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Saturday, August 1, 2020 | History

1 edition of Sequential suboptimal adaptive control of nonlinear systems found in the catalog.

Sequential suboptimal adaptive control of nonlinear systems

by Thomas Walter Ellis

  • 314 Want to read
  • 29 Currently reading

Published .
Written in English

    Subjects:
  • System analysis,
  • Nonlinear theories

  • Edition Notes

    Other titlesSuboptimal adaptive control.
    The Physical Object
    Paginationvii, 76 leaves.
    Number of Pages76
    ID Numbers
    Open LibraryOL25903590M
    OCLC/WorldCa13645272

    The Kalman filter uses a system's dynamic model (e.g., physical laws of motion), known control inputs to that system, and multiple sequential measurements (such as from sensors) to form an estimate of the system's varying quantities (its state) that is better than the estimate obtained by using only one measurement alone. [57] Chen W, Jiao L, Li J, et al. Adaptive NN backstepping output-feedback control for stochastic nonlinear strict-feedback systems with time-varying delays. IEEE Trans Syst Man Cybern-Part B: Cybernet, ,

    This book was written in response to the growing demand for a text that provides a unified treatment of linear and nonlinear complex valued adaptive filters, and methods for the processing of general complex signals (circular and noncircular). Xianzhong Chen, Jinfeng Liu, David Muñoz de la Peña, Panagiotis D. Christofides, Sequential and Iterative Distributed Model Predictive Control of Nonlinear Process Systems Subject to Asynchronous Measurements, IFAC Proceedings Volumes, /BE .

      W. Gao and Z. P. Jiang, Nonlinear and adaptive suboptimal control of connected vehicles: a global adaptive dynamic programming approach, Journal of Intelligent & Robotic Systems, Vol. 85, pp. , Sequential suboptimal adaptive control of nonlinear systems. By T. W. Ellis and A. P. Sage. Abstract. Sequential suboptimal adaptive control of nonlinear systems, considering generation of optimum closed-loop control for startup dynamics of nuclear reacto Topics: ELECTRONICS.


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Sequential suboptimal adaptive control of nonlinear systems by Thomas Walter Ellis Download PDF EPUB FB2

A suboptimal dual adaptive system is developed for control of stochastic, nonlinear, discrete time plants that are affine in the control input. The nonlinear functions are assumed to be unknown and neural networks are used to approximate by: 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. Adaptive Systems in Control and Signal Processing is a compendium of papers presented at the International Federation of Automatic Control in San Francisco on JuneOne paper addresses the results through comparative alternative algorithms in adaptive control of linear time invariant and time varying systems.

Linearization via optimal control (O.C.) of a discrete nonlinear multivariable system is analysed and an adaptive linear control is presented. The performances of the adaptive control applied to a simulated fermentation process linearized by Taylor series expansion and via O.C.

are compared, showing that the second option gives a very robust. As an efficient and frequently utilized way, fuzzy logic system (FLS) can approximate a smooth nonlinear function accurately [25], [27], so it has been widely used to identify and control unknown.

There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP).

The range of systems treated is extensive; affine, switched, singularly perturbed. To derive optimal control, Riccati equations are solved for linear systems, while Hamilton-Jacobi-Bellman (HJB) equations for nonlinear systems.

Since adaptive dynamic programming (ADP) [1][2] [3. Nonlinear control at the level of ME is helpful, but not required. This course provides a theoretical foundation for developing adaptive controllers for dynamic systems. Topics include system identification, model reference adaptive control, adaptive pole placement control, and adaptive control of nonlinear systems.

Meets with ENG ME The stabilizing composite control in a weakly nonlinear singularly perturbed control system Event-Triggered Suboptimal Tracking Controller Design for a Class of Nonlinear Discrete-Time Systems IEEE Transactions on Industrial Electronics, Vol.

64, No. Robust and adaptive model predictive control of non-linear systems Adetola, Veronica, DeHaan, Darryl, Guay, Martin This book offers a novel approach to adaptive control and provides a sound theoretical background to designing robust adaptive control systems.

Adaptive Fuzzy Sliding Mode Control for MIMO Nonaffine Dutch-Roll System 28 June | Journal of Dynamic Systems, Measurement, and Control, Vol. No. 10 Design of a pseudo- PD or PI robust controller to track C2 trajectories for a class of uncertain nonlinear MIMO systems.

Adaptive boundary control for stochastic parabolic systems with unknown potential coefficient. Proceedings of 33rd IEEE Conference on Decision and Control, Information inequalities, rate of adaptation and nonlinear identification in adaptive stochastic control.

There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming in Discrete Time approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP).

Because the system described in this work is optimal, it differs from previous attempts at adaptive estimation, all of which have used approximation techniques or sub-optimal, sequential, optimization procedures [12], [13], and [14]. Baba K, Yamamoto D, Akagi M. Model reference adaptive control systems with state estimators to mitigate seismic responses of structures.

Theor Appl ; [71] Chu S-Y, Lo S-C, Chang M-C. Real-time control performance of a model-reference adaptive structural control system under earthquake excitation.

Optimal Control of Uncertain Nonlinear Networked Control Systems via Neurodynamic Programming. Optimal Networked Control Systems with MATLAB, () Optimal minimum bids and inventory scrapping in sequential, single-unit, Vickrey auctions with demand learning.

A wide class of mechanical systems with uncertainties can be modeled through a state equation in parametric-pure-feedback form. Thus, in principle, the well-known backstepping design procedure can be applied to solve a regulation or a tracking problem. Hence, this chapter reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems.

The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized.

TÜLAY ADALI, PhD, is Professor of Electrical Engineering and Director of the Machine Learning for Signal Processing Laboratory at the University of Maryland, Baltimore County.

Her research interests are in statistical and adaptive signal processing, with emphasis on nonlinear and complex-valued signal processing, and applications in biomedical data analysis and communications.

Abstract: In this paper, we propose adaptive second-order Volterra filtered-X recursive least square (RLS) algorithms using sequential and partial updates for nonlinear active noise control.

Recent research advancement has demonstrated that nonlinear active control is feasible for applications where the noise to be controlled may be a nonlinear and deterministic noise process such as chaotic.

D. S. Bernstein, “The Treatment of Inputs in Real-Time Digital Simulation,” Simulation, Vol. 33, No. 2, pp.D. S. Bernstein and E. G. Gilbert. In this paper combined algorithms for the control of nontriangular nonlinear systems with unmatched uncertainties will be presented.

The controllers consist of a combination of Dynamical Adaptive Backstepping (DAB) and Sliding Mode Control (SMC) of first and second order.control, and for nonlinear DT systems, the updated control laws will converge to the suboptimal control.

Index Terms—Generalized Hamilton–Jacobi–Bellman (BHJB) equation, neural network (NN), nonlinear discrete-time (DT) system. I. INTRODUCTION I N the literature, there are many methods of designing stable control of nonlinear systems.