Neural Generalized Predictive Control: A Newton-Raphson Implementation
01 Feb 1997-
TL;DR: This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm and results show convergence to a good solution within two iterations and timing data show that real-time control is possible.
Abstract: An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant''s nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm''s implementation are also included.
Citations
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TL;DR: A novel nonlinear neural network (NN) predictive control strategy based on the new tent-map chaotic particle swarm optimization (TCPSO) is presented to enhance the convergence and accuracy of the TCPSO.
Abstract: In this letter, a novel nonlinear neural network (NN) predictive control strategy based on the new tent-map chaotic particle swarm optimization (TCPSO) is presented. The TCPSO incorporating tent-map chaos, which can avoid trapping to local minima and improve the searching performance of standard particle swarm optimization (PSO), is applied to perform the nonlinear optimization to enhance the convergence and accuracy. Numerical simulations of two benchmark functions are used to test the performance of TCPSO. Furthermore, simulation on a nonlinear plant is given to illustrate the effectiveness of the proposed control scheme
185 citations
Cites background or methods from "Neural Generalized Predictive Contr..."
...Several algorithms were successfully implemented in the NNPC, such as gradient–descent [7]–[9] and Newton–Raphson methods [10]....
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...Some related works have been performed [7]–[10], where nonlinear programming is needed to obtain a solution....
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TL;DR: Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient quasi-Newton algorithm.
63 citations
TL;DR: In this paper, a model-following adaptive control design technique for a class of non-affine and non-square nonlinear systems using neural networks is proposed, where an appropriate stabilising controller is assumed available for a nominal system model.
Abstract: A new model-following adaptive control design technique for a class of non-affine and non-square nonlinear systems using neural networks is proposed. An appropriate stabilising controller is assumed available for a nominal system model. This nominal controller may not be able to guarantee stability/satisfactory performance in the presence of unmodelled dynamics (neglected algebraic terms in the mathematical model) and/or parameter uncertainties present in the system model. In order to ensure stable behaviour, an online control adaptation procedure is proposed. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture matched unmodelled (neglected) dynamics or model uncertainties because of parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a desired nominal model. The neural network weight update rule is derived using Lyapunov theory, which guarantees both stability of the error dynamics (in a practical stability sense) and boundedness of the weights of the neural networks. The proposed adaptation procedure is independent of the technique used to design the nominal controller, and hence can be used in conjunction with any known control design technique. Numerical results for two challenging illustrative problems are presented, which demonstrate these features and clearly bring out the potential of the proposed approach.
61 citations
TL;DR: In this paper, a mathematical model based on first principles is presented to avert temperature fluctuations in SOFC (solid oxide fuel cell) systems operating at high temperatures, temperature fluctuation induces a thermal stress in the electrodes and electrolyte ceramics; therefore, the cell temperature distribution is recommended to be kept as constant as possible.
58 citations
Dissertation•
01 Jan 2007
TL;DR: Thesis (Sc. D.) as discussed by the authors, Mass. Institute of Technology, Dept. of Aeronautics and Astronautics, 2007, Boston, MA, United States.
Abstract: Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.
49 citations
References
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Book•
31 Jan 1986TL;DR: Numerical Recipes: The Art of Scientific Computing as discussed by the authors is a complete text and reference book on scientific computing with over 100 new routines (now well over 300 in all), plus upgraded versions of many of the original routines, with many new topics presented at the same accessible level.
Abstract: From the Publisher:
This is the revised and greatly expanded Second Edition of the hugely popular Numerical Recipes: The Art of Scientific Computing. The product of a unique collaboration among four leading scientists in academic research and industry, Numerical Recipes is a complete text and reference book on scientific computing. In a self-contained manner it proceeds from mathematical and theoretical considerations to actual practical computer routines. With over 100 new routines (now well over 300 in all), plus upgraded versions of many of the original routines, this book is more than ever the most practical, comprehensive handbook of scientific computing available today. The book retains the informal, easy-to-read style that made the first edition so popular, with many new topics presented at the same accessible level. In addition, some sections of more advanced material have been introduced, set off in small type from the main body of the text. Numerical Recipes is an ideal textbook for scientists and engineers and an indispensable reference for anyone who works in scientific computing. Highlights of the new material include a new chapter on integral equations and inverse methods; multigrid methods for solving partial differential equations; improved random number routines; wavelet transforms; the statistical bootstrap method; a new chapter on "less-numerical" algorithms including compression coding and arbitrary precision arithmetic; band diagonal linear systems; linear algebra on sparse matrices; Cholesky and QR decomposition; calculation of numerical derivatives; Pade approximants, and rational Chebyshev approximation; new special functions; Monte Carlo integration in high-dimensional spaces; globally convergent methods for sets of nonlinear equations; an expanded chapter on fast Fourier methods; spectral analysis on unevenly sampled data; Savitzky-Golay smoothing filters; and two-dimensional Kolmogorov-Smirnoff tests. All this is in addition to material on such basic top
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"Neural Generalized Predictive Contr..." refers methods in this paper
...In this form Equation (2) can be solved with two routines supplied in [7], the LU decomposition routine, ludcmp, and the system of linear equations solver, lubksb....
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...One technique to avoid the use of a matrix inverse is to use LU decomposition [7] to solve for the control input vector U(k+l)....
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TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.
Abstract: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems. The emphasis is on models for both identification and control. Static and dynamic backpropagation methods for the adjustment of parameters are discussed. In the models that are introduced, multilayer and recurrent networks are interconnected in novel configurations, and hence there is a real need to study them in a unified fashion. Simulation results reveal that the identification and adaptive control schemes suggested are practically feasible. Basic concepts and definitions are introduced throughout, and theoretical questions that have to be addressed are also described. >
7,692 citations
01 Jan 1989
TL;DR: This paper presents a list of recommended recipes for making CDRom decks and some examples of how these recipes can be modified to suit theommelier's needs.
Abstract: Keywords: informatique ; numerical recipes Note: contient un CDRom Reference Record created on 2004-09-07, modified on 2016-08-08
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