Adaptation and Learning in Control and Signal Processing 2001By
- S. Bittanti
In control and signal processing, adaptation is a natural tool to cope with real-time changes in the dynamical behaviour of signals and systems. In this area, strongly connected with prediction and identification, there has been an increasing interest in switching and supervising methods. Moreover in recent years, special attention has been paid to the ideas evolving round the theory of statistical learning as a potential tool of improved adaptation.
The IFAC workshop on Adaptation and Learning in Control and Signal Processing in 2001 gathered together experts in the field and interested researchers from universities and industry to present a full picture of the area. This proceedings volume presents papers covering the following subjects: Model reference and predictive control; Multiple model control; Adaptive control I/II; Adaptive control and learning; Learning; Adaptive control of nonlinear systems I/II; Supervisory control; Neural networks for control; PID design methods; Sliding mode; Adaptive filtering and estimation; Identification methods I/II.
For experts and researchers in the field of signal processing.
Paperback, 502 Pages
Published: September 2002
Selected Papers. Plenary Paper.
Nonlinear identification and adaptive control of combustion engines (R. Isermann, N. Müller)
Model Reference and Predictive Control.
Identification-oriented predictive control (L.C. Kammer, G.A. Dumont). An alternate new gap metric for robustness measure (L. Keviczky, Cs. Bányász). Model reference adaptive control of non-linear multivariable systems using an interactor structure estimation (Y. Mutoh).Robust MRAC of a linear time-varying parabolic system with bounded disturbance (K-J. Yang et al.).
Applications to Mechanical and Bio-Mechanical Systems.
Identification for the moving objects by using the observed image data (X. Chen, G. Zhai)Identification of a biomechanical system using neural networks (T. Schauer et al.).Decentralized adaptive continuous-time control of coupled drives apparatus (V. Bobál et al.).A simplified model for head-neck segment fast movements in the frontal plane (A. Pedrocchi et al.).
Adaptive Control and Learning.Neural Networks for Control.
A Comparison of optimal iterative learning control schemes (M. Rzewuski et al.).Virtual reference feedback tuning: a new framework for data-based design of PID and linear controllers (A. Lecchini et al.).Efficient linear MIMO adaptive inverse control (G.L. Plett). Norm optimal iterative learning control applied to chain conveyor systems (T. Al-Towaim et al.).
RBFN model adaptation based on orthogonal decomposition (D.L. Yu, J.B. Gomm). Approximating networks for the solution of T-stage stochastic optimal control problems (M. Baglietto et al.).Neural network based min-max predictive control. Application to a heat exchanger (D.R. Ramírez et al.).A design of neural-net based generalized minimum variance controller (Y. Ohnishi et al.).
Multiple Model Control.Adaptive Control I.
Safe switching in multi-controller implementation (B.D.O. Anderson et al.). Multiple model adaptive estimation for multiuser detection on CDMA communication (M.H. Jaward et al.).Guaranteed closed loop precision in multiple model based control (J. Diez, F. Previdi). Local linear modeling for control of batch processes (D. Bonné, S.B. Jørgensen).
An extension of self-tuning GMVC based on state-space approach (A. Yanou et al.).Overparametrization in predictive adaptive control: experimental results and an entropy interpretation (J.M. Lemos). Adaptive control of a two input - two output system (M. Kubalcík, V. Bobál). Adaptive control using controllers of restricted structure (M.J. Grimble, P. Martin).
Learning.PID Design Methods.
Applications of statistical-learning methods in systems and control (M. Ariola et al.).A new system identification method based on support vector machines (S. Adachi, T. Ogawa). Non-parametric heavy-tailed density estimation and classification problem (N.M. Markovitch, U.R. Krieger). Torques generation for driving an insect-like robot by evolutionary learning approach (A. Pedrocchi et al.).Adaptation and learning in neural networks multiple models based control of mobile robots (A. D'Amico et al.).
Self-Tuning two-degree-of-freedom pid compensator based on two-degree-of-freedom generalized minimum variance control (T. Sato et al.).Relay based gain and phase margins iterative PID controller design (G.H.M. de Arruda, P.R. Barros). A design of evolutionary tuning PID controllers (T. Yamamoto et al.). A design of multiloop predictive self-tuning PID controllers (M. Katayama et al.).
