control Design Using Kharitonov Theorem; 7.5 Notes and References; 7.6 Problems; 8 H and H2 Control; 8.1 Introduction; 8.2 Function Space; 8.3 Computation of H2 and H Norms; 8.4 Robust Control Problem as H2 and H Control Problem; 8.5 H2/H<&infinity> Control Synthesis8.6 Notes and References; 8.7 Problems; 9 Robust Active Damping; 9.1 Introduction; 9.2 Problem Formulation; 9.3 Robust Active Damping Design; 9.4 Active Vehicle Suspension System; 9.5 Discussion; 9.6 Notes and References; 10 Robust Control of Manipulators; 10.1 Robot Dynamics; 10.2 Problem Formulation; 10.3 Robust Control Design; 10.4 Simulations; 10.5 Notes and References; 11 Aircraft Hovering Control; 11.1 Modelling and Problem Formulation; 11.2 Control Design for Jet-borne Hovering; 11.3 Simulation; 11.4 Notes and ReferencesAppendix A: Mathematical Modelling of Physical SystemsReferences and Bibliography; IndexComprehensive and accessible guide to the three main approaches to robust control design and its applications Optimal control is a mathematical field that is concerned with control policies that can be deduced using optimization algorithms. The optimal control approach to robust control design differs from conventional direct approaches to robust control that are more commonly discussed by firstly translating the robust control problem into its optimal control counterpart, and then solving the optimal control problem. Robust Control Design: An Optimal Control Approach offers RSPAutomatic controlElectronic books.Automatic control.629.8629.8312Lin Feng882820MiAaPQMiAaPQMiAaPQBOOK9910143590503321Robust control design1972207UNINA11154nam 2200517 450 99647207010331620231110233354.03-031-02462-1(MiAaPQ)EBC6953668(Au-PeEL)EBL6953668(CKB)21513297600041(PPN)262167522(EXLCZ)992151329760004120221118d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierApplications of evolutionary computation 25th European conference, EvoApplications 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, proceedings /edited by Juan Luis Jiménez Laredo, J. Ignacio Hidalgo, and Kehinde Oluwatoyin BabaagbaCham, Switzerland :Springer,[2022]©20221 online resource (759 pages)Lecture Notes in Computer Science ;v.13224Print version: Jiménez Laredo, Juan Luis Applications of Evolutionary Computation Cham : Springer International Publishing AG,c2022 9783031024610 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents -- Applications of Evolutionary Computation -- An Enhanced Opposition-Based Evolutionary Feature Selection Approach -- 1 Introduction -- 2 Moth Flame Optimization -- 2.1 Binary Moth Flame Optimization -- 2.2 Binary Moth Flame Optimization for Feature Selection -- 3 The Proposed Approach -- 3.1 Initialization Using Opposition-Based Method -- 3.2 Retiring Flame -- 4 Experimental Setup and Results -- 5 Conclusions -- References -- A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings -- 1 Introduction -- 2 Conductance-Based Model Description -- 3 Defining a Benchmark with Known Types of Ion Channels -- 4 Methodology and Experimental Setup -- 5 Experimental Results -- 6 Conclusions -- A Mathematical Description of the Models -- B Experimental Setup and Parameter Ranges -- References -- Swarm Optimised Few-View Binary Tomography -- 1 Introduction -- 2 Binary Tomographic Reconstruction -- 3 Swarm Optimisation -- 4 Constrained Search in High Dimensions -- 5 Reconstructions -- 6 Results -- 7 Discussion -- 8 Conclusions -- References -- Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization -- 1 Introduction -- 2 The Metaheuristics Studied -- 2.1 Basin Hopping -- 2.2 Differential Evolution -- 2.3 Particle Swarm Optimization -- 3 The Benchmarking Environment -- 4 Experimental Setup -- 5 Experimental Results -- 6 Conclusions -- References -- Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models -- 1 Introduction -- 2 Methodology -- 2.1 Structured Grammatical Evolution -- 2.2 Random Structured Grammatical Evolution for Symbolic Regression Problems -- 3 Experimental Setup -- 3.1 Study Problems -- 3.2 Configuration of the Algorithms -- 4 Results -- 5 Conclusions -- References.EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Framework Overview -- 4.1 Parameters -- 4.2 Datasets -- 4.3 Clustering with EvoCluster -- 4.4 Classification -- 4.5 Evaluation Measures -- 4.6 Results Management -- 5 Experiments and Visualizations -- 6 Conclusion and Future Works -- References -- Evolution of Acoustic Logic Gates in Granular Metamaterials -- 1 Introduction -- 2 Problem Statement -- 3 Simulation Setup -- 3.1 2D Granular Simulator -- 3.2 Optimization Method -- 4 Results and Discussion -- 4.1 Evolution of an Acoustic Band Gap -- 4.2 Evolving an AND Gate -- 4.3 Evolving an XOR Gate -- 5 Conclusion and Future Work -- References -- Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry -- 1 Introduction -- 2 The Problem -- 3 Proposed Approach -- 3.1 The Model -- 3.2 Data -- 3.3 Adversarial Optimization -- 3.4 Operator (EA1) -- 3.5 Public Administration (EA2) -- 4 Experimental Evaluation -- 4.1 Stochastic Optimization -- 4.2 Analysis -- 4.3 Real World Case -- 5 Conclusions and Future Work -- References -- The Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation Optimization -- 1 Introduction -- 2 Background -- 2.1 Two-Body Problem -- 2.2 Maneuvers in Space -- 2.3 Lambert Problem -- 3 Asteroid Routing Problem -- 4 Optimization Algorithms -- 4.1 Sequential Least Squares Programming (SLSQP) -- 4.2 Greedy Nearest Neighbor Heuristic -- 4.3 Unbalanced Mallows Model (UMM) -- 4.4 Combinatorial Efficient Global Optimization (CEGO) -- 5 Experimental Study -- 5.1 Experimental Methodology -- 5.2 Results of the Black-Box Setting -- 5.3 Results of the Informed Setting -- 6 Conclusions -- References -- On the Difficulty of Evolving Permutation Codes -- 1 Introduction -- 2 Preliminaries.3 Incremental Construction with EA -- 3.1 Evolving Subsets of Permutations -- 3.2 Iterative Approach -- 3.3 Fitness Functions -- 4 Experimental Evaluation -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion -- 1 Introduction -- 2 Background -- 2.1 Differential Evolution -- 2.2 Moving Average -- 2.3 Population Diversity -- 2.4 Opposition-Based Learning -- 3 Proposed Approach -- 4 Experiments and Results -- 4.1 Experiments over 30 Dimensions -- 4.2 Experiments over 50 Dimensions -- 5 Conclusions and Future Work -- References -- Search-Based Third-Party Library Migration at the Method-Level -- 1 Introduction -- 2 Background and Motivation -- 2.1 Background -- 2.2 Motivating Example -- 3 Search-Based API Migration -- 3.1 Solution Representation -- 3.2 Calculating the Fitness Function -- 3.3 Genetic Algorithm Operators and Parameters -- 4 Experimental Evaluation -- 4.1 Dataset Used -- 4.2 Metrics Used -- 4.3 Results -- 4.4 Discussion and Limitations -- 5 Related Work -- 6 Conclusion -- References -- Multi-objective Optimization of Extreme Learning Machine for Remaining Useful Life Prediction -- 1 Introduction -- 2 Background -- 3 Methods -- 3.1 Individual Encoding -- 3.2 Optimization Algorithms -- 4 Experimental Setup -- 4.1 Benchmark Dataset -- 4.2 Back-Propagation Neural Networks (BPNNs) -- 4.3 Computational Setup and Data Preparation -- 5 Experimental Results -- 6 Conclusions -- References -- Explainable Landscape Analysis in Automated Algorithm Performance Prediction -- 1 Introduction -- 2 Related Work -- 3 Automated Algorithm Performance Prediction -- 4 Experimental Setup -- 4.1 Data -- 4.2 Regression Models and Their Hyper-parameters -- 4.3 Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References.Search Trajectories Networks of Multiobjective Evolutionary Algorithms -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Search Trajectory Networks -- 3.2 Multiobjective Optimisation Problems -- 4 STN Extension for the Multiobjective Domain -- 5 Experiments -- 5.1 Experimental Parameters -- 5.2 Metrics -- 5.3 Reproducibility -- 6 Results -- 7 Conclusion -- References -- EvoMCS: Optimising Energy and Throughput of Mission Critical Services -- 1 Introduction -- 2 Related Work -- 3 EvoMCS: Multi-objective Optimization -- 3.1 Scenario and