A robust Smith predictor modified by internal models for integrating process with dead time

The internal model principle and control together (IMPACT) structure of the Smith predictor is proposed. The structure constructively achieves both the robust stability and absorption of external disturbances. In the structure design, the absorption principle is applied to enable the rejection of arbitrary class of deterministic disturbances and/or to suppress the effects of low frequency stochastic external signals on the system output. It is shown that the tuning of IMPACT structure is extremely simple due to relatively small number of tuning parameters all having clear physical meanings. The presented results of the simulation runs demonstrate the design procedure and illustrate the efficiency of the structure in disturbance absorption.

[1]  F. Glover,et al.  In Modern Heuristic Techniques for Combinatorial Problems , 1993 .

[2]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[3]  Panos J. Antsaklis,et al.  Internal models in regulation, stabilization, and tracking , 1991 .

[4]  Ashok Kumar,et al.  Computational analysis of a flexible assembly system design problem , 2000, Eur. J. Oper. Res..

[5]  Sheldon Howard Jacobson,et al.  The Theory and Practice of Simulated Annealing , 2003, Handbook of Metaheuristics.

[6]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[7]  Sheldon Howard Jacobson,et al.  DISCRETE MANUFACTURING PROCESS DESIGN OPTIMIZATION USING COMPUTER SIMULATION AND GENERALIZED HILL CLIMBING ALGORITHMS , 1998 .

[8]  Karl Johan Åström,et al.  Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[9]  A. Federgruen,et al.  Simulated annealing methods with general acceptance probabilities , 1987, Journal of Applied Probability.

[10]  W. Wonham,et al.  The internal model principle for linear multivariable regulators , 1975 .

[11]  Ehl Emile Aarts,et al.  Simulated annealing and Boltzmann machines , 2003 .

[12]  U. Holmberg,et al.  Robust stochastic control using the internal mode principle and internal model control , 1995 .

[13]  Yves Crama,et al.  Local Search in Combinatorial Optimization , 2018, Artificial Neural Networks.

[14]  Takamasa Hori,et al.  Robust force control based on compensation for parameter variations of dynamic environment , 1993, IEEE Trans. Ind. Electron..

[15]  P. Kumar,et al.  Simulated-annealing type Markov chains and their order balance equations , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[16]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

[17]  Sheldon H. Jacobson,et al.  On the convergence of generalized hill climbing algorithms , 2002, Discret. Appl. Math..

[18]  Gerhard W. Dueck,et al.  Threshold accepting: a general purpose optimization algorithm appearing superior to simulated anneal , 1990 .

[19]  J. E. Normey-Rico,et al.  Smith predictor and modifications: A comparative study , 1999, 1999 European Control Conference (ECC).

[20]  Panos J. Antsaklis,et al.  Internal models in regulation, stabilization, and tracking , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[21]  Vassilis Zissimopoulos,et al.  On the Classification of NP-complete Problems in Terms of Their Correlation Coefficient , 2000, Discret. Appl. Math..

[22]  Bruce E. Hajek,et al.  Cooling Schedules for Optimal Annealing , 1988, Math. Oper. Res..

[23]  Ya. Z. Tsypkin.  Robust Internal Model Control , 1993 .

[24]  R. Stengel,et al.  CONTROL systems. , 1952, Hospitals.

[25]  O. J. M. Smith,et al.  A controller to overcome dead time , 1959 .

[26]  Masami Ito,et al.  A process-model control for linear systems with delay , 1981 .

[27]  C. C. Hang,et al.  A new Smith predictor for controlling a process with an integrator and long dead-time , 1994, IEEE Trans. Autom. Control..

[28]  Katsuhiko Ogata,et al.  Discrete-time control systems , 1987 .

[29]  W. Wonham,et al.  The role of transmission zeros in linear multivariable regulators , 1975 .

[30]  Eduardo F. Camacho,et al.  Robust tuning of dead-time compensators for processes with an integrator and long dead-time , 1999, IEEE Trans. Autom. Control..

[31]  Bruce A. Francis,et al.  The internal model principle of control theory , 1976, Autom..

[32]  Walter Murray Wonham,et al.  Towards an abstract internal model principle , 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

[33]  Sheldon H. Jacobson,et al.  A convergence analysis of generalized hill climbing algorithms , 2001, IEEE Trans. Autom. Control..