Adaptive Control with Recurrent High-order Neural Networks

1. Introduction.- 1.1 General Overview.- 1.2 Book Goals & Outline.- 1.3 Notation.- 2. Identification of Dynamical Systems Using Recurrent High-order Neural Networks.- 2.1 The RHONN Model.- 2.1.1 Approximation Properties.- 2.2 Learning Algorithms.- 2.2.1 Filtered Regressor RHONN.- 2.2.2 Filtered Error RHONN.- 2.3 Robust Learning Algorithms.- 2.4 Simulation Results.- Summary.- 3. Indirect Adaptive Control.- 3.1 Identification.- 3.1.1 Robustness of the RHONN Identifier Owing to Unmodeled Dynamics.- 3.2 Indirect Control.- 3.2.1 Parametric Uncertainty.- 3.2.2 Parametric plus Dynamic Uncertainties.- 3.3 Test Case: Speed Control of DC Motors.- 3.3.1 The Algorithm.- 3.3.2 Simulation Results.- Summary.- 4. Direct Adaptive Control.- 4.1 Adaptive Regulation - Complete Matching.- 4.2 Robustness Analysis.- 4.2.1 Modeling Error Effects.- 4.2.2 Model Order Problems.- 4.2.3 Simulations.- 4.3 Modeling Errors with Unknown Coefficients.- 4.3.1 Complete Model Matching at |x| = 0.- 4.3.2 Simulation Results.- 4.4 Tracking Problems.- 4.4.1 Complete Matching Case.- 4.4.2 Modeling Error Effects.- 4.5 Extension to General Affine Systems.- 4.5.1 Adaptive Regulation.- 4.5.2 Disturbance Effects.- 4.5.3 Simulation Results.- Summary.- 5. Manufacturing Systems Scheduling.- 5.1 Problem Formulation.- 5.1.1 Continuous Control Input Definition.- 5.1.2 The Manufacturing Cell Dynamic Model.- 5.2 Continuous-time Control Law.- 5.2.1 The Ideal Case.- 5.2.2 The Modeling Error Case.- 5.3 Real-time Scheduling.- 5.3.1 Determining the Actual Discrete Dispatching Decision.- 5.3.2 Discretization Effects.- 5.4 Simulation Results.- Summary.- 6. Scheduling using RHONNs: A Test Case.- 6.1 Test Case Description.- 6.1.1 General Description.- 6.1.2 Production Planning & Layout in SHW.- 6.1.3 Problem Definition.- 6.1.4 Manufacturing Cell Topology.- 6.1.5 RHONN Model Derivation.- 6.1.6 Other Scheduling Policies.- 6.2 Results & Comparisons.- Summary.- References.

[1]  David G. Taylor,et al.  Adaptive Regulation of Nonlinear Systems with Unmodeled Dynamics , 1988, 1988 American Control Conference.

[2]  M. Dolinska,et al.  Dynamic control of flexible manufacturing systems , 1995 .

[3]  K S Narendra,et al.  IDENTIFICATION AND CONTROL OF DYNAMIC SYSTEMS USING NEURAL NETWORKS , 1990 .

[4]  Frank L. Lewis,et al.  Neural net robot controller with guaranteed tracking performance , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[5]  V. V. Chalam,et al.  Lyapunov Redesign of Model Reference Adaptive Control Systems (Part I) , 1979 .

[6]  K. Narendra,et al.  Stable adaptive controller design, part II: Proof of stability , 1980 .

[7]  Teuvo Kohonen,et al.  Content-addressable memories , 1980 .

[8]  Petros A. Ioannou,et al.  Robust adaptive control: a unified approach , 1991 .

[9]  Hassan K. Khalil,et al.  Singular perturbation methods in control : analysis and design , 1986 .

[10]  K. Narendra,et al.  A new adaptive law for robust adaptation without persistent excitation , 1987 .

[11]  A. Morse,et al.  Adaptive control of single-input, single-output linear systems , 1977, 1977 IEEE Conference on Decision and Control including the 16th Symposium on Adaptive Processes and A Special Symposium on Fuzzy Set Theory and Applications.

[12]  P J Webros.  BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[13]  J. Hale,et al.  Ordinary Differential Equations , 2019, Fundamentals of Numerical Mathematics for Physicists and Engineers.

[14]  Graham C. Goodwin,et al.  A parameter estimation perspective of continuous time model reference adaptive control , 1987, Autom..

[15]  George A. Rovithakis,et al.  Tracking control of multi-input affine nonlinear dynamical systems with unknown nonlinearities using dynamical neural networks , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[16]  D. J. G. James,et al.  Stability of a model reference control system , 1971 .

[17]  Andrew Kusiak,et al.  Designing expert systems for scheduling automated manufacturing , 1987 .

[18]  S. Gershwin,et al.  A control perspective on recent trends in manufacturing systems , 1986, IEEE Control Systems Magazine.

[19]  Manolis A. Christodoulou,et al.  On Using Recurrent Neural Network Models To Develop Direct Robust Adaptive Regulators For Unknown Systems , 1996, Intell. Autom. Soft Comput..

[20]  Juergen Fenchel.  Stable, distributed real-time scheduling of flexible manufacturing systems :an energy approach , 1995 .

[21]  A. Dembo,et al.  High-order absolutely stable neural networks , 1991 .

[22]  Pineda,et al.  Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[23]  Werner Leonhard,et al.  Control of Electrical Drives , 1990 .

