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Monday, August 3, 2020 | History

5 edition of Optimal, predictive, and adaptive control found in the catalog.

Optimal, predictive, and adaptive control

by Edoardo Mosca

  • 128 Want to read
  • 32 Currently reading

Published by Prentice Hall in Englewood Cliffs, N.J .
Written in English

    Subjects:
  • Automatic control.,
  • Predictive control.,
  • Adaptive control systems.

  • Edition Notes

    Includes bibliographical references (p. 449-472) and index.

    StatementEdoardo Mosca.
    SeriesPrentice Hall information and system sciences series
    Classifications
    LC ClassificationsTJ213 .M558 1995
    The Physical Object
    Paginationxvii, 477 p. :
    Number of Pages477
    ID Numbers
    Open LibraryOL1082484M
    ISBN 100138476098
    LC Control Number94006082

    Mosca E () Optimal, predictive, and adaptive control. Prentice Hall, New Jersey Google Scholar Mosca E, Zappa G () ARX modeling of controlled ARMAX plants and LQ adaptive . the theory of nonlinear optimal predictive control is applied to the ideal induction motor speeding system, and dynamic decoupling of rotor speed and flux can be obtained. Considering the varieties of load torque and rotor resistance, an identification scheme of model reference adaptive system asymptotically tracks the true values of the load torque and rotor resistance.

    This book shows how approximate dynamic programming - a reinforcement machine learning technique that is motivated by learning mechanisms in biological and animal systems - can be used to design a family of adaptive optimal control algorithms that converge in real-time to optimal control solutions by measuring data along the system trajectories. Week (Exercise) Date: Recommended Reading: Contents: 4 (E1) 29/1 (i) Johansson (This is not covered in the book of Johansson, but the manual should be sufficient); (ii) Åström Section ; (iii) Manual for computer exercise. Simulation of Adaptive Systems. Sign up here!: 5 (E2) 31/1 (i) Johansson Sections ; (ii) Åström, Sections and (be sure to .

    Adaptive Control and Optimal Control methods. The book shows how ADP can be used to design a family of adaptive optimal control algorithms that converge in real-time to optimal control solutions by measuring data along the system trajectories. Generally, in the current. Keywords: Predictive control, Adaptive control, Discrete-time systems, Generalized predictive control, Mean-level control, dead-beat control, Robustness, Self-tuning, with a more general-purpose law which is in some sense optimal when used with processes which have complex (possibly time-varying) dynamics such as dead-time and.


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Optimal, predictive, and adaptive control by Edoardo Mosca Download PDF EPUB FB2

Key Benefit: Using a common unifying framework, this volume explores the main topics of Linear Quadratic control, predictive control, and adaptive predictive control -- in terms of theoretical foundations, analysis and design methodologies, and Cited by: Optimal, Predictive and Adaptive Control | Edoardo Mosca | download | B–OK.

Download books for free. Find books. LQ and Predictive Stochastic Control --pt. Adaptive Control. Single-Step-Ahead Self-Tuning Control. Adaptive Predictive Control --Appendix A Some Results from Linear Systems Theory --Appendix B Some Results of Polynomial Matrix Theory --Appendix C Some Results on Linear Diophantine Equations --Appendix D Probability.

In this paper a novel approach for energy-optimal adaptive cruise control (ACC) combining model predictive control (MPC) and dynamic programming (DP) is presented. The approach uses knowledge about a given route to precalculate a position-dependent energy-optimal speed trajectory using DP while taking information like speed limits, road slope Cited by: Exploring connections between adaptive control theory and practice, this book treats the techniques of linear quadratic optimal control and estimation (Kalman filtering), recursive identification, linear systems theory and robust arguments.

He is with the Control and Estimation Tools development team at The MathWorks, Inc. His research interest is in the areas of nonlinear control, optimal control, neural network control, and adaptive intelligent systems. He is the author/co-author of one book, two book chapters, 13 journal papers and and adaptive control book refereed conference proceedings.

