{"product_id":"iterative-learning-control-algorithms-and-experimental-benchmarking-isbn-9780470745045","title":"Iterative Learning Control Algorithms and Experimental Benchmarking","description":"Iterative Learning \u003cb\u003eCONTROL ALGORITHMS AND EXPERIMENTAL BENCHMARKING\u003c\/b\u003e \u003cp\u003e\u003ci\u003e\u003cb\u003eIterative Learning Control Algorithms and Experimental Benchmarking\u003c\/b\u003e\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003ePresents key cutting edge research into the use of iterative learning control\u003c\/b\u003e\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003eThe book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. The book provides integrated coverage of the major approaches to-date in terms of basic systems, theoretic properties, design algorithms, and experimentally measured performance, as well as the links with repetitive control and other related areas.  \u003c\/p\u003e\u003cp\u003eKey features: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Provides comprehensive coverage of the main approaches to ILC and their relative advantages and disadvantages.\u003c\/li\u003e \u003cli\u003e Presents the leading research in the field along with experimental benchmarking results. \u003c\/li\u003e \u003cli\u003e Demonstrates how this approach can extend out from engineering to other areas and, in particular, new research into its use in healthcare systems\/rehabilitation robotics.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThe book is essential reading for researchers and graduate students in iterative learning control, repetitive control and, more generally, control systems theory and its applications. \u003c\/p\u003e\u003cp\u003ePreface vii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Iterative Learning Control: Origins and General Overview \u003c\/b\u003e\u003ci\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Origins of ILC \u003ci\u003e2\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.2 A Synopsis of the Literature \u003ci\u003e5\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.3 Linear Models and Control Structures \u003ci\u003e6\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.3.1 Differential Linear Dynamics \u003ci\u003e7\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.4 ILC for Time-Varying Linear Systems \u003ci\u003e9\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.5 Discrete Linear Dynamics \u003ci\u003e11\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.6 ILC in a 2D Linear Systems\/Repetitive Processes Setting \u003ci\u003e16\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.6.1 2D Discrete Linear Systems and ILC \u003ci\u003e16\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.6.2 ILC in a Repetitive Process Setting \u003ci\u003e17\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.7 ILC for Nonlinear Dynamics \u003ci\u003e18\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.8 Robust, Stochastic, and Adaptive ILC \u003ci\u003e19\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.9 Other ILC Problem Formulations \u003ci\u003e21\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.10 Concluding Remarks \u003ci\u003e22\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Iterative Learning Control: Experimental Benchmarking \u003c\/b\u003e\u003ci\u003e\u003cb\u003e23\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Robotic Systems \u003ci\u003e23\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1.1 Gantry Robot \u003ci\u003e23\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1.2 Anthromorphic Robot Arm \u003ci\u003e25\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.2 Electro-Mechanical Systems \u003ci\u003e26\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.2.1 Nonminimum Phase System \u003ci\u003e26\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.2.2 Multivariable Testbed \u003ci\u003e29\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.2.3 Rack Feeder System \u003ci\u003e30\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.3 Free Electron Laser Facility \u003ci\u003e32\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.4 ILC in Healthcare \u003ci\u003e37\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.5 Concluding Remarks \u003ci\u003e38\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 An Overview of Analysis and Design for Performance \u003c\/b\u003e\u003ci\u003e\u003cb\u003e39\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 ILC Stability and Convergence for Discrete Linear Dynamics \u003ci\u003e39\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1.1 Transient Learning \u003ci\u003e41\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1.2 Robustness \u003ci\u003e42\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.2 Repetitive Process\/2D Linear Systems Analysis \u003ci\u003e43\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.2.1 Discrete Dynamics \u003ci\u003e43\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.2.2 Repetitive Process Stability Theory \u003ci\u003e46\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.2.3 Error Convergence Versus Along the Trial Performance \u003ci\u003e51\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.3 Concluding Remarks \u003ci\u003e55\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Tuning and Frequency Domain Design of Simple Structure ILC Laws \u003c\/b\u003e\u003ci\u003e\u003cb\u003e57\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Tuning Guidelines \u003ci\u003e57\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.2 Phase-Lead and Adjoint ILC Laws for Robotic-Assisted Stroke Rehabilitation \u003ci\u003e58\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.2.1 Phase-Lead ILC \u003ci\u003e61\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.2.2 Adjoint ILC \u003ci\u003e63\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.2.3 Experimental Results \u003ci\u003e63\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.3 ILC for Nonminimum Phase Systems Using a Reference Shift Algorithm \u003ci\u003e68\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.3.1 Filtering \u003ci\u003e74\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.3.2 Numerical Simulations \u003ci\u003e75\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.3.3 Experimental Results \u003ci\u003e75\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.4 Concluding Remarks \u003ci\u003e81\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Optimal ILC \u003c\/b\u003e\u003ci\u003e\u003cb\u003e83\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 NOILC \u003ci\u003e83\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1.1 Theory \u003ci\u003e83\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1.2 NOILC Computation \u003ci\u003e86\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.2 Experimental NOILC Performance \u003ci\u003e89\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.2.1 Test Parameters \u003ci\u003e90\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.3 NOILC Applied to Free Electron Lasers \u003ci\u003e93\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.4 Parameter Optimal ILC \u003ci\u003e96\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.4.1 An Extension to Adaptive ILC \u003ci\u003e98\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.5 Predictive NOILC \u003ci\u003e99\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.5.1 Controlled System Analysis \u003ci\u003e104\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.5.2 Experimental Validation \u003ci\u003e106\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.6 Concluding Remarks \u003ci\u003e116\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Robust ILC \u003c\/b\u003e\u003ci\u003e\u003cb\u003e117\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Robust Inverse Model-Based ILC \u003ci\u003e117\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.2 Robust Gradient-Based ILC \u003ci\u003e123\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.2.1 Model Uncertainty –Case (i) \u003ci\u003e127\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.2.2 Model Uncertainty –Cases (ii) and (iii) \u003ci\u003e128\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.3 \u003ci\u003eH\u003c\/i\u003e\u003csub\u003e∞\u003c\/sub\u003e Robust ILC \u003ci\u003e132\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.3.1 Background and Early Results \u003ci\u003e132\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.3.2 \u003ci\u003eH\u003c\/i\u003e\u003csub\u003e∞\u003c\/sub\u003e Based Robust ILC Synthesis \u003ci\u003e137\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.3.3 A Design Example \u003ci\u003e142\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.3.4 Robust ILC Analysis Revisited \u003ci\u003e151\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.4 Concluding Remarks \u003ci\u003e153\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Repetitive Process-Based ILC Design \u003c\/b\u003e\u003ci\u003e\u003cb\u003e155\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Design with Experimental Validation \u003ci\u003e155\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1.1 Discrete Nominal Model Design \u003ci\u003e155\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1.2 Robust Design –Norm-Bounded Uncertainty \u003ci\u003e160\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1.3 Robust Design – Polytopic Uncertainty and Simplified Implementation \u003ci\u003e165\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1.4 Design for Differential Dynamics \u003ci\u003e170\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.2 Repetitive Process-Based ILC Design Using Relaxed Stability Theory \u003ci\u003e170\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.3 Finite Frequency Range Design and Experimental Validation \u003ci\u003e178\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.3.1 Stability Analysis \u003ci\u003e178\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.4 HOILC Design \u003ci\u003e194\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.5 Inferential ILC Design \u003ci\u003e196\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.6 Concluding Remarks \u003ci\u003e202\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Constrained ILC Design \u003c\/b\u003e\u003ci\u003e\u003cb\u003e203\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 ILC with Saturating Inputs Design \u003ci\u003e203\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1.1 Observer-Based State Control Law Design \u003ci\u003e203\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1.2 ILC Design with Full State Feedback \u003ci\u003e209\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1.3 Comparison with an Alternative Design \u003ci\u003e210\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1.4 Experimental Results \u003ci\u003e215\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.2 Constrained ILC Design for LTV Systems \u003ci\u003e219\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.2.1 Problem Specification \u003ci\u003e219\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.2.2 Implementation of Constrained Algorithm 1 – a Receding Horizon Approach \u003ci\u003e223\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.2.3 Constrained ILC Algorithm 3 \u003ci\u003e224\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.3 Experimental Validation on a High-Speed Rack Feeder System \u003ci\u003e226\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.3.1 Simulation Case Studies \u003ci\u003e226\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.3.2 Other Performance Issues \u003ci\u003e230\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.3.3 Experimental Results \u003ci\u003e236\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.3.4 Algorithm 1: QP-Based Constrained ILC \u003ci\u003e236\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.3.5 Algorithm 2: Receding Horizon Approach-Based Constrained ILC \u003ci\u003e237\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.4 Concluding Remarks \u003ci\u003e238\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 ILC for Distributed Parameter Systems \u003c\/b\u003e\u003ci\u003e\u003cb\u003e241\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Gust Load Management for Wind Turbines \u003ci\u003e241\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1.1 Oscillatory Flow \u003ci\u003e246\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1.2 Flow with Vortical Disturbances \u003ci\u003e251\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1.3 Blade Conditioning Measures \u003ci\u003e253\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1.4 Actuator Dynamics and Trial-Varying ILC \u003ci\u003e254\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1.5 Proper Orthogonal Decomposition-Based Reduced Order Model Design \u003ci\u003e257\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.2 Design Based on Finite-Dimensional Approximate Models with Experimental Validation \u003ci\u003e266\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.3 Finite Element and Sequential Experimental Design-based ILC \u003ci\u003e280\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.3.1 Finite Element Discretization \u003ci\u003e281\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.3.2 Application of ILC \u003ci\u003e283\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.3.3 Optimal Measurement Data Selection \u003ci\u003e284\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.4 Concluding Remarks \u003ci\u003e288\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Nonlinear ILC \u003c\/b\u003e\u003ci\u003e\u003cb\u003e289\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Feedback Linearized ILC for