CDC2000 Iterative Learning and Control

Iterative Learning and Control
Home Full List of Titles 1: Proceedings of CDC2000 Discrete Event Systems Control in Communication Systems Optimal Control and Applications I Optimisation Approaches and Methods Model Predictive Control Advances in Linear Estimation Stochastic and Uncertain Systems Nonlinear Control and Applications Nonlinear Estimation and Filtering Formation Control and its Applications New Approaches to Fuzzy Control Manufacturing Systems Automotive Applications Stability Issues in Hybrid Control Recent Advances in Stochastic Networks Optimal Control and Applications II Robust Controller Design - mu, L1 and H2 Constrained and Receding Horizon Control Identification and Control around the World Markov Decision Processes Nonlinear Optimisation Observers for Nonlinear Systems Motion Planning Neural / Fuzzy Stability and Control Motor Control Control of Quantum Phenomena I Hybrid Systems Methods Control in Communication Networks Robustness and Optimisation Bumpless Transfer, Antiwindup and Saturation Adaptive Control: Linear Systems Estimation and Closed Loop Identification Control of Markov Processes Nonlinear Filtering and Control Modelling, Identification and Validation of Nonlinear Systems Differential Geometric Control Theory for Mechanical Systems Nonlinear Output Feedback Control Pneumatics and Compression Systems Control of Quantum Phenomena II Stability of Hybrid Systems Performance Analysis in Communication Networks Adaptive Control of Nonlinear Systems LMI Methods in Design Robust Control of Time Delay Systems Subspace Identification Methods Nonlinear Stochastic Filtering and Estimation Bifurcations, Chaos and Control I New Progress in Synthesis of Nonlinear Systems I Implementation Issues of Sliding Mode Control Theory Control of Mixing in Shear Flows Novel Neural Network Control Techniques for Industrial Motion Control Systems Physiological Control Systems Optimal Control of Hybrid Systems Stochastic Models for Communication Networks Control and Stabilisation of Nonlinear Systems New Directions in Robust Control Linear Systems Theory Advanced Topics in Systems Theory Estimation in Action Bifurcations, Chaos and Control II New Progress in Synthesis of Nonlinear Systems II Numerical Design and Analysis Techniques for Nonlinear Systems Analysis and Control of Underactuated Systems Sliding Mode Control I Challenges in the Application of Control to Computer Systems Estimation and Diagnosis of Discrete Event Systems Communications and Games Optimal Control Stochastic Systems Model Reduction Methodologies Identification and Subspace Methods Applications of Nonlinear Adaptive Control Advances in Nonlinear Output Feedback Design The Behavioural Approach to Systems and Control Vision Based Estimation and Control: Recent Advances and Open Problems Agile Control of Military Operations Sliding Mode Control II Model-based Fault Diagnosis of Industrial Processes Discrete Event Systems / Petri Nets System Identification and Confidence Estimation New Approaches to H-Infinity Control I Probabilistic Approaches to Robust Control Time Delay System Stabilisation Identification Methods Controlled Stochastic Processes Output Feedback of Nonlinear Systems Topics in Nonlinear Stabilisation Mobile Robots: Tracking Control Robust Control of Nonlinear Systems Power Systems Stabilisation and Control Disk Drive Control Hybrid Control Applications Discrete Time Systems New Approaches to H-Infinity Control II Linear Systems with Saturating Actuators New Theories in Distributed Parameter Systems Applications of Estimation and Identification Stochastic Control and Tuning Methodologies Control of Nonlinear Systems Iterative Learning and Control Coordinating Robot Systems Nonlinear Time Varying Systems Novel Applications of Neural Networks Aerospace Applications Switched Systems Implicit and Descriptor Systems LQG Periodic Systems and Disturbances New Horizons for Distributed Parameter Systems State Estimation Learning and Neuro-Control Nonlinear Control and Stabilisation I Tracking Vision Servoing Controllability of Nonlinear Systems Control of Flexible Systems Electro-Mechanical Systems Robust Control Methods and Applications Fault Detection and Diagnosis Optimisation and Applications Robust Stability Analysis Numerical Methods in Control Filtering in Continuous Time Stochastic Systems Interplay between Control and Signal Processing Fault Detection and Analysis Nonlinear Dynamical Systems Nonlinear Time Delay Systems Computational Issues in Nonlinear Control Disturbance Rejection Process Control Industry Applications Linear Parameter Varying Systems Linear Control Systems Dynamic and Nonlinear Programming Model Reduction Applications New Techniques for Control and Systems: Numerical Linear Algebra Estimation and Identification using Hidden Markov Models Applications of Stochastic Control Topics in Linear Design Nonlinear Control and Stabilisation II Ambulatory Robot Systems Chaotic and Oscillatory Systems Biomedical System Control Integrated Control and CPU Scheduling Linear Design Techniques Adaptive Disturbance / Noise Compensation Nonlinear Model Predictive Control Sensitivity Design, Analysis and Limitations Analysis of Linear Systems Linear Matrix Inequalities in Design Lyapunov's 2nd Method Robotics: Tracking Control Lagrangian and Hamiltonian Theory Variable Structure Control Machine Vision Signal Processing Methods in Control Applied Nonlinear Control Author Index A B C D E F G H I J K L M N O P Q R S T U V W X Y Z	Comparative Study On First And Second Order ILC -- Frequency Domain Analysis And Experiments Authors: M. Norrlöf, Volume: 1, Page 3415 Paper number 1108 Abstract: Aspects on the behavior of a general second order iterative learning control (ILC) algorithm is presented from a frequency domain perspective. This includes stability as well as performance and robustness issues. The basis for the analysis is linear iterative systems and these are briefly described. A design algorithm for second order ILC schemes