Adaptive Control of Nonlinear Systems I.Applications To Power Plants and Software Tools.
Adaptive control of the power factor precompensator: a comparative study (G. Kaliora, A. Astolfi). Adaptive compensation of nonlinearity in high power amplifier by support vector machine (T. Eda et al.).An heuristic learning modelling strategy for nonlinear systems (G.L. Santosuosso). Adaptive predictive fault tolerant control of super-heated steam temperature in an industrial boiler (R.V. Dionísio et al.).
Realization in MATLAB-SIGLAB environment of a real time estimator of the damping of the electromechanicalmode of the alternator-network system(C.L. Brasca, A. Danelli). Adaptive tracking control of nonlinear power converters (E.F. Colet, A.S.I. Zinober). TIDEA: A control system fine tuning software tool applied to a large power station (F. Pretolani et al.).MATLAB-toolbox for CAD of simple self-tuning controllers (V. Bobál et al.).
Supervisory Control.Sliding mode.
Feedback-loop monitoring for controller falsification (T. Agnoloni et al.). Design method for robust supervisor controller (T. Nakajima et al.). Fault detection and isolation applied to the supervision of adaptive control systems: a neural network based scheme (F.V. Barajas, R.A. Ramírez Mendoza)
Sliding mode control with adaptive decrease of chattering (R. Gessing). Sliding mode control for systems with unknown control direction (G. Bartolini et al.).
Plenary Paper II.Adaptive Filtering and Estimation.
Turbo-learning: A novel application to MIMO wireless communications (S. Haykin, M. Sellathurai).
New time-varying LMS and RLS algorithms with application to direction-of-arrival tracking (K. Yanagihara et al.).The accelerating adaptive filtering algorithm (P.E. Jojoa et al.).Adaptive notch filter with global stability (T. Suzuki, K. Hamada). Robust wiener design of adaptation laws with constant gains (M. Sternad et al.).
Application to Mechanical Systems.Identification Methods I.
Longitudinal Attitude control of a small aircraft using cascade LCN's (T. Caetano et al.).On-line tuning of one-mass motor drive system by simple adaptive control (T. Sekiai et al.). Direct adaptive predictive control of an automatic gear box (G. Ramond et al.). A road-adaptive LQG control for semi-active suspension systems (H-C. Sohn et al.).
Parameter identification for a scalar linear system with fractional brownian motion (T.E. Duncan, B. Pasik-Duncan). An iterative subspace identification with observation outliers based on weighted orthogonal decomposition (H. Tanaka, T. Katayama). Identification of linear parameter varying models using kalman filtering (M. Lovera, F. Previdi). A new adaptive identification method of critical point using frequency estimator (M. Saeki, H. Takeuchi).
Adaptive Control of Nonlinear Systems II.Identification Methods II.
One approach to adaptive control of nonlinear processes (P. Dostál et al.).Design of controller for nonlinear systems by the gain scheduling technique (P. Dobra). Adaptive nonlinear H∞ control for processes with bounded variations of parameters - general forms and general relative degree case (Y. Miyasato). Adaptive iterative refinement of an optimal 2DF nonlinear controller (L. Keviczky, Cs. Bányász)
Remarks on the sample complexity for linear control systems identification (P. Kuusela et al.). The EM algorithm for multivariable dynamic system estimation (B. Ninness, S. Gibson). Neurofuzzy state space modelling and control using kalman filtering state feedback with coloured noise (C.J. Harris, X. Hong). Penalized identification for self-tuning control: an overview (S. Bittanti et al.).Adaptive Control II
On the design of direct adaptive controllers (F.M. Pait). Adaptive control of technological processes based on dual youla-kucera parametrization (F. Gazdoş, P. Dostál). Transient performance improvement in discrete-time model reference adaptive controllers by filteringestimated parameters (N. Mizuno, Y. Fukui). Adaptive pole assignment control under constraints (A. Królikowski, R. Stawski).