Online Kernel Learning With Adaptive Bandwidth by Optimal Control Approach Article in IEEE Transactions on Neural Networks and Learning Systems PP(99). Topics include 1) basic knowledge of adaptive control and optimal control, 2) concept of model reference adaptive control system (MRACS) and its properties, and 3) concept of model predictive predictive (MPC) and its solution.

In this lecture, how to take on different merits depending on requirements and condition to be needed is also considered. The area of adaptive control has grown to be one of the richest in terms of algorithms, design techniques, analytical tools, and modiflcations.

Several books and research monographs already exist on the topics of parameter estimation and adaptive control. Despite this rich literature, the fleld of adaptive control may easily appear. Optimal control solution techniques for systems with known and unknown dynamics.

Dynamic programming, Hamilton-Jacobi reachability, and direct and indirect methods for trajectory optimization. Introduction to model predictive control. Model-based reinforcement learning, and connections between modern reinforcement learning in continuous spaces and fundamental optimal control.

Adaptive control is the control method used by a controller which must adapt to a controlled system with parameters which vary, or are initially uncertain. For example, as an aircraft flies, its mass will slowly decrease as a result of fuel consumption; a control law is needed that adapts itself to such changing conditions.

This book is about prediction and control of processes which can be expressed by discrete-time models (i.e. the characteristics vary in some way with time). The aim of the book is to provide a unified and comprehensive coverage of the principles, perspectives and methods of adaptive prediction, which is used by scientists and researchers in a wide variety of disciplines.

Read or Download Optimal, Predictive and Adaptive Control PDF. Similar electrical & electronic engineering books. C & Data Structures (Electrical and Computer Engineering Series) Divided into 3 separate sections, C & information buildings covers C programming, in addition to the implementation of information buildings and an research of complex.

In the Adaptive Model Predictive Control (AMPC) framework we primarily focus on learning and improving the uncertain model of a dynamical sytem to improve controller performance. We systematically use input-output data from the system to synthesize maximum bounds on the uncertainties present in the model, which we adapt as we gather more and.

Abstract In Model Predictive Control (MPC) an optimal control problem has to be solved at each sampling instant. The objective of this thesis is. Reinforcement Learning and Optimal Adaptive Control; Enter your mobile number or email address below and we'll send you a link to download the free Kindle App.

Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. Apple.

Android. Windows Phone. Android. To get the free app, enter your Reviews: Most physical systems possess parametric uncertainties or unmeasurable parameters and, since parametric uncertainty may degrade the performance of model predictive control (MPC), mechanisms to update the unknown or uncertain parameters are desirable in application.

One possibility is to apply adaptive extensions of MPC in which parameter estimation and control. Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the s.

In recent years it has also been used in power system balancing models and in power predictive controllers rely on. The LQG feedback optimal control, extended for the case of preprogrammed setpoint sequence, is compared with open-loop optimal Predictive control (GPC) [4].

It is proved that, for any linear stochastic model (with possible time delay), the first control input within the receding control horizon is the same for both cases. Home Browse by Title Books Adaptive Optimal Control: The Thinking Man's G.P.C. Adaptive Optimal Control: The Thinking Man's G.P.C.

January January Read More. Authors: Techniques for predictor design in multivariable predictive control, WSEAS Transactions on Systems and Control. Project Management: Adaptive Vs. Predictive. Waterfall project management is a more predictive planning strategy that utilizes specific steps and milestones to control the process.

A predictive planning strategy may fail when confronted by significant project specification changes or customer modifications, but it will also be more likely. An illustration of an open book. Books. An illustration of two cells of a film strip.

Video. An illustration of an audio speaker. Audio An illustration of a " floppy disk. Edoardo_Mossa__Optimal_Predictive_and_Adaptive_Control Identifier-ark ark://t1gj1h72m Ocr ABBYY FineReader Ppi plus-circle Add Review.

comment.Adaptive-Optimal Control of Constrained Nonlinear Uncertain Dynamical Systems using Concurrent Learning Model Predictive Control Maximilian Muhlegg¨, Girish Chowdharyy, Jonathan P. How zand Florian Holzapfelx A concurrent learning adaptive-optimal control architecture for aerospace systems with fast dynamics is presented.