is proposed and analyzed both theoretically as well as in an experiment. In the experiment, done on a commercial industrial robot control system, the second order ILC design is compared with a first order ILC design. The result from both the analysis and the experiment is that the second order design is not better with respect to performance or robustness. CD001108.PDF (From Author) TOP Approximate Value Iteration With Randomized Policies Authors: Daniela P. de Farias, Benjamin Van Roy, Volume: 1, Page 3421 Paper number 9032 Abstract: The curse of dimensionality in dynamic programming prevents in most problems of practical interest the exact computation of the value function. In this paper, we study the fixed points of approximate value iteration, a simple algorithm that combats the curse of dimensionality by generating approximate iterates of the classical value iteration algorithm in the span of a set of prespecified basis functions. We show that, in general, the modified dynamic programming operator need not possess a fixed point, and therefore, approximate value iteration should not be expected to converge. However, by using a class of randomized policies, approximate value iteration is guaranteed to possess at least one fixed point. We finally discuss the link between approximate value iteration and temporal-difference learning (TD), and show that the existence of fixed points for approximate value iteration implies existence of stationary points for the ordinary differential equation approximated by a version of TD that incorporates ``exploration''. CD009032.PDF (From Author) CD009032.PDF (Scanned) TOP Iterative Optimal Control of Liquid Slosh in an Industrial Packaging Machine Authors: Mattias Grundelius, Volume: 1, Page 3427 Paper number 1668 Abstract: This paper presents an iterative approach to optimal control when modeling errors are present. The problem considered is movement of open containers containing liquid in an industrial packaging machine. The goal is to move as fast as possible without too much slosh. There is no measurement of the slosh available in the machine, therefore open loop control via the acceleration reference is the only possibility to control the slosh. However it is possible to measure the slosh in an experimental testbed. This can be used to offline determine the acceleration reference. An algorithm for iterative optimal control is derived that uses experimental data to refine the solution of the optimal control problem. CD001668.PDF (From Author) TOP A Learning Model for Intelligent Agents Based on Classifier Systems and Approximate Reasoning Authors: Jalal Baghdadchi, Volume: 1, Page 3433 Paper number 2143 Abstract: The objective of this study is to synthesize a learning model capable of successful and effective operation in hard-to-model environments. Here, we are presenting a structurally simple and functionally flexible model. The model follows the learning patterns experienced by the humans. The novelty of the adaptive model lies on the knowledge base, dual learning strategy, and flexible reasoning. The knowledge base is allowed to grow for as long as the agent lives. Learning is brought about by the interaction between two qualitatively different activities leaving long-term and short-term marks on the behavior of the agent. The agent reaches conclusions using approximate reasoning. The focus of the model, the agent, starts life with a blank knowledge base. It learns as it lives. Classifiers are used to represent individual experiences. We demonstrate the functioning of the model through a case study. CD002143.PDF (Scanned) TOP Internal Model-Based Robust Iterative Learning Control for Uncertain LTI Systems Authors: Abdelhamid Tayebi, Marek. B. Zaremba, Volume: 1, Page 3439 Paper number 1672 Abstract: This paper investigates the combination of an iterative learning control (ILC) with an internal model control (IMC) for uncertain linear time-invariant (LTI) systems. The convergence of the iterative process is investigated and reformulated as a general robust control problem. For a certain choice of the IMC and ILC filters, we prove that the condition of convergence to zero of the iterative process is nothing but the robust performance condition of the IMC structure. Using the general robust control formulation, we propose a design procedure for the ILC-IMC filters using the mu-synthesis approach. CD001672.PDF (From Author) CD001672.PDF (Scanned) TOP Iterative Learning Control Using Adjoint Systems For Nonlinear Non-Minimum Phase Systems Authors: Sogo Takuya, Kouji Kinoshita, Norihiko Adachi, Volume: 1, Page 3445 Paper number 9113 Abstract: Most of iterative learning control (ILC) using causal updating law obtains the input given by Silverman's or Hirshorn's causal inversion. When the objective system is that of a non-minimum phase, we cannot use those methods because the input is exponentially increasing. To overcome this difficulty, an approach called stable inversion was proposed to give a non-causal but bounded input instead. However, no simple iterative method to obtain this non-causal input was proposed. In this paper, from a viewpoint of minimization, we develop a simple iterative method for the stable inversion toward ILC for non-minimum phase systems. CD009113.PDF (From Author) TOP Decentralized Stabilization of Fuzzy Large-Scale Systems Authors: Feng Hsiag Hsiao, Jiing Dong Hwang, Lin Goei Shiau, Volume: 1, Page 3447 Paper number 130 Abstract: A stability criterion in terms of Lyapunov's direct method is derived in this paper to guarantee the asymptotic stability of fuzzy large-scale systems. Based on this criterion and the decentralized control scheme, a set of fuzzy controllers is synthesized via the technique of parallel distributed compensation (PDC) to stabilize a fuzzy large-scale system which consists of a few interconnected subsystems represented by Takagi- Sugeno (T-S) fuzzy models. Finally, a numerical example with simulations is given to illustrate the results. CD000130.PDF (From Author) TOP