Technologies -- 3.2 Evolutionary Algorithm -- 3.3 Heuristic for Fitness -- 3.4 Selection Strategy -- 3.5 Operators to Generate Descendants -- 4 Experimentation -- 4.1 Validation Scenarios -- 4.2 Configuration Parameters -- 4.3 Evaluation Metrics -- 4.4 Profiles Validation - Inputs from EvoMCS -- 5 Results -- 5.1 Operators for the EvoMCS in H1(E/T) -- 5.2 Optimal Configurations -- 5.3 Optimal Profiles in Scenarios with Dense-Environments -- 6 Conclusions -- References -- RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation -- 1 Introduction -- 2 Background -- 2.1 Differential Evolution -- 2.2 L-SHADE -- 3 RWS-L-SHADE -- 4 Experimental Results -- 5 Conclusions -- References -- WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution -- 1 Introduction -- 2 Grammatical Evolution and Differential Evolution -- 3 Software Description -- 3.1 Modular Design -- 3.2 Parallel Execution -- 3.3 Persistence Layer -- 3.4 Implementation Technologies -- 4 WebGE Most Relevant Features -- 4.1 GUI for Experiments Management -- 4.2 Cross-fold Validation -- 4.3 Detailed Statistics -- 5 Use Case: Vladislavleva-4 -- 6 Conclusions -- References -- A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment.1 Introduction -- 1.1 Goals -- 1.2 Organisation -- 2 Background and Related Work -- 2.1 Vibrational Spectroscopy -- 2.2 Partial Least Squares Regression -- 2.3 Spectral Pre-processing -- 2.4 PLSR for Nutrient Assessment -- 3 The Proposed Approach -- 3.1 Representations for the Two Populations for Co-evolution -- 3.2 Mapping of the Two Populations for Pairwise Evaluations -- 3.3 The Evaluation Method -- 4 Experiment Design -- 4.1 Datasets -- 4.2 Parameter Settings -- 5 Results and Discussions -- 5.1 Comparisons on the Training and Test Performance -- 5.2 Analyses on the Pre-processing Selection -- 5.3 Analyses on Feature Selection Results -- 6 Conclusions and Future Work -- References -- Evolutionary Computation in Edge, Fog, and Cloud Computing -- Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling -- 1 Introduction -- 2 Related Work -- 3 Job Scheduling Structures -- 4 Structure Optimisation -- 4.1 Brute Force Search Algorithm -- 4.2 Genetic Algorithm -- 4.3 Simulated Annealing Algorithm -- 5 Simulation Experiments and Results -- 5.1 Setup -- 5.2 Experiment 1: Search Algorithm Comparison -- 5.3 Experiment 2: Server Processing Power Dispersion Impact -- 5.4 Experiment 3: Task Size Dispersion Impact -- 5.5 Experiment 4: Job Complexity Impact -- 6 Conclusion -- References -- Optimising Communication Overhead in Federated Learning Using NSGA-II -- 1 Introduction -- 2 Fundamental Concepts -- 2.1 Federated Learning -- 2.2 Communication Overhead in Distributed Deep Learning -- 3 Proposed Approach -- 3.1 The Proposed FL-COP Modelling and Formulation -- 3.2 The Communication-Overhead Reduction Routine -- 4 Experimental Study and Analysis -- 4.1 Problem Benchmarks and Experimental Settings -- 4.2 Experimental Results and Discussion -- 5 Conclusions and Perspectives -- References -- Evolutionary Machine Learning.Evolving Data Augmentation Strategies.Lecture Notes in Computer Science Evolutionary computationEvolutionary computation.006.3823Babaagba Kehinde OluwatoyinHidalgo Pérez José IgnacioLaredo Juan Luis JiménezMiAaPQMiAaPQMiAaPQBOOK996472070103316Applications of Evolutionary Computation2834163UNISA01321nam 2200433 450 991079299120332120230124194140.01-78714-282-5(CKB)3710000001306337(MiAaPQ)EBC4746437(EXLCZ)99371000000130633720170525h20172017 uy 0engurcnu||||||||rdacontentrdamediardacarrierApplications of management science /edited by Kenneth D. Lawrence, Gary KleinmanBingley, [England] :Emerald Publishing Limited,2017.©20171 online resource (204 pages) illustrationsApplications of Management Science ;Volume 161-78714-283-3 Includes bibliographical references and index.Applications of management science ;Volume 16.Management scienceDecision makingManagement science.Decision making.406Lawrence Kenneth D.Kleinman GaryMiAaPQMiAaPQMiAaPQBOOK9910792991203321Applications of management science3791078UNINA