[24]  Gopal Dubey.  Power Semiconductor Controlled Drives , 1989 .

[25]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[26]  Manolis A. Christodoulou,et al.  Neural adaptive regulation of unknown nonlinear dynamical systems , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[27]  C. R. Bector,et al.  A review of scheduling rules in flexible manufacturing systems , 1989 .

[28]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[29]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[30]  Ye-Hwa Chen,et al.  Adaptive robust control of uncertain systems , 1990, 29th IEEE Conference on Decision and Control.

[31]  Marios M. Polycarpou,et al.  High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.

[32]  A. Sharifnia.  Stability and performance of distributed production control methods based on continuous-flow models , 1994 .

[33]  J. Hopfield.  Neurons withgraded response havecollective computational properties likethoseoftwo-state neurons , 1984 .

[34]  Manolis A. Christodoulou,et al.  Regulation of unknown nonlinear dynamical systems via dynamical neural networks , 1995, J. Intell. Robotic Syst..

[35]  M. Polycarpou,et al.  On the existence and uniqueness of solutions in adaptive control systems , 1993, IEEE Trans. Autom. Control..

[36]  Petros A. Ioannou,et al.  Adaptive Systems with Reduced Models , 1983 .

[37]  George A. Rovithakis.  Robust neural adaptive stabilization of unknown systems with measurement noise , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[38]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[39]  Pierre Baldi,et al.  Neural networks, orientations of the hypercube, and algebraic threshold functions , 1988, IEEE Trans. Inf. Theory.

[40]  Petros A. Ioannou,et al.  A robust direct adaptive controller , 1986 .

[41]  A. Morse,et al.  Global stability of parameter-adaptive control systems , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[42]  George A. Rovithakis,et al.  Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks , 1995, IEEE Transactions on Systems, Man, and Cybernetics.

[43]  Manolis A. Christodoulou,et al.  Real-time control of manufacturing cells using dynamic neural networks , 1999, Autom..

[44]  Neil E. Cotter,et al.  The Stone-Weierstrass theorem and its application to neural networks , 1990, IEEE Trans. Neural Networks.

[45]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[46]  M. P. R. V. Rao.  Stable adaptive systems, Kumpati S. Narendra and Anuradha M. Annaswamy, Prentice Hall, Englewood Cliffs, NJ, 1989, 494 pp., ISBN 0‐13‐839994‐8, £51.35 , 1990 .

[47]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[48]  Manolis A. Christodoulou,et al.  Adaptive control of unknown plants using dynamical neural networks , 1994, IEEE Trans. Syst. Man Cybern..

[49]  R. Marino,et al.  Global adaptive output-feedback control of nonlinear systems. I. Linear parameterization , 1993, IEEE Trans. Autom. Control..

[50]  C. L. Giles,et al.  Second-order recurrent neural networks for grammatical inference , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[51]  Martin Hasler,et al.  Recursive neural networks for associative memory , 1990, Wiley-interscience series in systems and optimization.

[52]  George Leitmann,et al.  On ultimate boundedness control of uncertain systems in the absence of matching assumptions , 1982 .

[53]  Stanley B. Gershwin,et al.  Manufacturing Systems Engineering , 1993 .

[54]  J. Andrew Ware,et al.  Layered Neural Networks as Universal Approximators , 1997, Fuzzy Days.

[55]  Zoe Doulgeri,et al.  Loss minimization in DC drives , 1991 .

[56]  K. Nam,et al.  A model reference adaptive control scheme for pure-feedback nonlinear systems , 1988 .

[57]  Graham C. Goodwin,et al.  Adaptive filtering prediction and control , 1984 .

[58]  N. Rouche,et al.  Stability Theory by Liapunov's Direct Method , 1977 .

[59]  A. Isidori,et al.  Adaptive control of linearizable systems , 1989 .

[60]  G. Campion,et al.  Indirect adaptive state feedback control of linearly parametrized nonlinear systems , 1990 .

[61]  Hassan K. Khalil,et al.  Adaptive control of a class of nonlinear discrete-time systems using neural networks , 1995, IEEE Trans. Autom. Control..

[62]  M. Corless,et al.  Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems , 1981 .

[63]  Chen-Chung Liu,et al.  Adaptively controlling nonlinear continuous-time systems using multilayer neural networks , 1994, IEEE Trans. Autom. Control..

[64]  Kumpati S. Narendra,et al.  Gradient methods for the optimization of dynamical systems containing neural networks , 1991, IEEE Trans. Neural Networks.

[65]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[66]  Riccardo Marino,et al.  An extended direct scheme for robust adaptive nonlinear control , 1991, Autom..

[67]  C. Desoer,et al.  Feedback Systems: Input-Output Properties , 1975 .

[68]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[69]  M.R. Azimi-Sadjadi,et al.  Detection of dim targets in high cluttered background using high order correlation neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[71]  M. A. Christodoulou,et al.  Direct adaptive regulation using dynamic neural networks: application to DC motors speed control , 1996 .

[72]  R. Marino,et al.  Global adaptive output-feedback control of nonlinear systems , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[73]  Graham Goodwin,et al.  Discrete time multivariable adaptive control , 1979, 1979 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[74]  George A. Rovithakis.  Robustifying nonlinear systems using high-order neural network controllers , 1999, IEEE Trans. Autom. Control..

[75]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[76]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.