Center-Articulated Industrial Vehicles \u003ci\u003e289\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.2 Input–Output Linearization-based ILC Applied to Stroke Rehabilitation \u003ci\u003e293\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.2.1 System Configuration and Modeling \u003ci\u003e293\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.2.2 Input–Output Linearization \u003ci\u003e296\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.2.3 Experimental Results \u003ci\u003e299\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.3 Gap Metric ILC with Application to Stroke Rehabilitation \u003ci\u003e302\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.4 Nonlinear ILC – an Adaptive Lyapunov Approach \u003ci\u003e310\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.4.1 Motivation and Background Results \u003ci\u003e311\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.5 Extremum-Seeking ILC \u003ci\u003e320\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.6 Concluding Remarks \u003ci\u003e322\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Newton Method Based ILC \u003c\/b\u003e\u003ci\u003e\u003cb\u003e323\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Background \u003ci\u003e323\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.2 Algorithm Development \u003ci\u003e324\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.2.1 Computation of Newton-Based ILC \u003ci\u003e326\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.2.2 Convergence Analysis \u003ci\u003e327\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.3 Monotonic Trial-to-Trial Error Convergence \u003ci\u003e328\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.3.1 Monotonic Convergence with Parameter Optimization \u003ci\u003e329\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.3.2 Parameter Optimization for Monotonic and Fast Trial-to-Trial Error Convergence \u003ci\u003e330\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.4 Newton ILC for 3D Stroke Rehabilitation \u003ci\u003e331\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.4.1 Experimental Results \u003ci\u003e336\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.5 Constrained Newton ILC Design \u003ci\u003e337\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.6 Concluding Remarks \u003ci\u003e347\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Stochastic ILC \u003c\/b\u003e\u003ci\u003e\u003cb\u003e349\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Background and Early Results \u003ci\u003e349\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.2 Frequency Domain-Based Stochastic ILC Design \u003ci\u003e356\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.3 Experimental Comparison of ILC Laws \u003ci\u003e364\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.4 Repetitive Process-Based Analysis and Design \u003ci\u003e378\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.5 Concluding Remarks \u003ci\u003e387\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Some Emerging Topics in Iterative Learning Control \u003c\/b\u003e\u003ci\u003e\u003cb\u003e389\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 ILC for Spatial Path Tracking \u003ci\u003e389\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.2 ILC in Agriculture and Food Production \u003ci\u003e394\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.2.1 The Broiler Production Process \u003ci\u003e395\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.2.2 ILC for FCR Minimization \u003ci\u003e400\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.2.3 Design Validation \u003ci\u003e404\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.3 ILC for Quantum Control \u003ci\u003e406\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.4 ILC in the Utility Industries \u003ci\u003e410\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.4.1 ILC Design \u003ci\u003e413\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.5 Concluding Remarks \u003ci\u003e415\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A \u003c\/b\u003e\u003ci\u003e\u003cb\u003e417\u003c\/b\u003e\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA.1 The Entries in the Transfer-Function Matrix (2.2) \u003ci\u003e417\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA.2 Entries in the Transfer-Function Matrix (2.4) \u003ci\u003e418\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eA.3 Matrices \u003ci\u003eE\u003c\/i\u003e\u003csub\u003e1\u003c\/sub\u003e\u003ci\u003e, E\u003c\/i\u003e\u003csub\u003e2\u003c\/sub\u003e\u003ci\u003e, H\u003c\/i\u003e\u003csub\u003e1\u003c\/sub\u003e, and \u003ci\u003eH\u003c\/i\u003e\u003csub\u003e2\u003c\/sub\u003e for the Designs of (7.36) and (7.37) \u003ci\u003e419\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 421\u003c\/p\u003e \u003cp\u003eIndex 437\u003c\/p\u003e  \u003cp\u003eProfessor Eric Rogers, Dr. Bing Chu, Professor Christopher Freeman, and Professor Paul Lewin, University of Southampton, UK   \u003c\/p\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003eIterative Learning Control Algorithms and Experimental Benchmarking\u003c\/b\u003e\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003e\u003cb\u003ePresents key cutting edge research into the use of iterative learning control\u003c\/b\u003e\u003c\/i\u003e \u003c\/p\u003e\u003cp\u003eThe book discusses the main methods of iterative learning control (ILC) and its interactions, as well as comparator performance that is so crucial to the end user. The book provides integrated coverage of the major approaches to-date in terms of basic systems, theoretic properties, design algorithms, and experimentally measured performance, as well as the links with repetitive control and other related areas.  \u003c\/p\u003e\u003cp\u003eKey features: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e Provides comprehensive coverage of the main approaches to ILC and their relative advantages and disadvantages.\u003c\/li\u003e \u003cli\u003e Presents the leading research in the field along with experimental benchmarking results. \u003c\/li\u003e \u003cli\u003e Demonstrates how this approach can extend out from engineering to other areas and, in particular, new research into its use in healthcare systems\/rehabilitation robotics.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003eThe book is essential reading for researchers and graduate students in iterative learning control, repetitive control and, more generally, control systems theory and its applications.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989485043941,"sku":"NP9780470745045","price":135.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470745045.jpg?v=1761784291","url":"https:\/\/k12savings.com\/products\/iterative-learning-control-algorithms-and-experimental-benchmarking-isbn-9780470745045","provider":"K12savings","version":"1.0","type":"link"}